DEVELOPMENT OF SYSTEM FOR ONLINE/OFFLINE QUALITY … - 119997125001 - Krishma Desai.pdf · DEVELOPMENT OF SYSTEM FOR ONLINE/OFFLINE QUALITY CONTROL OF NONWOVEN FABRICS/FUNCTIONAL

DEVELOPMENT OF SYSTEM FOR

ONLINE/OFFLINE QUALITY CONTROL OF

NONWOVEN FABRICS/FUNCTIONAL FABRICS

USING DIGITAL IMAGE PROCESSING

TECHNIQUES

A Thesis submitted to Gujarat Technological University

for the Award of

Doctor of Philosophy

in

Textile Engineering by

Krishma Suresh Desai

Enrollment No.: 119997125001

GUJARAT TECHNOLOGICAL UNIVERSITY

AHMEDABAD

July – 2016

DEVELOPMENT OF SYSTEM FOR

ONLINE/OFFLINE QUALITY CONTROL OF

NONWOVEN FABRICS/FUNCTIONAL FABRICS

USING DIGITAL IMAGE PROCESSING

TECHNIQUES

A Thesis submitted to Gujarat Technological University

for the Award of

Doctor of Philosophy

in

Textile Engineering by

Krishma Suresh Desai

Enrollment No.: 119997125001

under supervision of

Prof. (Dr.) P. A. Khatwani


AHMEDABAD

July – 2016

iii

© Krishma Suresh Desai

iv

DECLARATION

I declare that the thesis entitled “Development of System for Online/Offline Quality

Control of Nonwoven Fabrics/Functional Fabrics Using Digital Image Processing

Techniques” submitted by me for the degree of Doctor of Philosophy is the record of

research work carried out by me during the period from August 2011 to July 2016 under

the supervision of Prof. (Dr.) P. A. Khatwani, Professor & Head, Dept. of Textile

Technology, SCET and this has not formed the basis for the award of any degree,

diploma, associateship, fellowship, titles in this or any other University or other institution

of higher learning.

I further declare that the material obtained from other sources has been duly acknowledged

in the thesis. I shall be solely responsible for any plagiarism or other irregularities, if

noticed in the thesis.

Signature of the Research Scholar : …………………………… Date: 29/07/2016

Name of Research Scholar: Krishma Suresh Desai

Place : Surat.

v

CERTIFICATE

I certify that the work incorporated in the thesis “Development of System for

Online/Offline Quality Control of Nonwoven Fabrics/Functional Fabrics Using

Digital Image Processing Techniques” submitted by Smt. Krishma Suresh Desai was

carried out by the candidate under my supervision/guidance. To the best of my knowledge:

(i) the candidate has not submitted the same research work to any other institution for any

degree/diploma, Associateship, Fellowship or other similar titles (ii) the thesis submitted is

a record of original research work done by the Research Scholar during the period of study

under my supervision, and (iii) the thesis represents independent research work on the part

of the Research Scholar.

Signature of Supervisor: ……………………………… Date: 29/07/2016

Name of Supervisor: Prof. (Dr.) P. A. Khatwani

Place: Surat

vi

Originality Report Certificate

It is certified that PhD Thesis titled “Development of System for Online/Offline Quality


Techniques” by Smt. Krishma Suresh Desai has been examined by us. We undertake the

following:

a. Thesis has significant new work / knowledge as compared already published or

are under consideration to be published elsewhere. No sentence, equation, diagram,

table, paragraph or section has been copied verbatim from previous work unless it

is placed under quotation marks and duly referenced.

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plagiarism). No ideas, processes, results or words of others have been presented as

Author own work.

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d. There is no falsification by manipulating research materials, equipment or

processes, or changing or omitting data or results such that the research is not

accurately represented in the research record.

e. The thesis has been checked using Plagiarism Detector (copy of originality

report attached) and found within limits as per GTU Plagiarism Policy and

instructions issued from time to time (i.e. permitted similarity index <=25%).

Signature of the Research Scholar : …………………………… Date: 29/07/2016


Place : Surat

Signature of Supervisor: ……………………………… Date: 29/07/2016


Place: Surat

vii

PhD THESIS Non-Exclusive License to


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facilitation of research at GTU and elsewhere, I, Krishma Suresh Desai having

119997125001 hereby grant a non-exclusive, royalty free and perpetual license to GTU

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viii

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Thesis Approval Form

The viva-voce of the PhD Thesis submitted by Smt. Krishma Suresh Desai (Enrolment

No. 119997125001) entitled “Development of System for Online/Offline Quality


Techniques” was conducted on …………………….………… (day and date) at Gujarat

Technological University.

(Please tick any one of the following option)

The performance of the candidate was satisfactory. We recommend that he/she be

awarded the PhD degree.

Any further modifications in research work recommended by the panel after 3

months from the date of first viva-voce upon request of the Supervisor or request of

Independent Research Scholar after which viva-voce can be re-conducted by the

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should not be awarded the PhD degree.

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x

ABSTRACT

As a result of globalization & also increasing competition, it has become very important

for any industry to develop solutions regarding the quality of products. Effective

monitoring and control, better data predictions, quick response to query is necessary for

effective Quality Control. The research work involves a development of system for

online/offline quality control of nonwoven fabrics/functional fabrics using Digital Image

Processing Techniques. The principal object is to determine the quality of fabric and

frequency of different types of defects occurred in the fabric during the process of

manufacturing.

Human Visual Inspection of fabrics has been a criteria for Visual Assessment of fabric

quality in the Textile Sector since long. It included the detection of fabric defects

generally. However, this method cannot detect more than 60% of the overall defects for the

fabric if it is moving at a faster rate and thus the process becomes insufficient and costly.

Therefore, automatic fabric defect inspection is required to reduce the cost and time waste

caused by defects.

The present works aims at recording the number of defects in the fabric per unit length

with the help of camera well supported with the software solution using MATLAB and

displaying the defects and hence the quality of fabric on computer screen. At the same

time, the present invention also displays the frequency of different types of defects

occurred in fabric during the manufacturing process.

A quality monitoring device has been developed as a part of the research work and the

quality of the fabric can be assessed by mounting the beam containing the fabric on the

developed device. The Fabric Quality Monitoring Device comprises of following major

components:

1. Fabric delivery roller

2. Fabric take-up roller

3. Chain and sprocket driving arrangement

4. Electric motor

xi

5. Video camera

6. Computer loaded with specially developed software solution

7. Input Power

The fabric is moved forward by the driving mechanism and finally it is wound on to the

take-up roller. The quality of fabric can be checked by directing the light towards the face

surface of fabric or back surface of fabric. During the forward movement of fabric, it is

scanned by the camera. A series of images are taken by the camera which are then

transferred to the computer loaded with the suitable software solution specially developed

for analysing the fabric for various defects. The captured images are processed using

MATLAB. Various parameters like mean, sd, histogram of the intensity values are studied

for estimating & identifying the standard images. The images of the samples with defect

are then processed for obtaining the defect statistics.

The proposed algorithm will check for variability and give defect statistics and classify as

per Defect Area. It will also check for no. of Defects in the Fabric Lot and give % Defects

in the Fabric. On the basis of the defect statistics a fabric grading system has been

developed which will classify the fabric for specific application. Thus, the developed

system will guide the users by way of providing the information related to the defects and

their frequency of occurrence during the manufacturing process.

xii

Acknowledgement

I express my deep sense of gratitude to my honourable guide Prof. (Dr.) P. A. Khatwani,

Professor & HOD, Dept. of Textile Technology, Sarvajanik College of Engineering &

Technology, Surat, for his immense interest, co-operation, enthusiastic and motivating

attitude and systematic guidance and involvement throughout the work. It is for his tireless

supervision that this work has developed into its present form.

I am very much thankful and grateful to my Co-supervisor, Dr. Hamed SariSarraf,

Professor, Electrical and Computer Engineering, Texas Tech University, TX, Doctoral

Progress Committee members Dr. P. C. Patel, Professor, Faculty of Engg. & Tech., M. S.

University and Mr. R. S. Backaniwala, Director, M/s. Himson Ltd. for mentoring me and

providing me valuable guidance as and when required.

I extend my sincere thanks to Mr. R. S. Backaniwala, Director, M/s. Himson Ltd., Mr.

Pinal Dakoria, M/s. Technofab, Udhana Magdalla Road, Surat, Mr. Nimish Gajjar, M/s.

N.M. Gajjar Hotels Pvt. Ltd, Village Kim, Surat Mr. Rakesh Bhai, M/s. Wovlene Tecfab

India, Hazira, Surat, Mr. Arun Nag, M/s. Ginni Filaments, GIDC, Panoli for extending

their full co-operation for sampling of the fabrics for the research work.

My gratitude goes out to the assistance and support of Dr. Akshai Aggarwal, Ex. Vice

Chancellor, Dr. Rajul Gajjar, I/c. Vice Chancellor & Dean, PhD Programme, Mr. J. C.

Lilani, I/C Registrar, Ms. Mona Chaurasiya, Research Coordinator, Mr. Dhaval Gohil,

Data Entry Operator and other staff members of PhD Section, GTU.

I express my deep sense of gratitude to all the members of Governing Body of Sarvajanik

College of Engg. & Tech. for their encouragement & full support in carrying out my

experimental & testing work at the laboratories of Sarvajanik College of Engg. & Tech.,

Surat.

xiii

Most importantly, none of this would have been possible without the love and patience of

my dear family members, my husband Dr. Pathik Naik for providing me constant support

and strength, my son Tishya for his love & understanding, my mother Ms. Ranjanben

Desai & Father Mr. Sureshbhai Desai, who always stood by me in my venture and to them

I solemnly dedicate this thesis.

Special thanks to all those who have directly or indirectly contributed for the progress and

completion of my work.

xiv

Table of Contents

CHAPTER 1 INTRODUCTION

1.1 Background 1

1.2 Objectives 2

1.3 Methodology 3

1.4 Scope of Thesis 4

1-4

CHAPTER 2 LITERATURE REVIEW

2.1 Introduction 5

2.2.1 Functional Fabrics 5

2.2.2 Nonwovens 6

2.2.3 Need for Quality Control 7

2.2 Manufacturing of Functional Fabrics 7

2.2.1 Introduction 7

2.2.2 Woven Fabrics 7

2.2.3 Knitted Fabrics 8

2.2.4 Nonwovens 8

2.2.5 Special Fabrics 9

2.3 Quality Control of Functional Fabrics 10


2.3.2 Quality Parameters of different Functional

Fabrics

10

5-50

xv

2.3.3 Methods of assessment of Functional Fabrics 10

2.4 Common Defects in Functional Fabrics 11


2.4.2 Defects in Woven Fabrics 12

2.4.3 Defects in Knitted Fabrics 15

2.4.4 Defects in Nonwovens 18

2.5 Fabric Inspection 19

2.5.1 Visual Manual / Traditional Inspection

Methods

19

2.5.2 Automatic Fabric Inspection Systems 21

2.6 Use of Image Processing in Assessing

Structural Variation in Different Areas of

Textiles

22

2.6.1 Image Processing & Structural Variability in

Fibres

22


Yarn

23


Fabrics

23

2.7 Image Processing 24


2.7.2 Human and Computer Vision System 25

2.7.3 Fundamental Steps in Digital Image 26

xvi

Processing

2.7.4 Methods of Assessment of Image Analysis or

Image Quality

27

2.8 Image Processing & Fabric Inspection 29


2.8.2 Implementation, Challenges and Difficulties 30

2.8.3 Image Acquisition 30

2.8.4 Components of Fabric Inspection System 31

2.9 Algorithms for Automatic Defect Detection 35


2.9.2 Structural Approaches 35

2.9.3 Statistical Approaches 36

2.9.4 Spectral Approaches 42

2.9.5 Model-based Approaches 45

2.10 Fabric Grading 46


2.10.2 Various Approaches 47

2.11 Summary of Literature Review 49

CHAPTER 3 PROPOSED SYSTEM & RESEARCH APPROACH

3.1 Problem Description 51

3.2 Proposed System 52

3.3 Research Approach & Hypothesis 52

51-53

xvii

CHAPTER 4 SYSTEM DESIGN & DEVELOPMENT

4.1 Introduction 54

4.2 Device Development 55

4.2.1 Development of manually operated device 56

4.2.2 Further modification of device for image

acquisition of fabric in roll form

58

4.2.3 Automation of the Device 59

4.2.4 Working of System using the Device 64

4.3 Fabric Manufacturing and Defect Analysis 67


4.3.2 Fabric Sampling 68

4.3.3 Fabric Defects 72

4.4 Image Acquisition for the Learning Phase 75


4.4.2 Geotextiles 75

4.4.3 Spunbond Fabrics 79

4.5 Image Processing Methodology 84


4.5.2 Steps involved in Processing of Images 84

4.5.3 General Image Parameters of the Images 85

54-96

xviii

4.5.4 Methodology for Defect Detection 90

4.5.5 Software Used 94

4.5.6 Stages of Implementation 94

4.6 Fabric Grading 95

CHAPTER 5 RESULTS AND DISCUSSIONS

5.1 Introduction 97

5.2 General Image Parameters of the Images 98

5.2.1 Woven Geotextiles 98

5.2.2 Spunbond Nonwovens 103

5.3 Histogram Analysis 108



5.4 Thresholding 117



5.5 Defect Detection & Validation 117

5.5.1 Defect Detection in Images of Woven

Geotextiles

117

5.5.2 Validation of Results of Geotextiles 129

5.5.3 Defect Detection in Spunbond Images 138

5.5.4 Validation of Results of Spunbond 149

97-158

xix

CHAPTER 6 CONCLUSIONS & FUTURE WORK

6.1 Objectives Achieved 159

6.2 Conclusions 160

6.3 Possible Future Scope 160

159-160

References 161-172

Bibliography 173-175

List of Publications 176-177

Appendices

xx

List of Figures

No. Descriptions Page No.

2.1 Defects in woven fabrics 14

2.2 Defects in knitted fabrics 17

2.3 Defects in Nonwovens 18

2.4 Manual Fabric Inspection 19

2.5 Power Driven Inspection machines 20

2.6 Automatic Fabric Inspection System 31

2.7 Commercial Automatic Fabric Inspection System – Elbit Vision

System

32

2.8 Various Illumination Configurations 33

2.9 Influence of Illumination in Textile Materials 34

2.10 Grading of Nonwoven Fabrics 48

4.1 Process flow chart of the developed system 55

4.2 Manually operated device 56

4.3 Design of Box Base 57

4.4 Photograph of the developed scanbox 57

4.5 Photograph of the Modified Device. 58

4.6 Drive and main parts of the Device 60

4.7 Different View Angles of Developed Device 61

4.8 Top View & Illumination Arrangement 62

4.9 Side View of Machine with Switch Board 62

4.10 Passage of Fabric 63

4.11 Final System 63

4.12 Selection of Fabric Type 65

4.13 Capture & Process Option 66

4.14 Final Output 66

4.15 Defects mentioned in Table 4.7 73

4.16 Defects mentioned in Table 4.8 74

4.17 Image of Defect Free Sample 75

4.18 Missing End/Chira 76

xxi

4.19 Slub (Warp) 77

4.20 Stain (Daggi) 77

4.21 Slub (Weft) 78

4.22 Missing Pick/Jerky 78

4.23 Gout 79

4.24 Image of Defect Free Sample 80

4.25 Drop/Bond point Fusion 81

4.26 Pin Hole 81

4.27 Wrinkle 82

4.28 Hard Filament 82

4.29 Holes 83

4.30 Calender Cut 83

4.31 Steps involved in processing of Images 85

4.32 RGB Image & Grayscale converted Image of Spunbond Nonwoven 86

4.33 Histogram of a Defect Free & Defective Region of Spunbond Fabric 87

4.34 Grayscale Image with histogram & Contrast Adjusted Image with

histogram of Spunbond Fabric

88

4.35 Grayscale Image & Filtered Image of Spunbond nonwoven fabric 89

4.36 Grayscale Image & Filtered Image of Woven Geotextile fabric 90

4.37 Grayscale Image & Binary Image of spunbond nonwoven fabric 92

4.38 Binary Image & Dilated Binary Image of Geotextile woven fabric 92

5.1 Comparison of the Mean Intensity Values between the unprocessed

Images of the various Geotextiles

99

5.2 Comparison of the Mean Intensity Values between the enhanced


99


Images of Defects & general Fabric in Geotextiles

101



101


Images of the various Defects in Geotextiles

102



102

xxii


Images of the various Spunbond Fabrics

104



104


Images of Defects & general Fabric in Spunbond Fabrics

106


Images of Defects & general Fabric in Spunbond Fabrics

107


Images of the various Defects in Geotextiles Spunbond Fabrics

107


Images of the various Defects in Spunbond Fabrics

108

5.13 Histogram of Images of some samples of defect free images of

Geotextiles

109

5.14 Histogram of defect free region and Missing End (Chira) 109

5.15 Histogram of defect free region and Slubs (Warp) 110

5.16 Histogram of defect free region and Stain (Daggi) 110

5.17 Histogram of defect free region and Slubs (Weft) 111

5.18 Histogram of defect free region and Missing Pick (Jerky) 111

5.19 Histogram of defect free region and Gout 112

5.20 Histogram of Images of some samples of defect free images of

Spunbond Nonwoven

113

5.21 Histogram of Drop / Bond Point Fusion 114

5.22 Histogram of defect free region and Pinhole 114

5.23 Histogram of defect free region and Wrinkle 115

5.24 Histogram of defect free region and Hard Filament 115

5.25 Histogram of defect free region and Hole 116

5.26 Histogram of defect free region and Calender cut 116

5.27 Grayscale Image - Missing End 118

5.28 Binary Image - Missing End 118

5.29 Highlighted Missing End 119

5.30 Grayscale Image – Slub (Warp) 119

5.31 Binary Image - Slub (Warp) 120

xxiii

5.32 Highlighted Slub (Warp) 120

5.33 Grayscale Image – Stain (Daggi) 129

5.34 Binary Image - Stain (Daggi) 121

5.35 Highlighted Stain (Daggi) 122

5.36 Grayscale Image – Slub (Weft) 122

5.37 Binary Image - Slub (Weft) 123

5.38 Highlighted Slub (Weft) 123

5.39 Grayscale Image - Missing Pick (jerky) 124

5.40 Binary Image - Missing Pick (jerky) 124

5.41 Highlighted Missing Pick (jerky) 125

5.42 Grayscale Image - Gout 125

5.43 Binary Image - Gout 126

5.44 Highlighted Gout 126

5.45 Comparison of Defect Area obtained from the System with those

obtained from Manual - visual Examination

128

5.46 Comparison of Length/Width of Biggest Defect obtained from the

System with those obtained from Manual - visual Examination

128

5.47 Comparison of Multiple Images of Regions with same defect 131

5.48 Test Image 1 132










5.58 Grayscale Image – Drop/Bond Point Fusion 138

5.59 Binary Image - Drop/Bond Point Fusion 139

5.60 Highlighted Drop/Bond Point Fusion 139

5.61 Grayscale Image – Pin Hole 140

xxiv

5.62 Binary Image - Pin Hole 140

5.63 Highlighted Pin Hole 141

5.64 Grayscale Image – Wrinkle 141

5.65 Binary Image - Wrinkle 142

5.66 Highlighted Wrinkle 142

5.67 Grayscale Image – Hard Filament 143

5.68 Binary Image - Hard Filament 143

5.69 Highlighted Hard Filament 144

5.70 Grayscale Image - Hole 144

5.71 Binary Image - Hole 145

5.72 Highlighted Hole 145

5.73 Grayscale Image – Calender Cut 146

5.74 Binary Image - Calender Cut 146

5.75 Highlighted Calender Cut 147

5.76 Comparison of Defect statistics obtained from the System with

those obtained from Manual - visual Examination

148

5.77 Comparison of Total Area between Multiple Images of Regions

with same defect

151

5.78 Comparison of Objectionable Area between Multiple Images of

Regions with same defect

151











xxv

List of Tables

No. Descriptions Page No.

2.1 Description of Defects in Woven Fabrics 12

2.2 Description of Defects in Knitted Fabrics. 15

2.3 Common Defects in Non Woven Fabrics 18

2.4 Comparison of CCD and CMOS camera 32

2.5 Comparison of Line Scan and Area Scan Camera 33

2.6 Penalty Points in 4-Point System 47

2.7 Penalty Points in 10-Point System 48

4.1 Specifications of Geotextiles 68

4.2 Specifications of Industrial Fabrics 69

4.3 Specifications of Coated Fabrics for Composites 69

4.4 Specifications of Spunbond Nonwovens 70

4.5 Specifications of Needle Punched Fabrics 71

4.6 Specifications of Spun Laced Fabrics 71

4.7 Identified Defects in Geotextiles 72

4.8 Identified Defects in Spunbond Fabrics 74

4.9 Details of the images of the defects of Geotextiles 76

4.10 Details of the images of the defects of Spunbond Fabrics 80

4.11 Defect Classification 95

5.1 Sample wise average values of Image parameters for unprocessed

images of each type of Geotextile.

98

5.2 Sample wise average values of Image parameters for enhanced


99

5.3 Defect wise values of Image parameters for unprocessed images of

identified defects in Geotextiles.

100

5.4 Defect wise values of Image parameters for enhanced images of


100

5.5 Sample wise average values of Image parameters for unprocessed

images of each type of Spunbond Nonwoven

103

5.6 Sample wise average values of Image parameters for enhanced 104

xxvi

images of each type of Spunbond Nonwoven

5.7 Defect wise values of Image parameters for unprocessed images of

identified defects in Spunbond Nonwoven

105

5.8 Defect wise values of Image parameters for enhanced images of

identified defects in Spunbond Nonwoven

106

5.9 Defect Statistics obtained from the System 127

5.10 Defect Statistics obtained from Manual - visual Examination 127

5.11 Manual Grading of the Defects in Geotextile Fabrics 129

5.12 Grading of Defects in Geotextile Fabrics using the System 130

5.13 Comparison between the Grading of Woven Geotextile Images

obtained by Manual - visual Examination & System

130

5.14 Grading of Multiple Images of Regions with same Defect 131

5.15 Comparison between the Grading achieved with developed System

as against Manual - visual grading

137

5.16 Defect Statistics obtained from the System 147

5.17 Defect Statistics obtained from Manual - visual Examination 148

5.18 Manual Grading of the Defects in Spunbond Fabrics 149

5.19 Grading of Defects in Spunbond Fabrics using the System 149

5.20 Comparison between the Grading of Spunbond Images obtained by

Manual - visual Examination & System

150

5.21 Grading of Multiple Images of Regions with same Defect 150

5.22 Comparison between the Grading achieved with developed System

as against Manual - visual grading

157

xxvii

List of Appendices

Appendix A Details of Patent Filed

Appendix B Software Code

Appendix C Originality Report

CHAPTER – 1

Introduction

1.1 Background:

Today, quality is to be considered as the most important parameter in any industry and the

textile industry is no exception. Each industry today aims to produce the highest quality

goods in the shortest amount of time leading to requirement of an enhanced quality control

system. Quality may be defined by the sum of all those attributes which can lead to the

production of products acceptable to the consumer when they are combined and one such

important attribute is the defects or the faults in the fabric. Thus keeping fabric defects to a

minimum is of prime importance from a quality perspective.

A process quality control system includes testing & inspecting of fabric, analysing the

observations so made and then making the decisions to improve the performance of the

system. As no manufacturing process is 100% defect-free, especially when considering the

fabric manufacturing process, the success of the process is significantly highlighted by the

success in detecting the objectionable fabric defects to maximum.

The frequency and nature of the defects in fabrics determines the quality of the product in

terms of grading as well as the price of the fabric. First, second, third quality fabrics are

often the terms used in the textile industry to determine the grading of fabrics as per the

frequency and nature of defects. The profit margin also varies with the grading, decreasing

from first quality to the last quality. This basis of quality grading is often subjective.

Introduction

2

With the improvement in the production process and also improvement in materials and

technology, the quality levels have increased to a great extent. This has also lead to

customer expectations of getting a minimum defect fabric.

The defects at present are frequently examined by human inspectors. The major limitation

here is the human perception may vary from individual to individual, high labour cost as

well as the time being involved. Chances of missing out the defects by operators is

common, mostly due to tiredness, boredom, inattentiveness, fatigue and lack of time and

the inspection so done may not be reliable. Thus the method of inspection plays a

significant role in detection of objectionable faults and hence proper grading of fabrics. In

order to have maximum benefit from the inspection process, the process should have high

degree of accuracy.

Therefore an automatic inspection system may be highly desirable as it gives possibly the

best objective and consistent evaluation.

An automated inspection system consists of a computer based vision system which may be

offline or online. The major components include the fabric monitoring system and the

defect analysing and classifying software. A high resolution camera is used for monitoring

of the fabric offline or online in most of the commercial systems available in the market

along with the software module identifying the defects. The software module uses various

image processing tools for enhancement of the images captured and then extracting the

variability or the defects.

1.2 Objectives:

The objectives of the research work are as below:

To develop cost and quality effective system for targeting mainly the growing

Indian Technical Textile Market.

To help the user in selection of proper quality of nonwoven / functional fabrics for

specific end use applications.

To help the user to avoid unnecessary wastage of time and materials, which

otherwise would be due to wrong selection of materials for any specific application

Objectives

3

Very bright prospects ahead for the system to be developed considering very high

market growth from 10 billion dollars in 2009 to expected 31 billion dollars in

2020.

1.3 Methodology:

Qualitative as well as formulative approach has been used for this research work. The

structure / qualities and properties of nonwoven/functional fabrics are influenced largely

by factors like type & structure of raw material, type of fabric-woven, knitted, nonwoven,

special fabrics, etc. which also influences the surface texture of the fabric.

The system developed is capable of identifying defects in various functional fabrics and

classify according to their nature, frequency and size. Different varieties of functional

fabrics-woven and nonwoven were manufactured. As mentioned earlier, no manufacturing

process is 100% defect free, thus causing defects in the fabrics.

A device has been developed as a part of the quality monitoring system. The important

parts of the device are fabric delivery roll, illumination for capturing images of surface of

the fabric, CMOS camera for capturing images of surface of fabric and a fabric take up

roller. A series of images are taken by the camera which are then transferred to the

computer loaded with the suitable software solution specially developed for analysing the

fabric for various defects. The captured images are processed using MATLAB.

The processing of images basically involves image enhancement and feature extraction to

identify type of defect. The principal objective of image enhancement is to modify

attributes of an image to make it more suitable for analysing it and estimating and

identifying the defect free images as well as the images with defects. The features of the

defects are then extracted and depending upon the statistics of the defect, they are graded,

thus guiding the users by way of providing the information related to the defects and their

frequency of occurrence during the manufacturing process.

Introduction

4

1.4 Scope of Thesis:

6 different varieties of functional fabric including woven as well as nonwoven fabrics were

manufactured for the study. The defects obtained in the manufactured fabric were a result

of the fabric manufacturing process and were assessed visually as well as with the software

developed using the proposed algorithm using MATLAB. However, the experts from IIT

had suggested to consider only one variety of fabric preferably spunbond nonwoven fabric

during the Research Week held during month of April 2015 at Gujarat Technological

University, Ahmedabad. They had also suggested to consider some of the major defects

occurred during the manufacturing of spunbond fabrics and also to validate the results so

obtained by taking multiple images of same defects. After considering the inputs from the

experts of IIT, the study has been narrowed down to 2 varieties of functional fabrics i.e.

Woven Geotextiles & Spunbond Nonwovens. 6 types of defects in each variety have been

focused on in the study.

The captured images were processed using MATLAB. Various parameters like mean, sd,

histogram of the intensity values were studied for estimating & identifying the standard

images. The images of the samples with defect were then processed for obtaining the

defect statistics. The proposed algorithm will check for variability and give defect statistics

and classify as per Defect Area. It will also check for no. of Defects in the Fabric Lot and

give % Defects in the Fabric. On the basis of the defect statistics a fabric grading system

has been developed which will classify the fabric for specific application.

CHAPTER – 2

Literature Review

2.1 Introduction:

2.1.1 Functional Fabrics:

Functional Fabrics are those fabrics which are manufactured primarily for their technical

and performance properties rather than their aesthetic or decorative characteristics (such as

filters, machine clothing, conveyor belts, abrasive substrates etc.). The major areas where

the functional fabrics are used are agriculture & horticulture, geotextiles, building &

construction, Industrial filtration, conveying, cleaning, hygiene and medical, automobiles,

shipping, railways and aerospace, packaging, personal and property protection, footwear

sport and leisure.

Since the functional fabrics are manufactured primarily for their technical applications, the

fabrics may be produced by weaving, felting, lace making, net making, nonwoven

processes and tufting or a combination of these processes.

Literature Review

6

2.1.2 Nonwovens:

Nonwovens owing to their different and specific structure from the woven and knitted

fabrics are majorly used as functional fabrics. There are various definitions describing the

nonwovens, but the most commonly used are those by the Association of the Nonwovens

Fabrics Industry (INDA) and the European Disposables and Nonwovens Association

(EDANA) which has been described below:

The nonwovens definition adopted by EDANA, the European nonwovens industry

association is: they are manufactured sheet, web or bat of directionally or randomly

oriented fibres, bonded by friction, and /or cohesion and/or adhesion, excluding paper or

products which are woven, knitted, tufted stitch bonded incorporating binding yarns or

filaments, or felted by wet milling, whether or not additionally needled. The fibres may be

natural or man-made origin[1]

. They may be staple or continuous or be formed in situ.

INDA, the North American Association has a slightly different, wider definition which has

the merit of apparent simplicity: a sheet, web or batt of natural and or man-made fibres or

filaments excluding paper, that have not been converted into yarns and that are bonded

together by any of the following means [1]

:

Adding an adhesive

Thermally fusing the fibres or filaments to each other or to the other meltable fibres

or powders.

Fusing fibres by first dissolving, and then resolidifying their surfaces.

Creating physical tangles or tuft among the fibres.

Stitching the fibres or filaments in place.

The manufacturing of nonwovens basically started with the use of textiles on other areas

than for the apparel use. With the development of Industrial textiles, the nonwovens started

gaining a larger market. The oldest nonwoven i.e. felt was made up of wool (felting of

wool because of its scaling structure & was used as rugs. With the invention of man-made

fibres, the application of nonwovens started being used in various industrial applications.

Earlier cotton & natural fibres such as hemp & jute were extensively used in production &

nowadays because of advantageous properties of synthetic fibres, they are used.

Introduction

7

2.1.3 Need for Quality Control:

Quality is basically signifies the needs of customer and failing to maintain the required

quality standard may affect the cost of the product. With the increase in number of

applications of technical textiles in different areas during the days to come, and to avoid

rejection of fabrics, it becomes necessary to design and develop the system to check the

quality of such varieties of fabrics in much shorter time and with utmost accuracy. As

mentioned earlier functional textiles are fabrics used for specific performance based

applications, it becomes very necessary to maintain standards of quality required for these

applications. These might become possible by implementing an efficient quality control

system.

Offline and online fabric inspection for woven fabrics has served as an important tool to

cater to the needs of good quality control systems especially for the fabrics to be used by

the garment industry [2-4]

. Similar systems seem to be a necessity for maintaining the

standards of the functional fabrics as well as the nonwoven fabrics.

2.2 Manufacturing of Functional Fabrics [5]

:

2.2.1 Introduction:

As discussed earlier Textile fabrics are most commonly woven but may also be produced

by other methods of fabric manufacturing like knitting, nonwoven processes, lacing,

tufting etc. A brief overview on all these process has been described in the following

sections.

2.2.2 Woven Fabrics:

Woven fabrics are made on looms. They consist of two sets of yarns that are interlaced and

lie at right angles to each other. The threads that run along the length of the fabric are

known as warp (ends) and the threads that run from one side to the other side of the fabric,

are weft (picks). In triaxial and in three-dimensional fabrics yarns are arranged differently.

Literature Review

8

Functional fabrics are manufactured on any of the weaving machines as per their

application. Their strength, thickness, extensibility, porosity and durability can be varied

and depend on the weave used, the thread spacing, that is the number of threads per

centimetre, and the raw materials, structure (filament or staple), linear density (or count)

and twist factors of the warp and weft yarns. Higher strengths and greater stability can be

obtained from woven fabrics than from any other fabric structure using interlaced yarns.

Structures can also be varied to produce fabrics with widely different properties in the

warp and weft directions.

2.2.3 Knitted Fabrics:

Knitted fabrics are formed by interloping of yarn(s) i.e. forming yarn(s) into loops, each of

which is typically only released after a succeeding loop has been formed and intermeshed

with it so that a secure ground loop structure is achieved and are manufactured on knitting

machines. The loops are also held together by the yarn passing from one to the next. They

can be divided into weft knit fabric and warp knit fabrics.

Warp Knitted: Loops formed across the width of fabric & each weft thread is fed more or

less at right angles.

Weft Knitted: Loops formed vertically down the length of the fabric from one thread. Weft

knitted is more versatile method of fabric formation, more production & simplest method

of converting yarn to fabric.

2.2.4 Nonwovens:

As discussed earlier this fabrics are neither woven nor knitted but are produced when the

fibres of a web/batt are intermingled or bonded by mechanical, chemical or thermal means.

Manufacturing of Functional Fabrics

9

2.2.5 Special Fabrics:

Lacing: It is an exquisitely detailed fabric and commonly used in lingerie, formal wear,

and decorative trim. Previously the lace was handmade. Lace are now also produced on

Raschel knitting machines as well as needle looms for the use of lace in areas of technical

textiles like hometech and indtech.

Netting: It is made by looping and knotting yarns in an open pattern, usually a

characteristic geometric design of rectangular, square or diamond shapes. Originally, net

was made by hand but now most contemporary versions are produced on lace machines.

Depending on the size of the yarn, nets can range from very fine fabrics to heavy, coarse

materials. The open pattern of the netting makes it useful for decorative accents as well as

functional purposes such as fish nets.

Braiding: A braid is a rope like thing, which is made by interweaving three or more

strands, strips, or lengths, in a diagonally overlapping pattern. Braiding is one of the major

fabrication methods for composite reinforcement structures. It is done by intertwining of

yarns in any direction. From a domestic art of making laces, it evolved as a fabric made by

narrow width looms. Of late, Crochet knitting machines have replaced large numbers of

traditional braiding machines.

Braiding can be classified as two and three-dimensional braiding. Two-dimensional braid

structure can be circular or flat braid. They are formed by crossing a number of yarns

diagonally so that each yarn passes alternately over and under one or more of the others.

Two dimensional braids are produced through circular braiding machine and rotary

machine. Three-dimensional braiding is relatively new and was developed mainly for

composite structures. In it, a two dimensional array of interconnected 2-D circular braids is

created on two basic types of machines- the horn gear and cartesian machines.

http://www.teonline.com/knowledge-centre/fabrics.html

Literature Review

10

2.3 Quality Control of Functional Fabrics:

2.3.1 Introduction:

By quality it‘s understood that all the features and characteristic values of a product or a

service fulfil the fixed and expected requirements with regard to their suitability. Since the

functional fabrics and nonwovens have their specific end-use, the test method and the

quality assessment is done specific to its application. Earlier, nonwovens were subjected to

the same quality control testing methods used for pulp and paper industries, but the

practices has now changed and separate testing methods have been developed specific to

the application of the particular fabric[1]

. Some of the methods to analyse the quality of

these kinds of fabrics have described in this section.

2.3.2 Quality Parameters of different functional fabrics:

The important parameters for characterization of technical textiles including nonwovens

include various surface & structural characteristics like mass per surface unit, nonwoven

thickness, elasticity and air permeability, defects, coating quality, etc. All these features

and characteristic values with respect to the expected requirements with regard to their

suitability accounts for assessment of quality. The presence of defects may lead to

reduction in the profit margin by about 45%-65% [3]

. Thus, considering the surface

characteristics as an important and a common parameter in assessment of the quality

performance of any functional fabrics, estimating the same by means of fabric inspection

methods is highly desirable. The main objective of the visual assessment is detection of

objectionable defects or fabric faults in the fabrics. Thus the different types of fabric faults

can be considered as important quality parameters for different functional fabrics.

2.3.3 Methods of Assessment of functional fabrics:

As mentioned earlier the test method and the quality assessment of the functional fabrics

and nonwovens are done specific to its application. Some of the common methods include

visual examination methods and automatic examination methods. Some of the other

automatic examination methods include Image Analysis, Infrared Measurement Principles

and using weight sensors for assessing uniformity.

Quality Control of Functional Fabrics

11

Image analysis: This method is commercially used by many companies to measure the

fibre orientation (fibre orientation analyzer) in web, measurement of uniformity of web,

measuring porosity of fabrics (TRI/Micro Absorbmeter), etc [1]

.

Infrared measurement principles [1]

: The light with wavelength near infra-red (NIR) and

mid of infrared is selectively absorbed by moisture or ―organic‖ bases coatings. The

amount of absorption that occurs is related to the concentration of the absorber and

therefore allows a coat weight or moisture measurement to be obtained. The NIR method

gives a single point measurement after the coater or re-moisturizer is required for coating

and moisture measurement. It is a non-contact measurement and the sensor operates well at

a distance of 200 mm as is required for coatings.

Using weight sensors for uniformity: This method primarily detects the weight per unit

area and the thickness of the fabric [1]

. It involves weight gauges or photoelectric sensors to

determine the weight per unit area. In the online system measuring the same the delivery

and feed are regulated according to the values of the weight.

2.4 Common Defects in Functional Fabrics:

2.4.1 Introduction:

A Fabric Defect is basically any abnormality in the Fabric that hinders its acceptability by

the consumer. Fabric defects may be characterised by an imperfection that impairs worth

or utility and spoils the utility of material [6]

. The defects in the fabrics may be accounted

as a result of use of defective yarn, error in process of manufacturing of the fabric or the

error in the wet processing of fabrics in case of finished fabrics.

As mentioned above, the defects may also be resulted due to defective manufacturing

process, hence the type of manufacturing process may result in set of defects characterised

by it‘s manufacturing process. The defect type, their principal causes and remedies of the

defects found in woven, knitted and nonwoven have been briefed in this section.

Literature Review

12

2.4.2 Defects in Woven Fabrics:

There are many fabric defects which occur during the process of weaving. Some of these

defects are described in Table 2.1 [6, 7, 8]

:

Table 2.1 : Description Of Defects In Woven Fabrics

Sr.

No.

Fabric

Defect

Definition Principal Causes Remedy

1. Missing

End

(Chira)

There may be one

end or a group of

ends missing in the

fabric.

If the broken ends are not mended

immediately by the operator, these

missing ends will occur in the

fabric.

This defect can be minimised

by minimising missing ends

in the weaver‘s beam and by

providing an efficient warp -

stop motion on a loom.

2. Slubs

(Warp)

Thick untwisted

portion in warp

yarn

Variation in draft during spinning. Set the draft as per the

requirement.

3. Stain

(Daggi)

These stains are

due to lubricants or

dust.

Improper material handling, bad

oiling & cleaning practices

By proper material handling

as well as good oiling &

cleaning practices, this defect

can be avoided.

4. Slubs

(Weft)

Thick untwisted

portion in weft

yarn

Variation in draft during spinning. Set the draft as per the

requirement.

5. Missing

Pick

(Jerky)

It is a strip which

extends across the

width of fabric &

has the pick

density lower than

the required one.

It is caused by faulty let - off &

take - up motions; Also, if the

loom is not stopped immediately in

case of weft break, few picks are

liable to be missed in the fabric.

This defect can be remedied

by proper setting of let - off &

take - up motions & also by

using an efficient brake -

motion.

6. Gout Foreign matter

woven in a fabric

by accident.

Usually lint or

waste.

It is caused when the hardened

fluff or foreign matter such as

pieces of leather accessories,

pieces of damaged pickers etc., is

woven into the texture of the

fabric.


by preventing the foreign

matter from falling onto the

warp between the reed & the

fell of the cloth.

7. Weft Bar It is a bar or band

which extends

across the width of

fabric.

If the weft yarn is not

regular/uniform; If there is more

variation in count of weft yarn; If

there is shade variation in case of

dyed weft yarn; If the difference in

blend composition is more in case

of blended Weft yarn.


by better process control to

get the uniform & regular

weft yarn. The mixing - up of

different counts, shades etc.,

of weft yarns can be avoided

by better house - keeping.

Common Defects in Functional Fabrics

13

TABLE 2.1 : Description of Defects in Woven Fabrics (cont.).

Sr.

No.

Fabric Defect Definition Principal Causes Remedy

8. Float (Jala) When there is no

proper interlacement

of warp & weft over a

certain area of fabric,

a float is formed.

When there is an entanglement

of adjoining ends in the region

between the heald shafts & the

fell of the cloth.

The entanglement of

ends & hence the float

can be avoided by

mending the broken end

immediately and by

providing efficient warp

- stop motion.

9. Patti or

Crammed Picks

It is also a band which

extends across the

width of fabric

Because of improper working

of take - up motion, sometimes

more pick density is obtained

than the required one & this

higher pick density appears as

a band in the fabric

By proper setting of

take - up motion this

defect can be avoided.

10. Starting Marks It is similar to a jerky Because of mechanical faults

in the loom such as loose

fitting of reed, loose or worn -

out crank, crank - arm or crank

- shaft bearings etc, the

starting marks will occur in the

fabric whenever the loom is

started

This defect can be

remedied by proper

maintenance of the

loom.

11. Shuttle Smash When many ends

break due to shuttle

trap, this defect will

occur

There are many causes for the

shuttle trap like wrong timing

of shedding, soft picking,

unbalanced shuttle,

insufficient checking of shuttle

in the boxes etc.

Setting of proper

timing, using balanced

shuttle.

12. Reed Mark Similar to missing

end.

When the wires of reed are

damaged or bent during

running of a loom, the space

between those wires & hence

between the warp ends is

increased. This increased

space between the ends is

clearly seen in the fabric as a

missing end.

This defect can be

avoided either by

replacing or by

straightening the

damaged wires of the

reed.

13. Temple Marks These are fine holes

caused near the

selvedges of a fabric.

Caused by improper use of

temples.

This defect can be

avoided by suitably

selecting the temples

for the fabrics to be

produced.

14. Box Marks These are fine

weftway dark or oily

lines in the fabric.

This defect will occur if the

weft yarn is trapped between

the box front plate & the

shuttle, & becomes oily & if

the balloon of weft yarn,

formed during its unwinding

from the pirn, touches the

picker guide spindle &

becomes oily.

This defect can be

remedied by tying a

cloth piece to box front

plate & keeping the

shuttle box as clean as

possible.

Literature Review

14

Images of the defects in woven fabrics:

Some of the common defects found in woven fabrics have been illustrated in Figure 2.1 [6,7]

.

FIGURE 2.1 : Defects in woven fabrics


15

2.4.3 Defects in Knitted Fabrics:

Knitting process also caters to fabric defects and some of these defects are described in

Table 2.2 [9, 10, 11]

:

TABLE 2.2 : Description of Defects in Knitted Fabrics

Sr. No. Fabric

Defect


1. Barriness Barriness defect

appears in the Knitted

fabric in the form of

horizontal stripes of

uniform or variable

width.

High Yarn Tension

Count Variation

Mixing of the yarn lots

Package hardness variation

Ensure uniform Yarn

Tension on all the feeders.

The average Count variation

in the lot should not be more

than + 0.3

Ensure that the yarn being

used for Knitting is of the

same Lot.

Ensure that the hardness of

all the yarn packages is

uniform using a hardness

tester.

2. Needle

Line

Needle lines are

prominent vertical

lines along the length

of the fabric which

are easily visible in

the gray as well as

finished fabric

Due to defective needle.

Dirty needle slot.

Needle too tight or loose in

the slot.

Due to improper lubrication

of needles

Replacing all the defective

needles having,

bentlatches, hooks or stems.

Removing the fibers

accumulated in, the Needle

tricks (grooves).

Replacing any bent Needles,

running tight in the tricks.

Checking the Needle filling

sequence in the Cylinder /

Dial grooves (tricks)

3. Hole Local Holes obtained

when yarn breaks

during loop

formation.

Due to badly tied knot.

Needle break due to slub.

Due to high tension of yarn.

Ensuring uniform yarn

tension on all the feeders,

with a Tension Meter.

Rate of yarn feed should be

strictly regulated, as per the

required Stitch Length.

Eyelets & the Yarn Guides,

should not have, any fibers,

fluff & wax etc. stuck in

them.

The yarn being used, should

have no imperfections, like;

Slubs, Neps & big knots etc.

4. Oil Mark Oil lines are

prominent vertical

lines which appear

along the length of the

knitted fabric tube.

The lines become

permanent if the

needle oil used is not

washable & gets

baked due to the heat

during the finishing of

the fabric.

Due to improper lubrication.

Fibers & fluff accumulated

in the needle tricks, which

remain soaked with oil.

Fibers, accumulated in the

needle tricks, cause the oil to

seep into the Fabric.

Remove all the Needles &

the Sinkers of the machine,

periodically.

Cleaning the grooves of the

Cylinder & Dial of the

machine thoroughly.

Blowing the grooves of the

Cylinder, Dial & Sinker

ring, with dry air after

cleaning.

Literature Review

16

TABLE 2.2 : Description of Defects in Knitted Fabrics (cont.).

Sr. No. Fabric

Defect


5. Foreign

Materials/

Fly

Contaminations

appear in the form of

foreign matter such

as; dyed fibers, husk,

dead fibers etc. in the

staple spun yarn or

embedded in the

knitted fabric

structure.

If foreign materials knit with the

yarn.

Blowing the grooves of the

Cylinder, Dial & Sinker ring,

with dry air during cleaning.

6. Press Off Fabric press off

appears as a big or

small hole in the

fabric caused due to

the interruption of the

loop forming process

as a result of the yarn

breakage or closed

needle hooks.

When all or some of needles on

circular knitting fail to function

and the fabric either falls off the

machine or design is completely

disrupted or destroyed.

Needle detectors, should be

set precisely, to detect the

closed needles & prevent the

fabric tube from completely

pressing off.

Proper yarn tension should

be maintained, on all the

feeders.

7. Sinker

Lines

Sinker lines are

prominent or feeble

vertical lines

appearing parallel to

the Wales along the

length of the knitted

fabric tube.

Bent or Worn out Sinkers

Sinkers being tight in the

Sinker Ring grooves.

Replace all the worn out or

bent sinkers

causing Sinker lines in the

fabric.

Sinker lines are very fine &

feeble vertical lines

appearing in the fabric.

Remove the fibers clogging

the Sinker tricks.

8. Spirality Spirality appears in

the form of a twisted

garment

after washing. The

seams on both the

sides of the garment

displace from their

position & appear on

the front & back of

the garment.

High T.P.I. of the Hosiery

Yarn

Uneven Fabric tension on

the Knitting machine.

Unequal rate of Fabric feed

on the Stenter, Calender &

Compactor machines.

Use the Hosiery yarns of the

recommended TPM level for

Knitting.

Ensure uniform rate of feed

of the dyed fabric on both

the edges while feeding the

fabric to the Calender,

Compactor or Stenter

machines.


17

Images of the defects in knitted fabrics:

Some of the common defects found in knitted fabrics have been illustrated in Figure 2.2 [9,

10, 11].

FIGURE 2.2 : Defects in knitted fabrics

Literature Review

18

2.4.4 Defects in Nonwovens:

Some of the common defects obtained in the Nonwovens are described in Table 2.3 [1,12,13]

.

They are illustrated in Figure 2.3[1,12,13]

.

TABLE 2.3: Common Defects in NonWoven Fabrics

Name of Defect Description

Eye brows Stretching or folds in fabric

Drops / bond point fusion Fused fibres on surface

Pinholes Very small holes in fabric

Thinspots Low density of fibres in a particular area

Wrinkles Wrinkle formation

Hard filaments Fused filaments on surface

Insects Trapping of insects in web/fabric

Monomer / polymer Drips Formation of spots on surface by droppings of monomer / polymer

Holes Holes in fabric/ web

External Contamination / Dirt Contamination due to external factors like dust, dirt, etc.

Oil contamination Contamination due to oil on surface of fabric

Melt-blown fly Drip in melt blown fabrics

Broken Filaments Filaments are broken in web

Calendar cut Cut marks due to calendaring

Scratch Scratches in web/fabric

Clumps Compact mass of fibres

Blowback Fibres in opposite direction of normal orientation

Un-bonded web Loose fibres in web

Rough Fabrics Surface of fabric is uneven

Streaks Thin line marks in fabric

Bad uniformity Uneven fabric

Spills Coming out of fibres/lump of fibres from the surface

FIGURE 2.3 : Defects in Nonwovens

Fabric Inspection

19

2.5 Fabric Inspection:

2.5.1 Visual Manual / Traditional Inspection Methods:

Visual Inspection of fabrics basically involves visual checking of the manufactured fabric

lot for the defects produced in it during the fabric manufacturing process. As mentioned

earlier a defect may be characterised by an imperfection that impairs worth or utility and

spoils the utility of material and thus is majorly concerned with the texture characteristics

of the surface of the fabric. Visual checking thus involves identifying and recording of the

defects. The traditional method of visual inspection involves manual - visual inspection by

well-trained human inspectors. The fabric inspection is performed manually by human

inspectors and using off-line stations as shown in the Figure 2.4. A trained person inspects

all types of fabrics and identifies all defects and then divides them into the corresponding

grades and accordingly the performance of the fabric may be assessed.

FIGURE 2.4: Manual Fabric Inspection

Literature Review

20

The oldest inspection method involved pulling of fabric over a table by hand. Now-a-days

power driven inspection machines as shown in Figure 2.5 are used. There are different

input/output variations (roll to roll, batch to batch, fold to fold) available in the latest

checking machines with a wide range of optional equipment for flexible inspection routine.

FIGURE 2.5 : Power Driven Inspection machines

The inspection process is done in suitable environment with proper ventilation as well as

lighting. The frame through which the fabric passes is normally at 45-60 degree angles to

the inspector. The illumination may involve top and back lighting which is used according

to the fabric to be checked. The speed of the inspection machine is normally less so as to

allow the inspector to view the fabric viewing area properly. When the inspector sees any

defect on the moving fabric, he records the defect name and the size of it. Also he mends

all the possible repairable defects [14]

.

Fabric Inspection

21

The main drawback of this method was its accuracy with identification rate about 70%. [15]

Many defects are missed, and the inspection is inconsistent, with its outcome depending on

the training and the skill level of the personnel. Also the work of inspectors is very tedious

and time consuming. They have to detect small details that can be located in a wide area

that is moving through their visual field. In addition, the effectiveness of visual inspection

decreases quickly with fatigue.

2.5.2 Automatic Fabric Inspection Systems:

As discussed above, manual fabric examination is very tedious and requires highest level

of concentration, which can be maintained only for about 20 to 30 minutes. Even in a well-

run operation, the reproducibility of a visual inspection will rarely be over 50 % [2]

.

Therefore the introduction of automated inspection was done which gives more reliable

results which are free from the subjective deficiencies of visual examination methods.

There have been a lot of research and development being done in the field of automated

visual inspection techniques for detection of fabric defects. Image Analysis [3,4,15,16]

is

widely being used for detection of defects. Also a variety of advanced approaches like

ultrasonic imaging system [17]

& laser optical systems [18]

have been proposed. Ultrasonic

system uses an ultrasonic imaging technique, which uses pair of ultrasonic transducers

operating efficiently in air at a predetermined frequency [17]

. Each pair has a transmitter

and a receiver. The signals received are processed using signal processing and image

processing to assess defects in fabrics. Laser Optical system comprises of a laser unit and

cylindrical lenses, which performs 1-D imaging of the weft yarn [18]

. The signals received

are used basically to give the weft density, weft count, fabric length and fabric speed. The

variations from the mean weft density and mean weft count are used as variables to detect

various fabric defects in the woven fabrics.

Among the approaches being researched, the most common one is the use of image

analysis that is use of computer vision system to detect the defects in the fabric[3,4,15,16,19-30]

.

Even the commercial systems largely follow the same approach. Commercial application

of image analysis for defect detection are being done by various companies like I-Tex of

Literature Review

22

Elbit Vision Systems ltd., Uster Fabricscan of M/s. Uster Technologies, Barco Vision‘s

Cyclops of M/s. Barco, SCANTEX of Sam Vollenweider, RoiBox web inspection systems

[russuka akerbag] etc. but the main hindrance of it‘s use in small scale industries is the

capital cost [31, 2]

.

In the automatic fabric inspection the checking of the surface characteristics of the fabric is

done with the help of a camera instead of human inspectors [2,3,16,31]

. The images of the

fabric surface are captured by a camera and then the image analysis of the captured image

is done to detect the defects in the fabric with the help of any image processing software.

Basically Image Analysis can be subdivided into two steps:

i. Image acquisition: the images of the fabric samples are captured using CCD or a

digital camera.

ii. Image analysis/pattern recognition by measuring scale and shape to identify the

defects.

2.6 Use of Image Processing in Assessing Structural Variation in

Different Areas of Textiles:

2.6.1 Image Processing & Structural Variability in Fibres:

Microscopic evaluation of cross section of the fibres is largely done for analysis of fibres

and it‘s characteristics since the evolution of microscopes. The sample preparation

involved in the microscopic evaluation needs to be very precise and the whole process is

time consuming also leading to research being done for the use of image processing for the

same. Image analysis is an attractive alternative to existing systems for investigating some

quantitative fiber characteristics.

A lot of studies are being done in assessing the structural variation in fibres to identify the

fibre structure as well as some properties like fibre fineness and maturity [32-38]

. Xu and

Huang (2005) [35]

suggests a method for analysing cotton fibres by taking images of fibre

cross section and processing the images for estimating the fineness and maturity of the

fibres. Xu, Pourdeyhimi & Sobus (1993) [53]

also suggests fibre cross sectional shape

Use of Image Processing in Assessing Structural Variation in Different Areas of

Textile

23

analysis using Image Processing Techniques which can be used to determine various fibre

parameters and properties.

Studies have also been done by using longitudinal images of cotton fibres for fibre

analysis[33]

. Ghith, Fayala & Abdeljelil (2011) proposes a maturity analysis of fibres by

image analysis by using longitudinal images of cotton fibres [33]

. The main advantage of

using this approach was that it eliminated the précised sample preparation which was

required cross sectional fibre approach. It was quick, reliable and unbiased technique

which was used to evaluate fiber maturity and fineness.

2.6.2 Image Processing & Structural Variability in Yarn:

Due to the various advantages of use of image processing in analysing fibre characteristic,

a review on the use of image processing for yarn analysis becomes necessary. The

commercial systems for yarn analysis and measuring the yarn parameters uses the

capacitive sensors but a lot of research is being done to check the use of image processing

for the same[39-44]

. The major yarn parameters include the yarn diameter, yarn mass or

fineness, twist and hairiness. The images of surface characteristics of the yarn are captured

using cameras which are processed using any image processing software and image

processing tools for estimating of the said parameters. Parameters of fancy yarn and

textured yarn have also been estimated using image analysis. Pan et all (2011) suggests

recognition of parameters of slub yarn parameters also using image analysis [43]

. Semnani

and Gholami (2009) suggests identification of defective points in false twist textured yarns

[39].

2.6.3 Image Processing & Structural Variability in Fabrics:

Just like textile fibres and textile yarn, image processing has been explored to assess the

various fabric characteristics [45-61]

. Image processing has been proved an efficient tool for

analysing fabric surface characteristics to determine various fabric parameters [45-61]

.

Literature Review

24

Pattern recognition has been vastly looked upon for analysing the woven fabric structure

[48,49,50,55,59]. A paper by Salem and Nasri proposes a method for weave identification by

analysing the structure of woven fabrics using image processing. They have proposed a

texture analysis for recognition of the weave patterns and classified the weaves

accordingly[50]

.

Successful determination of fabric air permeability [45,56]

and porosity in fabric [46, 57]

has

been obtained by using the various methods of image processing. Even surface properties

like pilling[47, 51, 60]

has been considerably estimated using this approach.

Texture analysis has offered good scope for measuring Fabric handle properties like

wrinkle[61]

, fabric smoothness[52]

, drape[53]

and surface roughness[54]

.

As discussed earlier effective methods for quality control of woven fabrics have been

proposed by various researchers by incorporating computer vision and image

processing[3,4,15,16,19-30]

. A line of ongoing research studies show variety of approaches have

been looked upon for estimating homogeneity in the fabrics majorly used for apparel

purposes. Studies in this area has shown quite positive results in detecting fabric faults in

woven fabrics and grading them depending on their texture characteristics [15,19,22,23]

. The

studies have vastly been done for woven fabrics used in apparel. The use of image

processing for texture analysis of nonwoven fabrics is little explored. However, fibre

orientation and web uniformity has shown positive results, but the area of defect detection

in nonwovens need to be explored [62-67]

.

2.7 Image Processing:

2.7.1 Introduction:

Image Processing basically refers to processing of images more specifically digital images

by means of a digital computer [68]

. The digital images are again obtained by means of

various imaging machines which cover almost the entire electromagnetic (EM) spectrum.

Image Processing

25

The applications of Image Processing now-a-days are numerous and include areas like

agriculture, biometrics, character recognition, forensics, industrial quality inspection, face

recognition, medical image analysis, security and surveillance, etc.

2.7.2 Human and Computer Vision System [68, 69]

:

Nixon & Aguado (2008) defines "Human vision as a sophisticated system that senses and

acts on visual stimuli and has evolved for millions of years for survival" [69]

. The major

components of the human vision system can be categorised as the eye (physical

component), a processing system (an experimental model which is not determined

precisely) and analysis by the brain.

They define "Computer vision system as system which processes images acquired from an

electronic camera, just like human vision system where the brain processes images derived

from the eyes".

It can be interpreted that computer and human vision system are similar in functionality,

however the computer vision system may not be able to exactly function like a human

vision system as the human vision system is not complete understood yet. The concepts of

the human vision system have been vastly used for developing the computer vision

systems.

The components of computer vision system basically include a camera, camera interface

(optional) and a processing unit.

Camera is the basic sensing element. The main types of cameras include vidicons

(older analogue technology), charge coupled devices (CCDs), complementary

metal oxide silicon (CMOS).

The processing unit includes computer system and computer software for

processing of images. The software includes system development softwares as well

as commercial image processing softwares. Use of mathematical

Literature Review

26

systems/mathematical tools like mathcad, mathematica, maple, matlab, scilab for

processing and analysing images.

2.7.3 Fundamental Steps in Digital Image Processing [68]

:

Digital Image Processing refers to processing of a two-dimensional picture by a digital

computer. A digital image is an array of real or complex numbers represented by a finite

number of bits/elements, each of which has a particular location & intensity values. These

elements are referred as picture elements, image elements or pixels. It is a basically a

matrix (a two dimensional array) of pixels. The value of each pixel is proportional to the

brightness/intensity value of the corresponding point in the scene.

The important steps involved in image processing are:

Image acquisition: Image acquisition is the first step where in the image of an object is

acquired using a suitable image sensor which can be any imaging device most common

one is the camera as described in the above section.

Image enhancement: Image enhancement is the process in digital image processing

which improves the interpretability or perception of information in the images acquired for

human viewers. It bring backs the detail that is obscured or lost. It may also be used to

highlight certain important features of the image. Changing the contrast of an image for

better view is one of the tool for image enhancement. It may be described as a subjective

pre-processing tool also.

Image restoration: Image restoration is a process which improves the appearance of an

image by elimination or compensation for any kind of degradations in images. The

degradations in images are normally a result of motion blur, noise, camera misfocus, etc. It

is an objective process, as the restoration techniques tend to be based on mathematical or

probabilistic models of image degradation.

Image Compression: Compression is a technique of reducing the storage required to save

an image, or the bandwidth required to transmit it. It may be defined as the process of

encoding data using a representation that reduces the overall size of data. It allows the use

Image Processing

27

of the images on platforms, where storage is a limitation, thus allowing its use in wider

range of applications. It is widely used in computers in the form of image file extensions,

such as the jpg file extension used in the JPEG (Joint Photographic Experts Group) image

compression standard.

Image segmentation: Segmentation means partition of an image into its constituent parts

or objects. It is the process of separating distinct regions containing each pixels with

similar attributes within an image i.e. set of pixels separating one image region from other.

An autonomous segmentation is considered as one of the most difficult tasks in digital

image processing as a rugged segmentation procedure may provide successful solution of

imaging problems to identify objects individually and on the other hand weak or erratic

segmentation algorithms may be a failure. Accuracy in segmentation influences the success

rate of object recognition

Image Recognition: Recognition is the process that assigns a label to an object based on

its descriptors like pattern matching, face or character recognition, etc. and is widely used

for applications like factory automation, monitoring and security surveillance.

Colour Image processing: Colour Image processing uses the variations in the colour or

the intensity spectrum in an image as the basis for extracting features of interest in an

image.

2.7.4 Methods of Assessment of Image Analysis or Image Quality:

It is important to determine the image characteristics before and after processing of images

to analyse the images and also to interpret them. The different methods used for

measurement of image quality have been discussed here:

Subjective Quality Assessment:

It is the method which uses subjective experiments and involves human to vote for the

quality of image in a controlled test environment. It may be described as a method of

analysing images by measurement techniques which provide numerical values that

Literature Review

28

quantify viewer's satisfaction. It depends on the type, size, range of images, observer's

background, experimental conditions like lighting, display quality, etc. It involves ranking

of images on a 5-point scale as: bad, poor, fair, good and excellent. As it involves

subjective ranking, the ranking may differ from individual to individual and therefore is

difficult to design any constructive method for performance improvement.

Objective Quality Assessment:

It can be described as a method to measure image quality algorithmically. It involves use

of image metrics like Peak Signal-to-noise (PSNR) and Mean Squared Error (MSE), etc. to

determine general image characteristics. The measurement techniques are easy but they

may not consider human visual sensitivities. These methods are not effective in predicting

distortion visibility and also for enhancing images with large luminance/intensity

variations. A combination of numerical and graphical measures is considered a better

means for analysing image quality.

Perceptual Quality Assessment:

This type of assessment is basically advancement of objective quality measurement as it is

based on human visual perception like image discrimination and tasked based models, so

as to characterize the variations in quality across the whole image. It suggests methods to

analyse images using algorithms which does not require human judgement. Algorithms

basically classified into three types based on the assessment of quality of images with

respect to a perfect (reference) image : full reference, no-reference & reduced-reference.

The algorithms using texture of the images has been considered vastly for automated

interpretation of digital images [70-75]

. These algorithms follow statistical, structural,

spectral approaches or model based approaches [70-75]

. The same has been summarised

below:

Statistical Approach: The statistical properties of images based on intensity

variations in the image are used for texture analysis. The statistical properties

include mean, variance, skewness, kurtosis, etc. These values are used to

discriminate between the images.

Image Processing

29

Structural Approach: It involves extraction of structural information from the

visual scene to define the image. Structural distortion is used to analyse image

quality.

Spectral Approach: In images following any periodic patterns, spectral approach

serves as a solution for image analysis. The periodic patterns are estimated using

the frequency domain of the intensity of the images. The same is used to extract the

texture characteristics. Differences in the frequency content are used for

segmentation of images with different textures.

Model Based Approach: It is effective for texture images which are stochastic in

nature. The image parameters are estimated to generate an image model which can

be processed as well as synthesized. It is useful for modeling natural textures.

2.8 Image Processing & Fabric Inspection:

2.8.1 Introduction:

As mentioned in the earlier section, image processing operations transform an input image

to an output image from which the desirable characteristics can be extracted for

interpretation and analysis. As described earlier, image processing is vastly used in variety

of areas for monitoring and control. It has been described in Section 2.6.3 about the use of

image processing in fabric inspection, most commonly the woven fabrics for quality

monitoring and control. The inspection process using image processing basically

compromises of two main phases :- the learning phase and the detecting phase. A number

of images of surface of the fabric are processed in learning phase to understand and

analyse the basic image characteristics of the fabrics with/without defects. In the detecting

phase, the images of the fabrics with defects are further processed to extract defect

characteristics [19-30,76-79]

.

Automation in the fabric inspection process has been one of the most difficult tasks to be

implemented practically and commercially in the textile industry. The implementation,

challenges and difficulties, various approaches, components of the system and use of

different algorithms have been discussed in this section.

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30

2.8.2 Implementation, Challenges and difficulties:

Due to the complexity [80, 19-30, 76-79, 80-87]

in the practical implementation of the fabric

inspection systems especially for the functional fabrics, the research in this field is widely

open. Based on the study of the literature review carried out in this area, the challenges and

difficulties in the implementation of the same have been briefed as below:

There are a variety of fabric faults obtained differing with respect to nature, size,

frequency and severity.

Variations in defect characteristics obtained in woven, knitted and nonwoven

fabrics.

Classification of defects.

Characterization of defects in case of nonwovens because of their homogeneous

fibrous structure.

Grading of fabrics, especially functional fabrics as the grading varies as per the

application area of the fabric.

Online Image acquisition.

Economical quality monitoring systems.

Extremely high data flow.

Distortion in images.

Use of different algorithms for different nature of defects.

Nature of the fabric characteristics normally imparts stretch and skew of fabric

texture.

2.8.3 Image Acquisition:

Image acquisition is the first and important step in developing and designing any quality

monitoring system using image processing. Image acquisition is basically the process of

obtaining a digital image from some hardware based source. The source usually depends

upon the application area and ranges from a desktop scanner to a massive optical telescope.

The image sensing mechanism can be further classified as image acquisition using a single

sensor like photo diode, image acquisition using sensor strips like flatbed scanners and

Image Processing & Fabric Inspection

31

image acquisition using sensor arrays such as CCD, CMOS camera. Trials have been done

using on line-scan CCD camera, digital camera, high resolution scanners, laser scanner,

optic scanner, etc. Studies related to PC based real time inspection systems have

mentioned about the influence of image acquisition on the over image quality, the

algorithms used and thus the effectiveness of the system as a whole[some excel sheet].

The fabric inspection usually uses a digital camera (CCD or CMOS) as a source for image

acquisition and has been described in detail in the next section.

2.8.4 Components of Fabric Inspection Systems:

An automatic fabric inspection system mainly comprises of 4 sections: Fabric unwinding

section, image acquisition section, fabric rewinding section and monitoring and analysis

section as shown in figure. The image acquisition, monitoring and analysis sections

differentiate between the conventional fabric inspection systems and automatic fabric

inspection systems. Figure shows commercially available fabric inspection systems from

Elbit Vision System-EVS.

FIGURE 2.6 : Automatic Fabric Inspection System

Literature Review

32

FIGURE 2.7 : Commercial Automatic Fabric Inspection System – Elbit Vision

System

The image acquisition section comprises of a source of image acquisition, source of

illumination & frame grabbers.

The source of image acquisition is normally by means of a CCD - charge coupled device or

a CMOS - (Complementary Metal-Oxide Semiconductor) camera. The comparison

between the two has been briefed out in the table below:

TABLE 2.4 : Comparison of CCD and CMOS camera

CCD CMOS

Here many signal processing functions are

performed outside the sensor.

It incorporates amplifiers, A/D-converters and often

circuitry for additional processing.

Higher power consumption than CMOS, hence has

heat issues.

Less power consumption than CCD, hence low

temperature inside camera.

Heat issues can increase interference. They tend to suffer more from structured noise.

There are mainly two types of techniques involved in image acquisition: line and area

scan[88, 89]

. The main difference between the two has been highlighted in the table below:

Image Processing & Fabric Inspection

33

TABLE 2.5 : Comparison of Line Scan and Area Scan Camera

Line Scan Area Scan

They contain a single row of pixels to capture image. They contain a matrix of pixels that capture an

image.

The image is reconstructed in software line by line as

the object moves past the camera.

The image of the whole area is captured.

They are expensive and used for high end

applications.

They are more general purpose than line scan

cameras, and offer easier setup and alignment

Best suited for high-speed processing or fast-moving

conveyor line applications.

Best suited for applications where the object is

stationary.

Illumination is a major issue in any image acquisition systems as the type of illumination

effects the quality of the image captured [80]

. The different illumination configurations have

been illustrated in Figure 2.8 [90]

.

FIGURE 2.8 : Various Illumination Configurations

Guruprasad & Behera (2009) [2]

suggested that the basis of choice of an illumination

depends on the fabric density, defect types and stage in which the inspection is carried out.

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34

Figure 2.9 shows the influence of illumination in a textile material. The front or top

lighting is normally used for enhancing surface texture while backlighting is normally used

to enhance the structure of translucent fabrics. Literature suggests use of Infra-red lighting

[29], fluroscent lamp

[91] and halogen lamp

[78], however the influence of them is hardly seen

and selection of lamp to suit the cost economy would be desirable.

FIGURE 2.9 : Influence of Illumination in Textile Materials

A frame grabber is an electronic device used for capturing still frames from analogue or

digital video stream. Use of frame grabbers is normally used in computer vision system.

Use of frame grabbers is expensive and with the use of digital cameras, they can be

replaced by any kind of video multiplexer unit.

The Monitoring and analysis section comprises of a PC platform with inspection software

module. It is the main image processing and analysing unit and the main functions of this

section are defect detection and control of image acquisition as well as the whole system.

The algorithm used in the software module majorly contributes to the effectiveness of the

system. The various approaches incorporated in designing the algorithms have been

discussed in the next section.

Algorithms for Automatic Defect Detection

35

2.9 Algorithms for Automatic Defect Detection:

2.9.1 Introduction:

As mentioned in the previous section, the image processing and the defect detection

algorithm contributes largely to the effectiveness of the inspection system. The various

approaches and algorithms commonly implemented in the image processing have been

discussed briefly in Section 2.7.4. Since the algorithms play a crucial role in automated

defect detection, the various approaches and different algorithms used specifically for

automated textile inspection system has been discussed in this section.

2.9.2 Structural Approaches:

Structural approaches use the idea that textures are made up of basic elements appearing in

more or less regular and repetitive arrangement. It involves extraction of texture elements

to interpret the texture. It is said to work well for regular texture and often to synthesize

textures [72,73,74,92,93]

. Structural approach using different algorithms like: studying the

skeleton and background texture to identify defects [94]

, defect detection using a texture

blobs detection algorithm has been proposed [95]

. Images of plain and twill woven fabric

samples were used. The algorithms needed to be specific with textures and also a lot of

computation was complex. Maximum frequency difference comparison was tried out as an

improved solution to blob detection algorithm giving a higher detection success rate [96]

.

One of the major drawback of this approach was missing out or confusion between the

structure of the defect and an irregularity periodic in nature [97,98]

. Also the variations in the

fabric structure lead to complications in the extraction of texture primitives [3, 80]

.

Literature Review

36

2.9.3 Statistical Approaches:

Statistical approaches propose measurement of the spatial distribution of intensity values,

which are defined by pixel values in a digital image. On the basis of statistical behaviour of

results, the texture analysis is done as well any deviation found from the mean values may

serve a reason for identification of the defects. While following this approach, an important

assumption which is made is that the statistics of the defect –free region is stationary and

these regions extend over significant portion of images [3, 80]

. This approach normally

attributes to the study of the intensity pattern within the image normally defined by the

intensity distribution within the image. Brief introductions to some of the methods using

statistical approaches have been described below:

Thresholding Approach: It involves the study of the gray intensity values[9,100,101,102]

. It is

also referred to as gray-level thresholding. Simple, bi-level and multilevel thresholding are

the common ways of using this approach. Simple gray-level thresholding is direct and can

be used to detect a defect with high contrast. The intensity histogram of this type of images

normally shows a single peak clearly defining the threshold value. The gray level higher or

lower than the threshold accounts to a defect. This approach has been used in developing

fabric inspection system for woven fabrics and have been able to identify defects like big

holes and dark stains in woven fabrics[3,80,103]

. Also the implementation of this kind

approach is quite easy, but cannot be used to detect all kinds of defects. Dust particles, lint

and light conditions may introduce false alarms. Studies using different methods of

thresholding include simple thresholding methods, global thresholding methods, adaptive

thresholding methods, otsu or automatic thresholding methods. Islam et all. suggests to use

a decision tree for determining a general threshold as different weaves and structure of the

fabric may lead to different threshold value[15]

. Use of fast adaptive thresholding limit to

detect low contrast defects in galvanized metallic strip has been studied upon[104]

.

Correlation: Cross correlation (between images) and autocorrelation(within image) are

the normal techniques involved in this approach.


37

Normalized cross-correlation approach: A correlation between the database of images with

defect and free of defect is derived and defined by a cross correlation function [3,80]

. This

function provides a measure of similarity between the images and significant variation is

measured, which indicates presence of defect.

Autocorrelation function (ACF) approach[99,105,106, 07]

: The characteristics of the repetitive

structure may be extracted as a part of pre-processing and then a correlation between the

image itself and the image translated with displacement vector is measured. Therefore it

can be considered quite similar to the power spectrum of fourier transform. The intensity

of the maxima is supposed to be constant for a repetitive primitive for an image of defect

free fabric while it will change dramatically in an image with fabric defect and therefore

this approach have been studied for fault detection in woven fabrics but the main limitation

is it cannot analyse a texture without a reference of primitive [108]

. Hoseini et all (2013)

used Otsu's approach for thresholding for patterned and plain woven fabrics[107]

.

Histogram based Approaches: It involves study of the histogram i.e. the intensity

distribution of the image and the commonly used methods are use of statistical moments,

cumulative histogram or rank order and enhancing contrast using histogram.

Statistical moments approach - It involves obtaining statistical parameters of the image

intensity distribution or the histogram like mean, standard deviation, skewness and kurtosis

and therefore is quite simple in implementation though considerable preprocessing of the

images may be required for images with non-uniform illumination conditions. Texture

features are obtained directly from the gray level image by computing moments in local

regions[29, 100]

. It is useful in segmenting binary images containing textures with iso-second

order statistics.

Rank order- The rank-order function is also based on histogram analysis, the difference

being the intensity values are sorted in the ascending order also can be termed as

cumulative histogram. The histogram and the rank function both provide the same

Literature Review

38

information, but the rank functions are used as rank distances can be efficiently computed.

Also the median and the other rank-order filters based on the rank function have been

proved useful in modifying image local properties and facilitating adaptive rank order

functions [109]

.

These approaches have been quite successful in determining defects in ceramic tiles [110,

111]. Since the fabric texture is likely to have a varying spatial distribution, orientation, etc.,

the approaches based on classical histogram and cumulative histogram analysis has limited

its use in fabric defect detection.

Studies have been proposed by S. Hariharan, S. A. Sathyakumar, P. Ganesan on measuring

of fibre orientation in nonwovens using image processing but not on detection of the faults

and their classification [65]

. Paper describes the application of image processing techniques

for measuring the fibre orientation in nonwovens. Three types of nonwoven fabrics were

studied. Spatial uniformity of fibrous structures has been described statistically by using

index of dispersion. Results show the technique is capable to identify variation in

geometrical dimensions of very small textile objects. Studies propose measuring of fibre

orientation in nonwovens using image processing but not on detection of the faults and

their classification. By elaborating the digitization algorithm along with numerical methods

would give solutions for obtaining characteristics of nonwovens and thus improving the

quality.

Histogram properties approach [20, 99,105,112]

- In this approach, histogram analysis is done

i.e. a point-to-point analysis is done. The properties of the histogram may be used to

enhance the images by histogram equalization. Histogram equalization operation reassigns

gray level values of pixels to achieve a more uniform intensity distribution in the image

thus enhancing the contrast in the image.

Fractal Dimension(FD): It is basically a ratio providing a statistical index of complexity

comparing how the pattern changes with the scale at which it is measured. Fractal methods

have been fruitfully used to model the statistical qualities like roughness and self similarity

in many natural textures [80,113,114]

. Conci and Proenca (1995) suggested use of fractal


39

dimension with differential box counting method, which minimised computational

complexity [115]

. Eight types of woven fabric defects were studied and the detection

accuracy obtained was 96%. However, due to poor localization of fabric defects, chances

of false alarms rate were found. Also due to wide range of fabric textures, the range of FD

also becomes wide limiting the use of the algorithm.

Edge: The gray level transitions are determined by variations in the intensity values and so

lines, spots and other spatial discontinuities can be represented using these gray level

transitions and this has been used to detect fabric defects [116,117]

. This approach has been

found suitable for images of plain woven fabrics with low resolution [116,117]

. The main

limitation of this approach is that the gray level transitions are influenced by the noise

generated due to the structure of the fabrics and therefore can result in false detection of

faults.

Lai et all. (2005) have developed a Nonwoven Defect Detecting Method (NDDM) using

gradient conversion and watershed transformations for segmentation of nonwoven images

into basically too thick and too thin regions [82]

. Textures of defect free nonwovens and the

ones with defect both have thick and thin regions and the regions which are too thick or too

thin are considered to be regions with defects. The methodology suggested by them

involved determining region average i.e the normal deviation from mean. The images

which had deviations more than the normal deviation were identified as defected regions.

The study included images of three types of nonwovens (standard as well as defective)-

thermal calendared, needle punch and spun bond. The defects included tears, folds and

heavy spots. The NDDM was effective in finding the too thick and too thin areas, however

it lack the identification of defects.

Morphological operations approach: Morphological operations basically involve

extraction of boundaries and skeletons if any within the texture images by erosion and

dilation [3,109,118,119]

. These operations result in better smoothing, sharpening and noise

removal. This approach has been proved quite well along with multi level thresholding and

critically selecting the structuring elements. A simple thresholding pre processing

Literature Review

40

operation to obtain a binary image resulted in missing out of commonly occurring defects

of woven fabrics when processed with morphological operations.

Use of Filters: Use of appropriate filters as a part of pre-processing an image facilitates

discrimination between two textures which can be obtained by using of various filters in

the learning phase. Optimal Filters, Eigenfilters or Independent Component Analysis

approach have been found to be used for removal of noise in the images[80, 120-128]

.

The eigen filters adapt automatically to the class of the texture to be treated and therefore

has found its use over traditional approaches. They are useful in separating pair wise linear

dependencies between image pixels but not very useful for higher order dependencies.

Since the most fabric textures are found having higher order relationships among image

pixels, a fabric defect detection using Independent Component Analysis (ICA) was tried

upon. But these approaches are highly sensitive to local fabric distortions and background

noise and therefore limited to its implementation for fabrics with random structures.

Local linear transforms Approach [3,80,129,130]

: It uses common bi-dimensional transforms

like Discrete Cosine Transform (DCT), Discrete Sine Transform (DST) or Discrete

Hadamard Transform (DHT) as a statistical justification for the extraction of texture

properties. As mentioned earlier the important information of fabric textures is contained

in higher order relationships among the pixels, obtaining this information by using a simple

histogram along selected axes in the space of pixel values in a specified neighbourhood

was tried upon. Also this method was tried out as a substitute for eigen filters.

Co-occurrence matrix approach [3,70,80,105,131-135]

: This approach is one of the popular

statistical approach used for texture analysis and has been used for characterization of

textures such as grass, wood, etc. There are normally repetitions in the gray level

configurations in any texture and this is used as the basis of co-occurrence matrix, which

contains information about the positions of pixels having similar pixel (gray level) value.

This information is used to approximate the probability distribution function of any texture


41

thus defining the model of texture. Texture features such as energy, entropy, contrast,

homogeneity and correlation are derived from the co-occurrence matrix and has been used

to characterize various textures.

Salem & Nasri (2011) used the GLCM approach for weave identification in woven fabrics

found it quite useable for identification of satin, twill and plain weaves[133]

. Raheja et

all.(2013) proposed a defect detection system for woven fabrics recently. They

implemented it on an embedded DSP platform, where they used GLCM to extract textures

of the fabric [134]

. The energy feature was found to be the best of use to determine the

defects in the texture. Pritpal and Prabhjyo (2015) implemented this approach for studying

its effectivity in woven fabrics[135]

. They observed shift in the values of the GLCM

parameters in the images with fabric defects compared to the one void of defects. This

technique was found to be computationally expensive thus limiting it‘s use for online

fabric inspection. Also the number of gray levels needed to be reduced to keep the size of

co-occurrence matrix manageable, which might reduce the accuracy of the method.

Iivarinen & Rauhamaa(1998) have developed a method for inspecting web surfaces using a

second order statistical measure of gray level variration i.e. co-occurence matric[86]

. The

shape features, gray level histogram and texture features extracted were used for

classification of the defects. However, the said study was used for images of paper web

and not the textile web.

Neural-Networks Approach [3,15,80,81,105,136-140]

: Artificial neural networks are basically a

family of models or network of neurons inspired by biological neural networks and are

used to estimate functions depending on large number of known or unknown inputs. They

are specified using their architecture (variables in the network and their topological

relationships), activity rule (the change in activity of neurons in response to each other)

and learning rule (understanding of the activity of neurons and its dependence on the target

values). They are considered among the fastest and most flexible classifiers, especially for

the fault detection owing to their non-parameteric nature and high ability to define

complex decision regions. Hence they are being used for monitoring and control in the

Literature Review

42

wide range of manufacturing industry. They have been found to be non scalable to massive

datasets. However, fabric defect segmentation using artificial neural network(ANN), feed

forward neural networks (FFN), back propagations neural network (BPN) with fuzzy logic,

pulse coupled neural network(PCNN) have been implemented. They have been found to

be quite successful in automatically detecting the fabric faults.

Liu et all. (2011) have done a comparision of the bayesian neural network and LVQ neural

network for visual uniformity recognition of nonwovens and have found bayesian neural

network more efficient for classifying nonwovens based on uniformity[81]

.

2.9.4 Spectral Approaches

Spectral approaches are based on spatial-frequency domain features that are less sensitive

to noise and intensity variations. They are largely being implemented in the latest

computer vision studies as they simulate the human vision system. They have overcome

the drawbacks of many low-level statistical methods. The main limitation to this kind of

approaches is they require high degree of periodicity, so have successfully implemented in

defect detection of uniform textured materials like woven fabrics. In spectral-domain

approaches, the texture features are normally extracted using the Fourier, Gabor and

Wavelet Transform. The same has been discussed in the following sections:

Fourier Transforms: It is widely used to characterize textured images in terms of

frequency components. They offer good noise immunity and enhancement of the periodic

features and therefore can be used to extract the power spectrum of images of defect free

fabrics to determine or modelise the general fabric structure. In an image with defect, the

general fabric structure is disturbed leading to change in corresponding intensity in its

frequency spectrum at the specific positions of the defect. Various methods of fourier

analysis like Optical Fourier Transforms (OFT), Discrete Fourier Transforms (DFT),

Inverse Discrete Fourier Transforms(IDFT), Fast Fourier Transforms (FFT) and

Windowed Fourier Transforms(WFT) have been studied for its implementation in

detecting defects in woven fabrics.


43

OFT are obtained in optical domain by using lenses along with spatial filters and the defect

detection in woven fabrics using them have found to be relatively easy and fast[80]

. It

involves modulation of the luminous intensities of the zero and first order diffraction

patterns by the existence of fabric defects [118]

. These techniques are suitable for global

defects and to some extent local defects but not suitable for textures with difficulty in

differentiating defect free and defected regions.

DFT and IDFT are useful for their digital implementation as they recover images from

spatial domains. The images of textures of woven fabrics are characterised by the warp and

weft yarns in terms of warp and weft patterns. Each set of yarn can be modulated by its

profile using 1-D. Sarraf and Goddard (1996) have used the local statistics of these 1-D

parameters to administer yarn densities and offering a future scope to use the technique for

defect detection in woven fabrics[139]

. A method using DFT and Hough transform was

implemented by Tsai and Heish(1996)[140]

. 1D hough transform was used to remove the

line patterns of the woven fabric textures by detecting the high energy frequency

components in the fourier domain image, which were set to zero. It was back transformed

into a spatial domain image. After restoration of the image, the defect free images had

homogeneous line region had approximately uniform gray level, while in the defective

regions it was not the case. The DFT based approaches have not been found suitable in the

images of the fabric in which the frequency components of the defects and the fabric

structure are mixed up in the fourier domain restricting its use in patterned fabrics.

Use of 2 D Fourier spectral analysis has been used for characterizing nonwoven web

structure globally. The mean image gray level at different positions along the machine

direction and across the cross direction of the web was analyzed to check the uniformity of

the web[64]

.

FFT and WFT have the ability to localize and analyse the texture features in both spatial

and frequency domain thus giving scope for it‘s implementation against the above methods

especially for local defects

Literature Review

44

Gabor filters approach: When the window function is guassian, the WFT becomes gabor

transform. It is found to achieve optimal localisation in the spatial and frequency

domain[3,105,124-128,141-142]

. Research shows implementation of gabor filters in texture

analysis and has been found quite similar to texture recognition abilities of human

brains[141,142].

Junfeng & Huanhuan(2011) implemented this approach for defect detection

in woven fabrics. A new method has been suggested here[126]

. About 5 kinds of defects for

woven fabrics have been considered here. The experimental results obtained confirmed the

satisfied performance and the low computational requirement and the performance of

algorithm. The method was not suitable for all textile flaws. As the method was not

suitable for all textile flaws, and so gave scope for further work to be done in the area.

Trials using a bank of gabor filters has been done for it‘s implementation in defect

detection systems in woven fabrics[124-128]

. Filter banks are set of filters with predetermined

parameters in frequency and orientation. They may turn out to be computationally

intensive. Recent study has been carried out by Kang et all. (2015) for defect detection on

printed woven fabrics. They have used a genetic algorithm to construct the optimal gabor

filter, which can remove noise of fabric background to facilitate segmentation of

defects[124]

. However, the defects which were not clear with respect to fabric background

were found difficult to be recognised.

Optimized Finite Impulse Response (FIR) filters approach: The above spectral

approaches have not been completely able to identify fabric defects that produce very

subtle intensity transitions. Therefore use of Finite Impulse Response filters (FIR) has been

explored in the said area. The implementation of these filters is of computational ease as

they have more free parameters than gabor filters.

Wavelet analysis (transform) approach: The wavelet transform is considered to be one

of the standard tool for image processing for multi resolution analysis and is being

explored for image compression applications. The use of this approach has been explored

in the area of defect detection in textiles [127,143]

. Sari-Sarraf and Goddard(1999) developed

a defect detection system for woven fabrics using 2-D discrete wavelet transform. The

system could detect small defects and had an overall detection rate of 89%[127]

. Rallo et

all. (2009) proposed an algorithm which combines gabor analysis of the sample image with

a statistical analysis of the wavelet coefficients[128]

. The algorithms were tried for image


45

samples of paper and woven fabric. However, it failed to identify the main fabric structure

and so could not built the set of filters in the frequency domain.

Studies by Liu et all. (2011) on comparision of neural network for visual uniformity

recognition of nonwovens have used a Gaussian density (GGD) model in wavelet domain

for texture analysis of nonwoven images[81]

.

Another approach using Laplacian pyramid has been implemented by Weickret (1999)] for

online grading of nonwoven fabrics[21]

. The algorithm calculates a measure of quality by

analysing cloudiness (concentration of fibres) using Laplacian pyramid decomposition.

The pyramid needed to be modified at boundaries to reduce errors. It was found more

successful than use of wavelet and fourier approaches. The use of algorithm was restricted

to assessing only the cloudiness of nonwovens.

2.9.5 Model-based approaches:

The model based approach involves capturing of the process that generates texture by

generating of model of texture by determining the parameters of a pre-defined model. The

application of this approach has been tried when statistical and spectral approaches have

failed for detection of faults in fabrics. It involves synthesizing of textures and therefore

requires the image features at different levels of details matching the possible models of

different image classes. Thus the task becomes very tedious and computational intensive

when large number of models is considered. Some model based approach like Gauss

Markov Random Field (GMRF) Model, Poisson‘s Model and Model-based clustering have

studied for it‘s use in the texture analysis based defect detection[80, 101]

.

The study of the approaches used for defect detection in the fabrics shows that each of the

approach has it‘s own advantage and limitations. Therefore combination of the various

approaches have been explored like combination of more than one statistical approach like

thresholding and morphological or neural networks [15,78,136]

, statistical and spectral

methods [22, 70,81, 127 144, 145].

Literature Review

46

Studies by Liu et all. (2011) on comparision of neural network for visual uniformity

recognition of nonwovens used combination of statistical and spectral methods for grading

of nonwoven fabrics [81]

. They have proposed the Gaussian density (GGD) model, LVQ

and Bayesian neural network. The authors proposed the, LVQ and Bayesian neural

network. However, its recognition rate has to be improved. The paper describes all the

aspects of the proposed research in it. Introduction of neural networks increases the

complexity in the study and therefore increases computation time to improve the

recognition rate. The paper proposes comparison of neural networks rather than going for

simple classification of the faults in nonwovens as required by industrial point of view and

therefore the study is limited to validation from the point of view of experiments. The

paper is mainly focused on uniformity recognitions of nonwovens. So the objectives of

research should be centered around the quality of nonwovens only. But here in this article

it is related to mere comparison of complex techniques to be used for finding uniformity of

nonwovens rather than finding out some other simple solutions which may be useful in the

industry. The paper suggests method which is valid from point of view of experiments, the

problem regarding detection and grading of faults in nonwovens still remains for the

industrial applications to be explored in future. Also only one variety of nonwovens has

been considered over here and solutions for other types of nonwovens/functional fabrics

are required to be developed.

2.10 Fabric Grading:

2.10.1 Introduction:

A proper system of defect analysis or defect grading becomes a necessity for the

commercial viability between the sellers and consumers. The simplicity, accuracy and easy

execution of the grading system are benefitted by one and all in the business. The flow

based and point systems are the most common ones being used for the woven and knitted

fabrics being used for apparels. These systems are basically based on size, number and

frequency of the defects. The grading systems have been discussed briefly in the next

section.

Fabric Grading

47

2.10.2 Various Approaches [14,146,147]

:

Flow Based/Metric System of Measurement: The defects are basically classified as

major and minor defects:

Major: A defect severe enough if exposed to place an end item in seconds.

Minor: An imperfection that may or may not cause a second, depending upon its

location in the end item and/or its chance of being lost in fabrication.

The width of the fabric is measured in cms. Substitution of 25 cms for 9‖ increment of

defect measure is to be done. The results are calculated in points per 100 square meters.

Point Systems:

In the point system penalty points are given to defects depending on the size and

orientation of the defect. The most common ones are 4-Point system and 10-Point system

implemented by the textile industries.

The Four-Point System: It assigns 1- 4 penalty points according to the size and

significance of the defect. Points more than 4 are not assigned for any single defect. It

remains same for defect in either length or width direction. Only major defects are

considered and no penalty points are assigned to minor defects. Total defect points per 100

square yards of fabric. Fabric rolls containing more than 40 points are considered

"seconds" and the grading is the same for any end product.

TABLE 2.6 : Penalty Points in 4-Point System

Length of Defect Penalty Points

Upto 3 inches 1

3-6 inches 2

6-9 inches 3

Over 9 inches 4

Holes and Openings (1inch or less) 2

Holes and Openings (over 1inch) 4

Literature Review

48

The Ten-Point System:

In this system, the fabrics are graded as first and second quality. It assigns 1, 3, 5 and 10

penalty points according to the size and significance of the defect. Different points are to

added for defects in warp and weft directions, thus making the system difficult to use.

TABLE 2.7 : Penalty Points in 10-Point System

Warp Defect Weft Defect Penalty Points

Upto 1 inches Upto 1 inches 1

1-5 inches 1-5 inches 3

5-10 inches 5 inches to the half of fabric 5

10-36 inches Full width 10

There is no literature available for this kind of grading for nonwovens as well as functional

fabric. However, the Smart View WQM automatic fabric quality system from M/s. Cognex

grades the nonwovens as Poor, Usable, Good, Better, and Outstanding[148]

. The system

gives the grading in terms of grade value from 1 – 10 depending how the material looks

like. A quality value of 1 is reported when the material is of poor quality; value of 5 is

reported when the material is of good quality or value of 10 when the material is

outstanding.

FIGURE 2.10 : Grading of Nonwoven Fabrics

Summary of Literature Review

49

2.11 Summary of Literature Review:

From the literature survey carried out, it can be concluded that need for an efficient,

consistent cost effective quality control system is always a basic requirement for the textile

industry. The industry especially dealing with the functional fabrics is expected to deliver

first quality fabrics. It is also understood that the defects or faults in the fabric tend to

affect the quality grade of fabric and often affects the pricing of the fabric.

Thus accurate defect detection is a very important and traditionally visual inspection of the

fabrics is very common at present in the local textile industrial sector. But due to various

drawbacks of the traditional visual inspection systems like the low detection rate of defect

detection, high time consumption, inefficiency of the system owing to fatigue of classer,

etc., automated defect inspection system were introduced. These kind of few commercial

systems are currently available but are still considered as very expensive.

Therefore developing a PC based real time fabric inspection system has been an area of

research interest globally. Extensive studies relating to this area show a lot of studies being

done in the area of fabric inspection systems for woven and knitted fabrics used for

apparels. It was found that defect analysis was done by image analysis or rather texture

analysis using various image processing algorithms. The various approaches and

algorithms using computer vision and image processing in the area of defect detection in

various textures have been studied. It was found that each method had it‘s own advantage

and disadvantage.

The survey shows that most of the studies carried out involved a very few varieties of

fabric and also were limited to executing of the algorithm for about only 2 or 3 fabric

samples with different specifications of fabrics of similar nature have been considered. A

study considering more fabric samples with different fabric specifications but of similar

nature requires to be done.

Also very limited studies have been done in the area of inspection systems for functional as

well as nonwovens. Owing to the fibrous structure of nonwovens, the texture is completely

Literature Review

50

different from woven fabric giving scope for exploring the method of implementation of

various approaches for texture analysis.

It was also found that most of the implemented PC based inspection systems used high

quality equipment for image acquisition, and therefore a study using low cost camera could

be explored.

It was also found that a grading system does not exist for functional fabrics and nonwoven.

The scope of the thesis thus aims at developing cost and quality effective device for a

quality monitoring and analysis of functional as well nonwoven fabrics.

CHAPTER – 3

Proposed System & Research Approach

3.1 Problem Description:

Extensive literature survey of the papers gives an idea of the different approaches that have

been considered in designing quality control systems in the area of textiles. The study has

been summarized in Section 2.11. The problem thus can be extracted out from the

summary of the literature review and can be described as follows:

A study exploring the use of low cost acquisition devices with scalable algorithm

suitable for different fabric specifications of woven, knitted or nonwoven fabrics.

A need for cost and quality effective device for a quality monitoring and analysis of

functional as well nonwoven fabrics.

Proposed System & Research Approach

52

3.2 Proposed System:

Thus a cost and quality effective device for functional/nonwoven fabrics has been intended

to be developed during this research work.

The developed system will basically have two modules hardware and software. The

hardware components include a fabric quality monitoring device having a fabric delivery

roller, fabric take-up roller, motorised driving arrangement, PC or a laptop and a cmos

camera for image acquisition. The software module consists of an application for

monitoring and analysing the fabric. The system will be described in detail in the next

section.

3.3 Research Approach & Hypothesis:

Qualitative as well as formulative approach has been used for this research work. A device

was developed as a part of the quality monitoring system. The important parts of the device

are fabric delivery roll, illumination for capturing images of surface of the fabric, CMOS

camera for capturing images of surface of fabric and a fabric take up roller.

Functional/nonwoven fabrics were manufactured. Defects in the fabrics were a result of the

manufacturing process.

Two categories of fabrics were selected for the study: Woven Geotextiles and Spunbond

Nonwovens. Different GSM fabrics in each category had been sampled. A series of images

of the selected fabrics taken by the camera with the help of the device were processed for

studying the general features of the acquired images using MATLAB. The histogram trend

for all the images was studied.

Images of fabrics with common identified defects were acquired and processed using

MATLAB for the general image parameters. The defects were detected basically using a

unique algorithm developed for texture analysis. The defects were graded and compared

against manual - visual grading of the same.

Research Approach & Hypothesis

53

The algorithm was validated by processing it for multiple images of the same defective

regions. The algorithm was also checked for multiple images of different fabrics for

validation.

The proposed system will therefore check for variability and give defect statistics and

classify as per Defect Area. It will also check for no. of Defects in the Fabric Lot and give

% Defects in the Fabric. On the basis of the defect statistics a fabric grading system has

been developed which will classify the fabric for specific application.

CHAPTER – 4

System Design & Development

4.1 Introduction:

The main object of the research work is developing cost and quality effective system for

functional as well nonwoven fabrics. Therefore based on the literature review done the said

system designing was the first step. Efficient fabric inspection system is the prime

requirement of online quality control of the fabrics.

The main components of the fabric inspection system are:

Fabric Unwinding Section – It has a fabric delivery roller on which the fabric roll

to be assessed is mounted.

Image Acquisition Section – It is the section where the images of the fabrics are

captured using a CMOS camera.

Fabric Rewinding Section – After the capturing of the images the fabric is to be

rewound and which is done by a fabric take-up roller.

Monitoring and Analysis Section (Hardware and Software) – It comprises the PC

and the software module for analyzing the fabric.

Device Development

55

The process flowchart followed for the development of the system has been demonstrated

below:

FIGURE 4.1 :- Process flowchart of the developed system

4.2 Device Development:

Device development was the first and integral step to achieve our main object i.e. a cost

and quality effective system for functional/nonwoven fabrics. To design a device which

was cost effective as well as suitable for the said study, the development of device was

done in phases. The first phase involved development of a device for optimum illumination

and optimum camera distance from the fabric for getting considerable quality of images. In

the second phase, the device was advanced for capturing of images in roll form, since that

would be a requirement for online image acquiring system. In the third and last phase, the

automation of device was done.

The software module was developed using MATLAB and the code for the same has been

shown in the appendix B.

Device Development Image Acquisition Fabric

Sampling

Image Processing

• Enhancing Images

• Extracting Features Classification


56

4.2.1 Development of manually operated device:

The device was initially designed mainly for the image acquisition of the fabric samples in

general. Therefore a manually operated device was designed and developed and the same

has been illustrated in Figure 4.2. As mentioned in the literature review, the configuration

of illumination influenced the overall processing and the efficiency of the system,

provision for illumination from bottom and top both were given. The main parts of the

device included a frame / stand, scan box with provision from inside, top lights for top

illumination and a CMOS camera. A slot for adjustment of camera height was provided on

the side arms on the machine frame. The scan box had provision for illumination from

inside. The design of the base of scan box and a photograph of the scan box has been

shown in Figure 4.3 and Figure 4.4 respectively. Two 20 Watts CFL bulbs (which were

then replaced with LED bulbs for better outcomes) were mounted inside the box after the

box was prepared for illumination. White acrylic sheet was used for cover. Aluminium foil

was provided in bottom surface of the box for getting better reflecting properties.

FIGURE 4.2 : Manually operated device

Device Development

57

FIGURE 4.3 : Design of Box Base

FIGURE 4.4: Photograph of the developed scanbox.


58

The fabric samples cut of random size suitable to be laid flat on the scan box and were kept

on the scan box for image acquisition. Images of fabric were taken using the CMOS

camera which was connected with a laptop via a usb cable. The quality of images was

checked for top and bottom illumination. Also trials were taken for checking the optimum

distance of camera from the fabric surface to be monitored. Once the optimum height was

obtained, the device was further modified for image acquisition of fabric in roll form.

4.2.2 Further modification of device for image acquisition of fabric in roll form:

To achieve similar conditions as online monitoring, the device was further modified for the

image acquisition of the fabric in the roll form. Figure 4.5 shows the photograph of the

modified device. The main parts added to the device included a fabric let off/delivery roll,

fabric take up roll, a handle and driving for positive control of fabric.

FIGURE 4.5: Photograph of the Modified Device

Device Development

59

A chain and sprocket arrangement was used for positive control of the fabric from one end

(fabric delivery) to another (fabric take up). The fabric was cut to a width of 18cms and

wound onto the spindle of the fabric let off roll. The spindle was mounted on the frame and

it was positively driven by a chain and sprocket arrangement mounted on both the sides of

the spindle. The open end of the fabric was wound on the take up roll. The fabric could

now be moved from one end to another by means of a handle, which was mounted on the

fabric take up roll spindle.

A number of images of the manufactured fabric were acquired using this device to find out

the general parameters of the fabric images. Also the images for the fabric regions with

selected identified defects were acquired for further processing.

4.2.3 Automation of the Device:

In the last phase, the automation of the device was done to offer scope for continuous

monitoring of fabric in running condition. The manual-handle driven device was

transformed to a motor driven device. The physical modifications in the device included

motorised driving arrangement, vertical top illumination, frame for camera mounting,

replacement of the fabric take up and delivery roll spindles by package holders, covers for

the device along with the switches for operation of the device.

The Figure 4.6 shows the main parts and the driving arrangement of the fabric delivery and

take up roll. An AC motor with 20rpm, 100mA & 7 kg cm torque is mounted on the

machine frame at the front. The drive from the motor is positively transferred to the fabric

take-up roll by means of a chain and sprocket. The fabric delivery roll can be driven both

positively and negatively. Sprockets are provided on each side of the package holder,

which acts as dead weight for facilitating negative driving of the fabric delivery roll.

Positive driving arrangement of the fabric take up roll was facilitated by providing side

chain on the sprockets.


60

FIGURE 4.6 : Drive and main parts of the Device

Package Holders were designed so as to facilitate easy change of fabric rolls. The design

and the photographs of the final machine is shown in Figures from Figure 4.7 to Figure

4.11.

Device Development

61

FIGURE 4.7 : Different View Angles of Developed Device

a) Back View b) Side view c) Front view d) Side view


62

FIGURE 4.8 : Top View & Illumination Arrangement

FIGURE 4.9 : Side View of Machine with Switch Board

Device Development

63

FIGURE 4.10 : Passage of Fabric

FIGURE 4.11 : Final System


64

The switch box on the top machine cover consists of 4 main swtiches:-

Switch 1 – Main switch for power supply to the device.

Switch 2 – for starting the motor

Switch 3 - for using Top Illumination

Switch 4 - for using Bottom Illumination

A power and an usb cable are provided for getting the power supply to the machine and

inter-phasing with a PC/Laptop respectively.

4.2.4 Working of System using the Device:

The stepwise sequence of operating the device has been described below:

Step 1: The System (Device and PC/laptop) is made active by supplying power using the

Switch 1.

Step 2: The method of illumination is selected using switch 3 (top illumination), switch 4

(bottom illumination), both (top & bottom illumination simultaneously) or none (for

natural illumination). The selection of illumination depends on the nature of the fabric to

be used. The fabrics studied as a part of this research have been found suitable for top

illumination and the same has been used for capturing of the images.

Step 3: The fabric to be checked is selected using the software module via. PC/laptop. The

screenshot of the same has been shown in Figure. 4.12

Device Development

65

FIGURE 4.12 : Selection of Fabric Type

Step 4: The process of fabric monitoring is now started by pressing switch 2 which will

start motor so the drive to take-up roll will be transmitted by chain and sprocket wheel and

hence the fabric to be scanned will start winding on this take-up roll.

Step 5: The images of the fabric are acquired using the capture option from software

module via. PC/laptop. The screenshot of the same has been shown in Figure. 4.13

Step 6: The command for the fabric grading is given using the process option from

software module via. PC/laptop. The screenshot of the same has been shown in Figure.

4.13

The final output of the system is shown in Figure 4.14


66

FIGURE 4.13 : Capture & Process Option

FIGURE 4.14 : Final Output

Fabric Manufacturing and Defect Analysis

67

4.3 Fabric Manufacturing and Defect Analysis :

4.3.1 Introduction:

Since a quality control system for functional fabrics was intended as a part of this research

work, the next step after development of the device was to produce functional fabric of

different segments. 6 different varieties of functional fabric including woven as well as

nonwoven fabrics were manufactured for the study. The details of the fabrics have been

described in the next section.

Since a quality control system was intended to be developed for different functional

fabrics, it was decided to consider the defects occurring commonly in the said varieties of

the fabrics. So the defects considered were the defects which were obtained in the

manufactured fabric as a result of the fabric manufacturing process. Different types of

defects as mentioned in Section 4.3.3 were found in the manufactured fabrics.

However, the experts from IIT had suggested to consider only one variety of fabric

preferably spunbond nonwoven fabric during the Research Week held during month of

April 2015 at Gujarat Technological University, Ahmedabad. They had also suggested

considering some of the major defects occurred during the manufacturing of spunbond

fabrics. Also, they suggested validating the results so obtained by taking multiple images

of same defects.

After considering the inputs from the experts of IIT, the study has been narrowed down to

2 varieties of functional fabrics i.e. Woven Geotextiles & Spunbond Nonwovens. 6 types

of defects in each variety have been focused on in the study. The description of the

selected defects in the said two varieties has been made in Section 4.3.3.


68

4.3.2 Fabric Sampling:

The details of the fabrics manufactured have been described in this section. The fabrics

produced were of full width as they were manufactured at the local industries. The fabric

manufactured was cut to obtain a fabric width of 18cms as per the requirement of the

device developed. Fabrics of varying specifications had been manufactured in each

category. After the cutting of the fabric width, the fabrics of varying specifications were

stitched to obtain a roll. The roll so obtained was ready to be monitored and analysed by

the developed device.

Woven Functional Fabrics:

Geotextiles – Manufactured at M/s. Technofab, Udhana Magdalla Road, Surat.

• Machine Specifications:

– Sulzer Projectile Loom

• PU model loom

• 3.5 m and 5 m width

• Speed-230 rpm for 3.5 m & 180 rpm for 5 m

TABLE 4.1 : Specifications of Geotextiles

Sample Name epi x ppi warp x weft

Denier

GSM

G1 38 x 24 720 x 400 160

G2 38 x 26 400 x 400 120

G3 34 x 24 800 x 800 215

G4 38 x 24 660 x 660 210

G5 21 x 21 2000 x 2000 290

G6 36 x 24 720 x 720 210

G7 34 x 26 1000 x 1000 220


69

Industrial Fabrics – Manufactured at M/s. N.M. Gajjar Hotels Pvt. Ltd., R.S. No. 286/1,

Block No. 229, Mota Borasara, Village Kim, Surat.


– Water Jet Loom

TABLE 4.2 : Specifications of Industrial Fabrics


Denier

GSM

I1 48 x 60 200 D x 200D 56

I2 60 x 68 200 D x 200 D 78

I3 40 x 68 200 D x 200D 80

I4 60 x 64 200 D x 200 D 86

I5 44 x 24 390 D x 390 D 204

I6 30 x 30 390 D x 390 D 100

I7 32 x 32 400 D x 400 D 100

I8 40 x 32 370 D x 360 D 120

I9 36 x 32 234 D x 234 D 66

I10 40 x 36 270 D x 261 D 90

I11 36 x 40 95 D x 185 D 45

I12 68 x 44 185 D x 77 D 66

Coated Fabrics for Composites – The fabric was manufactured at SSI Unit Vapi and

coating was done at SCET, Surat. All fabric samples were coated using padding mangle.

Curing of samples CC1, CC2 & CC3 was done at 150°C and curing of samples CC4, CC5

& CC6 was dome at 170°C.

Machine Specifications:

– Power Loom

• Speed-130 rpm

TABLE 4.3 : Specifications of Coated Fabrics for Composites


Ne

GSM

CC1(Plain weave without coating) 76 x 56 30 x 30 95

CC2(resin treated 100 gpl) 76 x 56 30 x 30 103


CC4(2/1 twill weave without coating) 64 x 56 8 x 56 303




70

Nonwoven Fabrics:

Spunbond- Manufactured at M/s. Wovlene Tecfab India, A-42/5, Ichchhapore G.I.D.C,

Near GEB Substation, ONGC Road, Hazira, Surat-394510.


– Chinese make spunbond machine -1.6 m width

– Capacity : 5 tonnes/day

– GSM range :10 -200

TABLE 4.4 : Specifications of Spunbond Nonwovens

Sample Name GSM

NS1 40

NS2 60

NS3 60

NS4 60

NS5 60

NS6 85

NS7 120

NS8 135

NS9 60

NS10 60

NS11 80


71

Needle Punched Fabrics – Manufactured at M/s. Autotech Nonwovens, Fairdeal Textile

Park, NH8 Kim.


– Korean make needle punching machine -1.6 m width

– GSM range :above 150

TABLE 4.5 : Specifications of Needle Punched Fabrics

Sample Name GSM

NP1 270

NP2 280

NP3 200

NP4 300

NP5 400

NP6 400

NP7 400

NP8 450

Spun Laced (Hydroentangled) Fabrics – Manufactured at M/s. Ginni Filaments, GIDC,

Panoli.


– Plant Line with capacity of 12000 metric tonnes per annum : Perfojet,

France

– GSM range :40

TABLE 4.6 : Specifications of Spun Laced Fabrics

Sample Name Type GSM

SL1 PV5050P1035 35

SL2 PV5050A2035 35

SL3 PV6535E1050 50

SL4 PV3565P1055 55

SL5 PV406091070 70

SL6 PV406091070 70

SL7 PV3070E1075 75

SL8 PV2080P1100 100


72

4.3.3 Fabric Defects:

Geotextiles: 6 types of common defects obtained while manufacturing of the geotextile

fabrics were identified and considered for the study. The details of the same have been

described in Table 4.7 and illustrated in Figure 4.15

TABLE 4.7 : Identified Defects in Geotextiles

Sr.

No.

Fabric

Defect


1. Missing

End

(Chira)

There may be one end

or a group of ends

missing in the fabric.

If the broken ends are not

mended immediately by the

operator, these missing ends

will occur in the fabric.

This defect can be minimised

(a) by minimising missing

ends in the weaver‘s beam &

(b) by providing an efficient

warp – stop motion on a

loom.

2. Slubs

(Warp)

Thick untwisted

portion in warp yarn

Variation in draft during

spinning.

Set the draft as per the

requirement.

3. Stain

(Daggi)

These stains are due

to lubricants or dust.

Improper material handling,

bad oiling & cleaning practices

By proper material handling

as well as good oiling &

cleaning practices, this defect

can be avoided.

4. Slubs

(Weft)

Thick untwisted

portion in weft yarn

Variation in draft during

spinning.

Set the draft as per the

requirement.

5. Missing

Pick

(Jerky)

It is a strip which

extends across the

width of fabric & has

the pick density lower

than the required one.

It is caused by faulty let – off &

take – up motions. Also, if the

loom is not stopped

immediately in case of weft

break, few picks are liable to be

missed in the fabric.


by proper setting of let – off

& take – up motions & also

by using an efficient brake –

motion.

6. Gout Foreign matter woven

in a fabric by

accident. Usually lint

or waste.

It is caused when the hardened

fluff or foreign matter such as

pieces of leather accessories,

pieces of damaged pickers etc.,

is woven into the texture of the

fabric.


by preventing the foreign

matter from falling onto the

warp between the reed & the

fell of the cloth.


73

FIGURE 4.15 : Defects mentioned in Table 4.7

Spunbond Fabrics: 6 types of common defects obtained while manufacturing of the

spunbond fabrics were identified and considered for the study. The details of the same

have been described in Table 4.8 and illustrated in Figure 4.16


74

TABLE 4.8 : Identified Defects in Spunbond Fabrics

Sr.

No.

Fabric

Defect


1. Drops / bond

point fusion

Fused fibres on

surface

Breaking of bundle of

filaments during the process.

Proper setting of draw ratio.

2. Pinholes Very small

holes in fabric

Damaged surface of delivery

roller.

Filing of surface of roller.

3. Wrinkles Wrinkle

formation

Improper tension across the

width of fabric.

Maintaining uniform tension.

4. Hard

filaments

Fused filaments

on surface

Breaking of filaments during

the process.

Proper setting of draw ratio.

5. Hole Holes in fabric/

web

Improper supply of polymeric

material across the width of

fabric, blockage of spinnerette

holes.

Maintaining proper supply of

polymeric material across the

width of fabric, cleaning of

spinnerrrate.

6. Calendar cut Cut marks due

to calendaring

Rough surface of calendar roll. Polishing of surface of roller.

FIGURE 4.16 : Defects mentioned in Table 4.8

Image Acquisition for the Learning Phase

75

4.4 Image Acquisition for the Learning Phase:

4.4.1 Introduction:

The first step of the Image analysis process is image acquisition of the fabric images. The

texture images of the defective as well as defect free samples of geotextiles and spunbond

fabrics were acquired using the device. Initially the images were captured by providing

illumination or source of light from bottom or top surface of fabric as well as by adjusting

the height of camera with reference to the surface of fabric for optimization of different

parameters leading towards the designing of the developed device. More than 400 images

were captured initially for the extraction of basic image characteristics of the textures of

the defective as well as defect free fabric samples. The images of the geotextile and

spunbond fabrics have been illustrated in section 4.4.2 and 4.4.3 respectively.

4.4.2 Geotextiles:

As mentioned earlier number of images were required to acquire the basic characteristics

of the geotextiles. The actual image acquired of a fabric region free of defects has been

shown in Figure 4.17.

FIGURE 4.17 : Image of Defect Free Sample


76

The actual images of the 6 identified defects in the geotextiles have been shown in the

figures from Figure 4.18 – Figure 4.23 and the details of the images have been shown in

Table 4.9. These images have been used for the study of the defect pattern. Multiple

images of the same region were taken and used for processing during the testing phase.

TABLE 4.9 : Details of the images of the defects in Geotextiles

Sr. No. Defect Name Fabric Sample GSM Image No.

1. Missing End (Chira) G6 210 8

2. Slubs (Warp) G1 160 74

3. Stain (Daggi) G6 210 20

4. Slubs (Weft) G5 290 27

5. Missing Pick (Jerly) G4 210 50

6. Gout (Foreign Matter) G2 120 67

FIGURE 4.18 : Missing End / Chira


77

FIGURE 4.19 : Slub (Warp)

FIGURE 4.20 : Stain (Daggi)


78

FIGURE 4.21 : Slub (Weft)

FIGURE 4.22 : Missing Pick / Jerky


79

FIGURE 4.23 : Gout

4.4.3 Spunbond Fabrics:

As mentioned earlier number of images were required to acquire the basic characteristics

of the spunbond fabrics. The actual image acquired of a fabric region free of defects has

been shown in Figure 4.24.


80

FIGURE 4.24 : Image of Defect Free Sample

As mentioned earlier 6 types of defects had been identified for the learning phase. The

details of the images of the fabrics with defect captured have been shown in the Table 4.10.

The actual images of the 6 identified defects in the spunbond have been shown in the

figures from Figure 4.25 – Figure 4.30. These images have been used for the study of the

defect pattern and its influence in nonwoven fabrics. Multiple images of the same region

were taken and used for processing during the testing phase.

TABLE 4.10 : Details of the images of defects in spunbond fabrics

Sr. No. Defect Name Fabric Sample GSM Image No.

1. Drop/Bond Pt. Fusion NS7 120 27

2. Pin Hole NS6 85 23

3. Wrinkle NS9 60 39

4. Hard Filament NS3 60 5

5. Hole NS5 60 12

6. Calender Cut NS10 60 41


81

FIGURE 4.25 : Drop / Bond Pt. Fusion

FIGURE 4.26 : Pin Hole


82

FIGURE 4.27 : Wrinkle

FIGURE 4.28 : Hard Filament


83

FIGURE 4.29 : Holes

FIGURE 4.30 : Calender Cut


84

4.5 Image Processing Methodology:

4.5.1 Introduction:

The images of the fabrics need to be processed for the extracting their characteristics. As

mentioned earlier, there are limited studies carried out in the area of automated quality

control system for functional fabrics, the biggest task was to decide the best possible

approach and method for the processing of the images. Since the main object of this study

was to develop a simple cost effective quality control system for the functional/nonwoven

fabrics, a combination of various approaches described in Chapter 2, Section 2.9 was used.

Two varieties of fabrics resulting due to completely different manufacturing processes

needed to be studied as a part of the study. The fabric structure of the woven geotextiles

and the spunbond fabrics is completely different. Therefore different algorithms needed to

be used and implemented. The basic steps involved in processing of images were the same

and has been described in the next section.

4.5.2 Steps involved in Processing of Images:

The flowchart shown in Figure 4.31 shows the steps involved in processing of images. The

algorithm for processing of the images of both the fabric samples have been designed as

per the same. A brief discussion about the various steps has been described in this next

section.

Image Processing Methodology

85

FIGURE 4.31 : Steps involved in processing of Images

4.5.3 General Image Parameters of the Images:

All the images were first studied and were processed for obtaining basic characteristics

using gray level conversion, contrast adjustment and studying their histogram.

Gray Level Conversion:

The images acquired are in true colour as can be seen in the images (Figure 4.32 a ) and

they store the information of Red, Blue and Green (RGB) levels for each pixel. The

computation involved with the RGB images is more and they also use more of disk space

and memory. Thus the first step involved in processing is to convert these images into a

gray image. A gray image has only gray level values for each pixel (0-255). In a 8-bit

system the image display can show a maximum of 256 gray levels. Figure 4.32 shows a

RGB image and grayscale converted image. We have used a simple graylevel conversion

approach.


86

FIGURE 4.32 : RGB Image (a) & Grayscale converted Image (b) of spunbond

nonwoven fabric

Histogram:

Histogram of an image is basically a graphical representation of the intensity distribution

of the grayscale image. It plots the number of pixels for each gray level value. The tonal

distribution within the image can be judged by the histogram and therefore it serves as an

important tool for analysing the basic characteristics of fabric images. Figure 4.33 shows

the histogram plot of defective and defect free region of a fabric. The intensity values

ranges from 0-255 i.e dark to light for an image in 8-bit mode. The range and intensity

distribution of all the images for both the fabrics were studied from the histogram. The

multiple peaks in the histogram (b) shows the presence of defects in the image. Also it

indicates more values of intensity towards the lighter side and this information may be

used to determine the nature of the defect.


87

FIGURE 4.33 : (a) Histogram of a defect free region of spunbond fabric; (b)

histogram of a defective region of spunbond fabric.

Contrast Adjustment:

The contrast of an image is basically a measure of its dynamic range, or the "spread" of its

histogram. As seen from Figure 4.34(a), a grayscale image needs to be enhanced,

especially to find any regions different from the background. Therefore contrast

enhancements are needed to be done. They are typically performed as a contrast stretch

followed by a total enhancement, although these could both be performed in one step.

Different approaches may be used for contrast adjustment: contrast stretch and adaptive

contrast stretch.

A contrast stretch improves the brightness differences uniformly across the dynamic range

of the image.


88

In adaptive contrast stretch the peaks are located in the histogram and then marching in

both the directions until only a small number of pixel intensities are rejected.

Both the methods were tried on for the images of geotextiles and spunbond fabrics. Based

on the nature of histogram, adaptive contrast stretch was found suitable on spunbond

nonwoven fabrics. Figure 4.34 shows the captured original image and contrast adjustment

image along with their respective histogram.

FIGURE 4.34 : Grayscale Image with histogram (a) & Contrast Adjusted Image with

histogram (b) of spunbond nonwoven fabric

Noise removal:

Noise in an image is a random variation of the colour information in the images and is

resulted due to the errors of limitations of the image acquisition process. It results in image

pixel values that may not be the true intensity of the real scene considered. The noise in an

image is reduced by using an appropriate filtering technique. A filtering technique is also

an enhancement of an image by emphasizing or removing certain features. Linear, median

and adaptive filtering are the common methods used. Since the filtering technique involves


89

emphasizing or removing certain features, there are chances of losing important image

information.

Figure 4.35 shows a grayscale and filtered image of spunbond fabrics. It can be seen that

the gray image has considerable noise which is removed in the processed image, but at the

same time in the spunbond fabrics due to their random fibrous structure, important

structural information may also be removed and therefore only contrast enhancement of the

images was done.

FIGURE 4.35. Grayscale Image (a) & Filtered Image (b) of spunbond nonwoven

fabric

A lot of noise was found in the geotextile fabric (Figure 4.36a) owing to the highly lustrous

surface texture of the same. So the noise removal in the fabric was an important task. We

used 2 step filtering method. Firstly a 2D Gaussian filtering of images was done to remove

the general noise in the image (Figure 4.36b). In the second step, another filtering

operation was done (Figure 4.37c). The pixel weight for each pixel in the image was

calculated based on the gradient magnitude at the pixel and was replaced by the same. The

gradient magnitude is influenced by the geometric closeness and photometric similarity

within the image and it reflects to edge and texture directly. Pixels in smooth regions i.e

small gradient magnitude pixels have large weight while those with large gradient

magnitude i.e the edge regions have small weight. Thus a high contrast is applied and

therefore it has better results in differentiating the influence of noise and the influence of

defect. Even the weak edges and minute details of the input image are preserved, while the

actual noise may be removed.


90

FIGURE 4.36 : Grayscale Image (a) & Filtered Image (b & c) of woven geotextile

fabric

4.5.4 Methodology for Defect Detection:

Histogram analysis during the learning stage showed that the presence of major defects

could be easily detected, however the minor defects especially the one whose intensity

levels are almost same as the structure itself or the local defects could not be identified.

The defects in the fabric in terms of image representation can be basically characterised

into two types depending upon it‘s appearance in the texture images:

defects with intensities lower than the mean intensity in an image - pin hole,

wrinkles, hard filaments, holes, calendar cut

defects with intensities higher than the mean intensity in an image - drop/bond

point fusion, stains.

After the images were studied for general parameters, the images with defect were then

processed. The images were processed using gray level conversion, contrast enhancement

and noise was removed. The images were then converted to binary images using the


91

optimum threshold for each defect. The binary images were further processed for finding

the region of interest i.e. the defects using the morphological operations and finally after

segmenting the region of interest the statistics of these regions were studied to classify the

defects.

Thresholding:

Once the images are properly enhanced in a manner that the important information of the

images is not eliminated, the next step is to replace the gray level intensity into binary

values. Thresholding is the process of converting a gray scale image into binary image by

replacing the predetermined gray level intensity values to 0 or 1. In simple words,

thresholding methods replace each pixel in an image with a black pixel if the image

intensity I(i,j) is less than some fixed constant T (that is, I(i,j) < T), or a white pixel if the

image intensity is greater than that constant. Simple or multilevel thresholding methods

are commonly used for determining the value of T. Determining of T is an important task

since it will decide the information about the pixels to be further processed.

The filtering and noise removal process in the images of the woven geotextiles enhanced

the difference between the defective and non defective part in the images and so converting

the enhanced image into a binary image by selecting an appropriate threshold was found

suitable to extract the defective regions in the images.

The spunbond nonwoven fabrics are characterised by thick and thin places, and also the

images could not be processed for noise removal, so deciding of common threshold for

defect extraction was found difficult. Therefore different thresholds were used for

extracting the extreme light and extreme dark regions.

Figure 4.37 shows a grayscale and binary image of a spunbond fabric after performing

simple thresholding.


92

FIGURE 4.37 : Grayscale Image (a) & Binary Image (b) of spunbond nonwoven

fabric

Morphological Operations:

The binary images obtained by simple thresholding are normally the distorted ones, the

ones influenced by noised and texture as can be seen in Figure 4.38. The morphological

operations thus intend to correct these errors in the image so that images are more

accountable. These operations can also be applied to grayscale images. They include

operations like erosion and dilation in the images considering the corresponding

neighbourhood pixels and work in accordance with structuring element (SE). SE is a small

binary image; a small matrix of pixels and basically has the same role as convolution

kernels in linear image filtering. As can be seen in the Figure 4.38b, the morphological

operations allow to add or to remove pixels from the corresponding neighbourhood.

FIGURE 4.38 : Binary Image (a) & Dilated Binary Image (b) of geotextile woven

fabric


93

Feature Extraction:

A feature is basically the "interesting" part of an image or the region of interest in an

image, and these features are used as a starting point for many computer vision algorithms

in computer vision and image processing. The concept of feature detection refers to

methods that aim at computing abstractions of image information and making local

decisions at every image point whether there is an image feature of a given type at that

point or not. The resulting features will be subsets of the image domain, often in the form

of isolated points, continuous curves or connected regions. Edges, corners, blobs (regions

of interest) and ridges are the common types of features.

Once the desired binary image free from noise and background is obtained, the regions of

interest (defects) if any need to be identified and studied. The techniques involved in the

feature detection and extraction have been used for the same.

After the detection of these features, necessary attributes, such as the edge orientation,

edge detection, statistical features, shape and other parameters can be obtained and

computed as per requirement.

Common approach was found suitable for both the varieties of fabrics.

The connected components in the binary images were found out, which are known as

objects or blob. Each blob has it‘s own statistics which were found out. Stepwise

elimination of these blobs was done to identify the defective regions. The blobs with

minimum area which cannot be termed as defect were identified and eliminated in the first

step. In the second step the features of the remaining blobs were obtained and the general

parameters for the defects were calculated. The same were compared with the statistics

obtained by manual - visual examination for validation and classification.


94

4.5.5 Software Used:

Our proposed algorithm, technique and their all optimizations were accomplished during

this study by implementing several Matlab scripts (Appendixes B).

4.5.6 Stages of Implementation:

For the final implementation of the image processing techniques and the system, the

system needed to pass through the learning and the testing or validation.

Learning Stage:

It is known also as the training phase. It involves study or inspection of the fabric images

with no defects or may also be called as the standard images. More than 200 images of

both the fabric samples were captured. The main object during this phase is to calculate the

important parameters of these images like mean intensity of the images, study of histogram

and it‘s properties. These values were used to determine the image processing parameters

for the detect detection.

After the learning of the basic parameters of the images free from defect, images of fabrics

with defects were processed for learning the pattern of each type of defect. The processing

algorithm was designed by optimising various parameters at each of the above mentioned

stages of algorithm like contrast enhancement, thresholding, etc.

Testing & Validation Stage:

The designed algorithm for the optimum defect detection was capable to identify regions

with defects but it needed to be tested. The testing was done basically by three means:

The area, shape and size of the defects were extracted using the algorithm which was

compared with the visual examination of the defects. Based on the defect statistics, the

grading of the defects was done.

Multiple images of the same defect regions were captured and processed to check for

any variation in the definition and grading of defects.

Fabric Grading

95

Regions of the fabric other than those studied for defect detection were captured and

processed to grade the same.

4.6 Fabric Grading:

On the basis of the defect parameters obtained as result of the processing of images of the

fabric lot & considering the proposed classification of defect, a fabric grading system was

developed. The Defect Classification is shown in Table 4.11

TABLE 4.11 : Defect Classification

Defect Name (DN) Woven Geotextile Spunbond Nonwoven

Missing End (Chira) Drops / bond point fusion

Slubs (Warp) Pinholes

Stain (Daggi) Wrinkles

Slubs (Weft) Hard filaments

Missing Pick (Jerky) Holes

Gout Calendar cut

Defect Size (DS) Mendable- 10 % Defective Area

Permissible- 30% Defective Area

Critical - 60% Defective Area

Rejected - 80% Defective Area

Defect Frequency (DF) Frequency of occurrence of defects

Defect Orientation (DO) Machine Direction/Warp Way

Cross Direction/Weft Way


96

The Proposed Grading System is shown below:

Grade of Fabric Proposed Performance of Fabric

A (Best) The defect has no or very negligible influence, the fabric can thus be

used for suggested applications.

DN - All

DS – Mendeable (10% Defective Area/upto 3‖ length of defect)

DF – 1- within an image frame, 10% of the total images for the fabric

roll.

DO - any

B (Good) Substandard applications of suggested areas are possible with this

grade of fabrics.

DN - All

DS – Permissible (30% Defective Area/ 3‖-6‖ length of defect)

DF – 1- within an image frame, 30% of the total images for the fabric

roll.

DO - any

C (Poor) Can be considered after repairing or taking preventive measures for

suggested areas of applications.

DN - All

DS – Critical (60% Defective Area/ upto 6‖-9‖ length of defect)

DF – more than 1 within an image frame, 60% of the total images for

the fabric roll.

DO – any

D (Rejected) Not to be considered for any suggested applications.

DN - All

DS – Rejected (80% Defective Area more than 9‘‘ length of defect)

DF – more than 1 within an image frame, 80% of the total images for

the fabric roll.

DO - any

97

CHAPTER – 5

Results and Discussions

5.1 Introduction:

In this chapter we present the results of the various trails conducted during the learning and the

testing phase for the implementation, optimisation and validation of the designed algorithm as

well as an effective quality control system for the geotextiles as well as spunbond nonwovens.

For designing a suitable algorithm for defect detection, the optimization of various image

processing parameters was needed to be done. The images were processed by taking trials

with different values and combinations using Matlab script. Various direct and indirect

parameters were studied for obtaining the optimum algorithm and the results for the

significant ones have been presented here. The main parameters which were to be

optimised were deciding the value of threshold, value of the structuring element.

A vast number of images of both the fabrics had been studied during each phase. Since it is

difficult and insignificant to display the results of all the images, only the results having

significant contribution for implementation, optimisation and validation during the learning

and the testing phase have been tabulated here.

Results and Discussion

98

5.2 General Image Parameters of the Images:

This section shows results of the general image parameters of the fabric images of

defective as well as defect free regions. As mentioned earlier vast number of images were

acquired and processed for extracting the basic characteristics of the fabrics. The average

values for unprocessed and enhanced images of each variety of geotextiles as well as

spunbond fabrics were obtained and the readings of the defects of the same have been

tabulated in Section 5.2.1 and Section 5.2.2 respectively.

5.2.1 Woven Geotextiles:

The images of defect free regions of geotextiles were processed and the results have been

tabulated in this section. The images of regions with defects also had been processed to

obtain the basic intensity distribution for comparison with the defect free ones.

TABLE 5.1 : Sample wise average values of Image parameters for unprocessed


Sample

Name

GSM Mean

Intensity

SD

(variation in

intensity)

Threshold

Average

Maximum

Intensity

Average

Minimum

Intensity

G1 160 123 29 0.48 205 29

G2 120 123 30 0.48 206 37

G3 215 127 30 0.49 205 45

G4 210 126 30 0.49 204 44

G5 290 118 35 0.48 216 40

G6 210 123 30 0.48 216 46

G7 220 118 41 0.46 237 15

General Parameters of the Images

99

TABLE 5.2 : Sample wise average values of Image parameters for enhanced images

of each type of Geotextile.

Sample

Name

GSM Mean

Intensity

SD

(variation in

intensity)

Threshold

Average

Maximum

Intensity

Average

Minimum

Intensity

G1 160 0.42 0.1 0.44 0.77 0.1

G2 120 0.26 0.17 0.12 0.69 0

G3 215 0.28 0.16 0.12 0.71 0

G4 210 0.31 0.15 0.12 0.71 0

G5 290 0.04 0.10 0.12 0.52 0

G6 210 0.33 0.14 0.12 0.72 0

G7 220 0.32 0.15 0.12 0.71 0

FIGURE 5.1 : Comparison of the Mean Intensity Values between the unprocessed


FIGURE 5.2 : Comparison of the Mean Intensity Values between the enhanced


The mean intensity of the unprocessed images was found to be almost same for different

types of fabrics. Minor variations in the mean value of intensity can be mainly accounted

110

115

120

125

130

160 120 215 210 290 210 220

G1 G2 G3 G4 G5 G6 G7

Me

an In

ten

sity

Fabric Sample & GSM

Mean Intensity (unprocessed Images)

Mean

0

0.1

0.2

0.3

0.4

0.5

160 120 215 210 290 210 220

G1 G2 G3 G4 G5 G6 G7

Me

an In

ten

sity

Fabric Sample & GSM

Mean Intensity (enhanced Images)

Mean


100

due to noise in the images on account of errors in image acquisition. After processing of

the images, it was found that the mean intensity of sample G5(290 GSM) with maximum

GSM was very less.

TABLE 5.3 : Defect wise values of Image parameters for unprocessed images of


Sr. No. Defect

Name

Fabric

Sample

GSM Mean

Intensity

SD

(variation

in

intensity)

Threshold

Average

Maximum

Intensity

Average

Minimum

Intensity

1. Missing

End

(Chira)

G6 210 128 31 0.51 225 45

2. Slubs

(warp)

G1 160 121 22 0.49 185 50

3. Stain G6 210 126 31 0.5 219 49

4. Slubs

(weft)

G5 290 126 41 0.52 244 33

5. Missing

Pick

(jerky)

G4 210 123 36 0.49 236 27

6. gout G2 120 125 29 0.49 212 37

TABLE 5.4 : Defect wise values of Image parameters for enhanced images of


Sr. No. Defect

Name

Fabric

Sample

GSM Mean

Intensity

SD

(variation

in

intensity)

Threshold

Average

Maximum

Intensity

Average

Minimum

Intensity

1. Missing

End

(Chira)

G6 210 0.31 0.15 0.12 0.72 0

2. Slubs

(warp)

G1 160 0.4 0.13 0.12 0.82 0

3. Stain G6 210 0.33 0.15 0.12 0.72 0 4. Slubs

(weft)

G5 290 0.08 0.14 0.12 0.6 0

5. Missing

Pick

(jerky)

G4 210 0.38 0.15 0.12 0.75 0

6. Gout G2 120 0.29 0.16 0.12 0.7 0


101




Images of the Defects & general Fabric in Geotextiles

112 114 116 118 120 122 124 126 128 130

210 160 210 290 210 120

G6 G1 G6 G5 G4 G2

Missing End

(Chira)

Slubs (warp)

Stain Slubs (weft)

Missing Pick

(jerky)

gout

Me

an In

ten

sity

Fabric GSM & Defect Type

Mean Intensity (Defect)

Mean Intensity (General)

Mean (Defect)

Mean (General)

0 0.05

0.1 0.15

0.2 0.25

0.3 0.35

0.4 0.45

210 160 210 290 210 120

G6 G1 G6 G5 G4 G2

Missing End

(Chira)

Slubs (warp)

Stain Slubs (weft)

Missing Pick

(jerky)

gout

Me

an In

tesn

tiy




Mean (Defect)

Mean (General)


102





116

118

120

122

124

126

128

130

210 160 210 290 210 120

G6 G1 G6 G5 G4 G2

Missing End

(Chira)

Slubs (warp)

Stain Slubs (weft)

Missing Pick

(jerky)

gout

Me

an In

ten

sity

Defect

Mean Intensity

Mean

0 0.05

0.1 0.15

0.2 0.25

0.3 0.35

0.4 0.45

210 160 210 290 210 120

G6 G1 G6 G5 G4 G2

Missing End

(Chira)

Slubs (warp)

Stain Slubs (weft)

Missing Pick

(jerky)

gout

Me

an In

ten

sity

Defect

Mean Intensity

Mean


103

It can be seen from Tables 5.1 - 5.4, Figure 5.4 & Figure 5.5, that no significant difference

was found between the basic image parameters between the images of defect free and

defective regions. Also no significant information was obtained for further processing of

images for defect detection.

5.2.2 Spunbond Nonwovens:

The images of defect free regions of spunbond nonwovens were processed and the results

have been tabulated in this section. The images of regions with defects also had been

processed to obtain the basic intensity distribution for comparison with the defect free

ones.

TABLE 5.5 : Sample wise average values of Image parameters for unprocessed

images of each type of Spunbond Nonwoven.

Sample

Name

GSM Mean

Intensity

SD

(variation in

intensity)

Threshold

Average

Maximum

Intensity

Average

Minimum

Intensity

NS1 40 118 20 0.48 197 73

NS2 60 127 21 0.50 196 32

NS3 60 123 27 0.48 193 38

NS4 60 121 19 0.47 171 68

NS5 60 118 33 0.50 233 45

NS6 85 118 27 0.47 207 47

NS7 120 120 25 0.46 183 49

NS8 135 127 22 0.49 174 61

NS9 60 126 25 0.49 185 41

NS10 60 123 23 0.48 180 53

NS11 80 122 20 0.47 163 74


104

TABLE 5.6 : Sample wise average values of Image parameters for enhanced images

of each type of Spunbond Nonwoven.

Sample

Name

GSM Mean

Intensity

SD

(variation in

intensity)

Threshold

Average

Maximum

Intensity

Average

Minimum

Intensity

NS1 40 123 40 0.50 236 30

NS2 60 133 52 0.47 241 10

NS3 60 130 51 0.48 237 13

NS4 60 130 34 0.49 212 37

NS5 60 126 67 0.50 253 4

NS6 85 128 54 0.48 245 13

NS7 120 130 66 0.49 250 7

NS8 135 138 43 0.48 223 27

NS9 60 132 51 0.49 216 13

NS10 60 130 47 0.49 230 21

NS11 80 128 32 0.50 194 49





110

115

120

125

130

40 60 60 60 60 85 120 135 60 60 80

NS1 NS2 NS3 NS4 NS5 NS6 NS7 NS8 NS9 NS10 NS11

Me

an In

ten

sity

Fabiric Sample & GSM

Mean Intensity

Mean

115

120

125

130

135

140

40 60 60 60 60 85 120 135 60 60 80

NS1 NS2 NS3 NS4 NS5 NS6 NS7 NS8 NS9 NS10 NS11

Me

an In

ten

sity

Fabric Sample & GSM

Mean Intensity

Mean


105

It can be seen from the Figures 5.7 & 5.8 that there is some difference between the values

of each type of fabric, and the same might influence the thresholding value. The higher

GSM fabric had maximum mean intensity while the lower GSM had minimum mean

intensity, but the other fabrics exhibited almost same mean intensity value. The variation in

the intensity levels in the images can be accounted due to the basic fibrous structure of the

nonwovens. Again here also no significant difference found between the images for

different gsm fabrics. The contrast enhancement in the images increase the range of the

high and low level intensity values giving a considerable improvement in the visual

appearance of the images.

TABLE 5.7 : Defect wise values of Image parameters for unprocessed images of

identified defects in spunbond nonwovens.

Sr. No. Defect

Name

Fabric

Sample

GSM Mean

Intensity

SD

(variation

in

intensity)

Threshold

Average

Maximu

m

Intensity

Average

Minimu

m

Intensity

1. Drop/

Bond Pt.

Fusion

NS7 120 119 24 0.47 183 49

2. Pin Hole NS6 85 117 25 0.46 196 50

3. Wrinkle NS9 60 129 27 0.5 198 43

4. Hard

Filament

NS3 60 125 24 0.48 187 39

5. Hole NS5 60 117 35 0.53 177 66

6. Calender

Cut

NS10 60 120 22 0.47 175 62

7. Thin

Spots

NS4 60 120 18 0.46 165 68


106

TABLE 5.8 : Defect wise values of Image parameters for enhanced images of

identified defects in spunbond nonwovens.

Sr. No. Defect

Name

Fabric

Sample

GSM Mean

Intensity

SD

(variation

in

intensity)

Threshold

Average

Maximu

m

Intensity

Average

Minimu

m

Intensity

1. Drop/

Bond Pt.

Fusion

NS7 120 126 58 0.5 247 8

2. Pin Hole NS6 85 131 55 0.47 242 15

3. Wrinkle NS9 60 131 54 0.49 243 11

4. Hard

Filament

NS3 60 131 52 0.48 238 12

5. Hole NS5 60 120 43 0.5 214 34

6. Calender

Cut

NS10 60 128 40 0.49 223 31

7. Thin

Spots

NS4 60 129 32 0.48 207 40


Images of Defects & general Fabric in Spunbond

110 112 114 116 118 120 122 124 126 128 130

120 85 60 60 60 60 60

NS7 NS6 NS9 NS3 NS5 NS10 NS4

Drop/ Bond Pt. Fusion

Pin Hole Wrinkle Hard Filament

Hole Calender Cut

Thin Spots

Me

an Im

ten

sity


Mean Intensity (Defect) Mean Intensity (General) Mean (Defect)

Mean (General)


107


Images of the Defects & general Fabric in Spunbond



114 116 118 120 122 124 126 128 130 132 134

120 85 60 60 60 60 60




Hole Calender Cut

Thin Spots

Me

an In

ten

sity




Mean (Defect)

Mean (General)

110 112 114 116 118 120 122 124 126 128 130

120 85 60 60 60 60 60




Hole Calender Cut

Thin Spots

Me

an In

ten

sity

Defect

Mean Intensity

Mean


108



Similar to the case of the woven geotextiles, no significant difference found from the basic

image parameters between the images of defect free and defective regions. Difference in

the Mean Intensity Values can be seen between major and minor defects.

5.3 Histogram Analysis:

Study of histogram was done in both the fabrics to analyse the distribution of intensities

within the fabrics. The results along with discussions for geotextiles and spunbond fabrics

have been described in Section 5.3.1 and Section 5.3.2 respectively.


Histograms of a large amount of images for all varieties of geotextile fabrics were studied.

The histograms of some of the images of fabrics void of defects have been shown in Figure

5.13. The histogram of the defective fabric images were also studied and have been shown

from Figure 5.14 to Figure 5.19.

114 116 118 120 122 124 126 128 130 132

120 85 60 60 60 60 60




Hole Calender Cut

Thin Spots

Me

an In

ten

sity

Defect

Mean Intensity

Mean

Histogram Analysis

109

FIGURE 5.13 : Histogram of Images of some samples of defect free images of

Geotextiles.

The histogram of the images show a single peak and since it is a woven fabric, the

distribution of the intensity is symmetric about the mean intensity. Also higher gsm fabric

shows a broader peak.

FIGURE 5.14 : Histogram of defect free region and Missing End (Chira)


110

The presence of missing end accounts to the deviation of the shape of histogram from the

defect free image.

FIGURE 5.15 : Histogram of defect free region and Slubs (Warp)

The presence of slubs accounts for more pixels at the peak.

FIGURE 5.16 : Histogram of defect free region and Stain (Daggi)

The presence of stain accounts to the deviation of the shape of histogram from the defect

free image. The shape is similar to the histogram of missing end (Figure 5.2). This is

because both the defects are in the same sample. Thus we can conclude that the histogram

shape is not affected by the type of defect.

Histogram Analysis

111

FIGURE 5.17 : Histogram of defect free region and Slubs (Weft)

The shape of the histogram of the defect is higher towards the low intensity and accounts

for the presence of defect.

FIGURE 5.18 : Histogram of defect free region and Missing Pick (Jerky)

Similar to the above figure, here also the presence of defect is reflected by more no of low

intensity pixels or a higher spread of intensities of pixels.


112

FIGURE 5.19 : Histogram of defect free region and Gout

The presence of gouts just like slubs(warp) accounts for more pixels at the peak.

Histogram analysis shows that presence of defects could be largely detected from the shape

of histogram. The threshold value was therefore easy to be decided from the histogram.

5.3.2 Spunbond Nonwoven:

Histograms of a large amount of images for all varieties of spunbond fabrics were studied

like geotextiles. The histograms of some of the images of fabrics void of defects have been

shown in Figure 5.20.

Histogram Analysis

113

FIGURE 5.20: Histogram of Images of some samples of defect free samples of

Spunbond Nonwoven

The images of fabrics void of defects show a single peak. Also it can be seen that the shape

and pattern of intensity distribution of each sample varies. Also the histograms are not

symmetric around the mean intensity values like the woven geotextiles. The defect wise

histogram of the images of the fabrics with defects and with it‘s corresponding defect free

fabric image for each defect have been shown in from Figure 5.21 to Figure 5.26.


114

FIGURE 5.21: Histogram of defect free region and Drop / Bond Point Fusion

Variations in the histogram indicate presence of defect.

FIGURE 5.22: Histogram of defect free region and Pinhole

Very minute variation in the histogram indicates presence of minor defect.

Histogram Analysis

115

FIGURE 5.23: Histogram of defect free region and Wrinkle


FIGURE 5.24: Histogram of defect free region and Hard Filament

The region having this defect falls near the higher intensity region.


116

FIGURE 5.25: Histogram of defect free region and Hole

Prominent variation in the histograms can be seen, especially the multiple peaks concludes

presence of major defects.

FIGURE 5.26: Histogram of defect free region and Calender cut


Quite similar intensity distribution cane be seen concluding very minor defect.

Histogram analysis shows that presence of major defects could be easily detected, however

the minor defects especially the one whose intensity levels are almost same as the structure

itself could not be identified. The deviation of skewness in histograms of defective images

from the defect free images is useful in determining the threshold for further processing of

Histogram Analysis

117

the image. It was seen that the defects could be broadly categorised as the ones having

more skewness deviation towards dark intensity (drop/bond point fusion, stains) and the

ones having more skewness deviation towards light intensity (pin hole, wrinkles, hard

filaments, holes, calendar cut). However, a common threshold for all fabric images and

defects could not be obtained.

5.4 Thresholding:


From the mean intensity values of the processed images of the geotextile fabrics, it was

found that G5 fabric exhibited a very less mean intensity and so a different threshold(-0.2)

was required for it, while all the other fabrics a common threshold of 0.2 was found

suitable.

5.4.2 Spunbond Nonwoven:

Since the nonwovens are characterised by thin and thick regions, different thresholds were

used to obtain regions with the extreme light and dark regions. Threshold Intensity for dark

regions was 100 and for light regions was 180 used. All pixels with intensity below 100

were eliminated to obtain the extreme dark regions and all pixels with intensity above 180

were eliminated to obtain the extreme light regions.

5.5 Defect Detection & Validation:

5.5.1 Defect Detection in Images of Woven Geotextiles:

The images of the regions with defects were processed using the method as described in

Chapter 4, Section 4.5.2.2. The visual results of the grayscale image, binary image after

thresholding and the image with highlighted defective regions for each defect have been

shown in the Figures from Figure 5.27 – 5.44.


118

FIGURE 5.27 : Grayscale Image - Missing End

FIGURE 5.28 : Binary Image - Missing End

Defect Detection & Validation

119

FIGURE 5.29 : Highlighted Missing End

FIGURE 5.30 : Grayscale Image-Slub (Warp)


120

FIGURE 5.31 : Binary Image-Slub (Warp)

FIGURE 5.32 : Highlighted Slub (Warp)


121

FIGURE 5.33 : Grayscale Image - Stain (Daggi)

FIGURE 5.34: Binary Image - Stain (Daggi)


122

FIGURE 5.35 : Highlighted Stain (Daggi)

FIGURE 5.36 : Grayscale Image –Slub (weft)


123

FIGURE 5.37: Binary Image –Slub (weft)

FIGURE 5.38 : Highlighted Slub (weft)


124

FIGURE 5.39 : Grayscale Image – Missing Pick (jerky)

FIGURE 5.40 : Binary Image – Missing Pick (jerky)


125

FIGURE 5.41 : Highlighted Missing Pick (jerky)

FIGURE 5.42 : Grayscale – Gout


126

FIGURE 5.43 : Binary Image – Gout

FIGURE 5.44 : Highlighted Gout


127

It is evident from the processed images, that the algorithm was able to extract the defective

areas from the unprocessed images. It was found that in Figure 5.38, some non defective

regions also had been highlighted giving a false alarm of defect. The same had been

eliminated by using the statistics of these regions while grading the image. The area,

length/width and the orientation of the identified defects were calculated and the same has

been tabulated in Table 5.9. The values were compared with the values obtained by

manual - visual examination of the defects and the same have been tabulated in Table 5.10.

TABLE 5.9 : Defect Statistics obtained from the System

Sr.

No.

Defect Name Fabric

Sample

GSM Defect Area

(sq. inch)

Length/Width of

Biggest Defect

(inch)

Defect

Orientation

1 Missing End

(Chira)

G6 210 0.162778 2.735635 Warp-line

2 Slubs (warp) G1 160 0.178667 1.536842 Warp-line 3 Stain G6 210 0.672667 5.402169 Warp-line 4 Slubs (weft) G5 290 0.079222 0.940033 Weft-line

5 Missing Pick

(jerky)

G4 210 0.650333 5.195384 Weft-line

6 Gout G2 120 0.137333 0.768245 circular

TABLE 5.10 : Defect Statistics obtained from Manual - visual Examination

Sr.

No.

Defect Name Fabric

Sample

GSM Defect Area

(sq. inch)

Length/Width of

Biggest Defect

(inch)

Defect

Orientation

1 Missing End

(Chira)

G6 210 0.33 5 Warp-line

2 Slubs (warp) G1 160 0.17 1.7 Warp-line

3 Stain G6 210 0.85 5 Warp-line

4 Slubs (weft) G5 290 0.15 1.18 Weft-line

5 Missing Pick

(jerky)

G4 210 3.06 6.67 Weft-line

6 Gout G2 120 0.05 0.32 circular


128

FIGURE 5.45 : Comparison of Defect Area obtained from the System with those


FIGURE 5.46 : Comparison of Length/Width of Biggest Defect obtained from the

System with those obtained from Manual - visual Examination

0

0.5

1

1.5

2

2.5

3

3.5

Missing End (Chira)

Slubs (warp)

Stain Slubs (weft) Missing Pick (jerky)

Gout

De

fect

Are

a (S

q.

inch

)

Defect Type

Manual

Automatic

0

1

2

3

4

5

6

7

8

Missing End (Chira)

Slubs (warp) Stain Slubs (weft) Missing Pick (jerky)

Gout Len

gth

/Wid

th o

f B

igge

st D

efe

ct (

Inch

)

Defect Type

Manual

Automatic


129

It can be seen from Table 5.9 & Table 5.10 that the defect statistics obtained from the

system are similar to the ones obtained from manual - visual examination. It can be seen

from the Figures 5.45 that there is considerable difference between the area obtained in

Missing Pick, however less difference can be seen between the width of the defect, which

might be due to non uniformity in the intensity of the defect.

5.5.2 Validation of Results of Geotextiles:

5.5.2.1 Grading of Geotextiles:

Since the defects were a result of the manufacturing process, most of the images had

multiple defective regions. Therefore, grading of the images was done based on the

length/width & the number of these defective areas and also considering the severity of the

defects as done in the manual - visual grading system for the woven fabrics. The manual -

visual grading of the fabrics with identified defective regions is shown in Table 5.11. The

total defective area present in the image, percentage defective area, length/width of the

biggest defect in the image, total number of objectionable defects & grading of the image

have been calculated using the algorithm and the same has been tabulated in Table 5.12.

TABLE 5.11 : Manual Grading of the Defects in Geotextile Fabrics

Image. No. Main Defect Present Fabric Sample GSM Manual - visual Overall Grading

1 Missing End (Chira) G6 210 Major

2 Slubs (warp) G1 160 Major

3 Stain G6 210 Major

4 Slubs (weft) G5 290 Minor

5 Missing Pick (jerky) G4 210 Major

6 Gout G2 120 Major


130

TABLE 5.12 : Grading of Defects in Geotextile Fabrics using the System

Image.

No.

Main

Defect

Present

Fabric

Sample

GSM Total

Defective

Area

(sq inch)

Total

Percentage

Defective Area

Length / Width of

Biggest Defect

(inch)

No. of

Defects

Overall

Grade

1 Missing

End

(Chira)

G6 210 1.9632 5.7516 3.2622 3 C

2 Slubs

(warp)

G1 160 3.2136 9.4147 1.8039 9 D

3 Stain G6 210 1.923 5.6338 3.0752 5 C

4 Slubs

(weft)

G5 290 20.3193 59.5293 2.818 2 A

5 Missing

Pick

(jerky)

G4 210 2.4261 7.1077 1.3019 6 C

6 Gout G2 120 2.3562 6.903 1.4938 6 C

TABLE 5.13 : Comparison between the Grading of Woven Geotextile Images

obtained by Manual - visual Examination & System

Image.

No.

Main Defect Present Fabric Sample GSM Manual - visual Overall

Grading

Overall

Grade using

System

1 Missing End (Chira) G6 210 Major C

2 Slubs (warp) G1 160 Major D

3 Stain G6 210 Major C

4 Slubs (weft) G5 290 Minor A

5 Missing Pick (jerky) G4 210 Major C

6 Gout G2 120 Major C

Table 5.13 shows that the grading system used for classification as suggested in Chapter 4,

Section 4.6 is comparable with manual - visual grading major and minor defects


131

5.5.2.2 Grading of Multiple Images of Geotextiles with same defect:

The results obtained as above were validated by capturing multiple images of same fabric

regions. 5 images were taken of the same regions to check for variability in the length or

width of the biggest defect and the grading obtained as per the algorithm. The results are

tabulated in Table 5.13 and Figure 5.35 show the variability of the order of only 5-10 %

which is considered to be negligible.

TABLE 5.14 : Grading of Multiple Images of Regions with same defect

Image.

No.

Main Defect

Present

Fabric

Sample

GSM Length / Width of Biggest

Defect

(inch)

Grading as per

System

I1 I2 I3 I4 I5 I1 I2 I3 I4 I5

1 Missing End

(Chira)

G6 210 3.26 3.20 2.02 1.92 3.27 C C C C C

2 Slubs (warp) G1 160 1.80 1.82 1.82 1.80 1.82 D D D D D

3 Stain G6 210 3.08 5.48 3.20 5.75 5.57 C C C C C

4 Slubs (weft) G5 290 2.82 2.70 3.59 3.36 1.98 A A A C C

5 Missing Pick

(jerky)

G4 210 1.30 1.21 1.20 1.23 1.31 C C A C C

6 Gout G2 120 1.49 1.50 1.10 2.38 1.58 C C C C C

FIGURE 5.47 : Comparison of Multiple Images of Regions with same defect

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

Missing End (Chira)

Slubs(warp) Stain Slubs (weft) Missing Pick(jerky)

gout

Len

gth

/ W

idth

of

Big

ges

t D

efec

t (i

nch

)

Defect Type

I1

I2

I3

I4

I5


132

It can be seen from Figure 5.47, that there is some variation in the length of the maximum

defect sensed in the line defects in the warp direction. It is due to the fact that most of the

times the continuity of the line defects is not maintained while thresholding and results in 2

3 breakages in line as can be seen from Figure 5.28. As a result of this, the software

interprets 2 or 3 defects (objects) instead of one. But by considering the number of these

kind of defects (objects) along with the length/width results in obtaining the desired

grading of the Defects as can be seen from Table 5.14.

5.5.2.3 Grading of other Geotextile Images than the Studied Ones:

The developed algorithm was thus found reliable and was tested for a number of images of

other fabric regions. Some of the images with highlighted defective regions and the

grading so obtained have been shown in Figures 5.48 – Figures 5.57.

FIGURE 5.48 : Test Image 1


133




134




135




136




137


The results show that the algorithm is able to detect the defective regions and the results

show the variability of the order of only 5-10 % which is considered to be negligible.

TABLE 5.15 : Comparison between the Grading achieved with developed System as

against Manual - visual grading

Test Image Grading As Per Software Manual - visual Grading

1 C Major – Missing End

2 C Major – Missing Ends

3 C Major – Missing End & Stretch Marks

4 C Major – Stretch Marks

5 A No Faults

6 C Major – Missing End & Stain

7 C Major – Stain & Missing Pick

8 D Major – Stretch Marks

9 C Major – Faulty Selvedge

10 C Major – Missing End & Stretch Marks

It can be seen from Table 5.15 that Test Image 1-4, 6, 7, 9 &10 have been graded as ‗C‘

quality which matches perfectly with the severity of the defective region. Also Test Image

5 which had no defective regions had been graded as ‗A‘ quality. The grading obtained for

Test Image 8 was also found to be perfect considering the severity of the defect. It can be


138

concluded that 10 out of 10 Test Images gave perfect results and therefore it can be said

that the algorithm is able to detect the defective regions successfully and the results show

the variability of the order of even less than the range 5-10 % as specified earlier.

5.5.3 Defect Detection in Spunbond Images:

The images of the regions with defects were processed using the method as described in

Chapter 4, Section 4.5.2.2. The visual results of the grayscale image, binary image after

thresholding and the image with highlighted defective regions for each defect have been

shown in the Figures from Figure 5.58 to 5.75.

FIGURE 5.58 : Grayscale Image – Drop/Bond Point Fusion


139

FIGURE 5.59 : Binary Image – Drop/Bond Point Fusion

FIGURE 5.60 : Highlighted Drop/Bond Point Fusion


140

FIGURE 5.61 : Grayscale Image – Pin Hole

FIGURE 5.62 : Binary Image – Pin Hole


141

FIGURE 5.63 : Highlighted Pin Hole

FIGURE 5.64 : Grayscale Image– Wrinkle


142

FIGURE 5.65 : Binary Image – Wrinkle

FIGURE 5.66 : Highlighted Wrinkle


143

FIGURE 5.67 : Grayscale Image – Hard Filament

FIGURE 5.68 : Binary Image – Hard Filament


144

FIGURE 5.69 : Highlighted Hard Filament

FIGURE 5.70 : Grayscale Image – Hole


145

FIGURE 5.71 : Binary Image – Hole

FIGURE 5.72 : Highlighted Hole


146

FIGURE 5.73 : Grayscale Image – Calender Cut

FIGURE 5.74 : Binary Image – Calender Cut


147

FIGURE 5.75 : Highlighted Calender Cut

As compared to the woven fabrics, the extraction of the defects was quite difficult due to

the presence of thin - thick regions in the nonwoven fabrics. The detection rate of the local

defects i.e. the one tending to merge with the basic structure was found to be low. The area

of the identified defects was calculated and the same has been tabulated in Table 5.16. The

values were compared with the values obtained by manual - visual examination of the

defects and the same have been tabulated in Table 5.17.

TABLE 5.16 : Defect Statistics obtained from the System

Sr.

No.

Defect Name Fabric

Sample

GSM Defect Area

(sq. cm)

Defect Type

1 Drop/Bond Pt.

Fusion

NS7 120 0.78 Irregular(dark)

2 Pin Hole NS6 85 0.35 Small circle

(light)

3 Wrinkle NS9 60 2.44 Irregular lines

(light)

4 Hard Filament NS3 60 3.73 Irregular lines

5 Hole NS5 60 53.51 Irregular (light)

6 Calender Cut NS10 60 0.20 Very Fine

line/curve


148

TABLE 5.17 : Defect Statistics obtained from Manual - visual Examination

Sr.

No.

Defect Name Fabric

Sample

GSM Defect Area

(sq. cm)

Defect Type

1 Drop/Bond Pt.

Fusion

NS7 120 1.5376 Spots

2 Pin Hole NS6 85 0.33 Small hole

3 Wrinkle NS9 60 2 lines

4 Hard Filament NS3 60 9.17 Patch

5 Hole NS5 60 53.352 Holes

6 Calender Cut NS10 60 10 Fine line/curves

FIGURE 5.76 : Comparison of Defect statistics obtained from the System with those


It can be seen from Table 5.16, Table 5.17 and Figure 5.76 that the defect statistics

obtained from the system are quite similar to the ones obtained from manual - visual

examination. The detection rate of Calender Cut was found to be less as compared to the

other defects, the reason being very less difference between the intensity levels of the

defect and defect free area. Also the defect was very fine, however some information about

the defect could be gathered to mark presence of little variation.

0

10

20

30

40

50

60

Drop/Bond Pt. Fusion


Hole Calender Cut

De

fect

Are

a (S

q.

cms)

Defect Type

Manual

Automatic


149

5.5.4 Validation of Results for Spunbonds:

5.5.4.1 Grading of Spunbond Images:

As mentioned earlier, the fabric images had multiple defective regions and so the grading

of the spunbond images was done. The grading was based on the total defective area,

objectionable defective area and the number of defective areas. The manual - visual

grading of the fabrics with identified defective regions is shown in Table 5.18. The total

defective area present in the image, percentage defective area, objectionable defective area,

percentage defective area, total number of objectionable defects & grading of the image

have been calculated using the algorithm and the same has been tabulated in Table 5.19.

TABLE 5.18 : Manual Grading of Spunbond Fabrics

Image. No. Main Defect Present Fabric Sample GSM Manual - visual Grading

1 Drop/Bond Pt. Fusion NS7 120 Minor

2 Pin Hole NS6 85 Minor

3 Wrinkle NS9 60 Major

4 Hard Filament NS3 60 Minor

5 Hole NS5 60 Major

6 Calender Cut NS10 60 Minor

TABLE 5.19 : Grading of Spunbond Images using the System

Image.

No.

Main

Defect

Present

Fabric

Sample

GSM Total

Defect

-ive

Area

% Total

Defective

Area

Number

of

Objection

-able

Defects

Objection

-able

Area

%

Objection

-able

Defective

Area

GRAD

E OF

FABRI

C

1 Drop/Bond

Pt. Fusion

NS7 120 24.33 11.01 2 15.7827 7.1413 B

2 Pin Hole NS6 85 6.74 3.05 0 0 0 A

3 Wrinkle NS9 60 42.24 19.11 2 19.6842 8.9066 B

4 Hard

Filament

NS3 60 44.20 20 2 29.8914 13.5251 B

5 Hole NS5 60 259.38 100 6 258.1209 100 D

6 Calender

Cut

NS10 60 15.85 7.17 1 3.4806 1.5749 A


150

TABLE 5.20 : Comparison between the Grading of Spunbond Images obtained by

Manual - visual Examination & System

Image.

No.

Main Defect Present Fabric Sample GSM Manual - visual

Grading

GRADE OF

FABRIC

1 Drop/Bond Pt. Fusion NS7 120 Minor B

2 Pin Hole NS6 85 Minor A

3 Wrinkle NS9 60 Major B

4 Hard Filament NS3 60 Minor B

5 Hole NS5 60 Major D

6 Calender Cut NS10 60 Minor A

5.5.4.2 Grading of Multiple Images Spunbond Images with same Defect:

The results obtained as above were validated by capturing multiple images of same fabric

regions. The results are tabulated in Table 5.21 and show the variability of the order of

only 5-10 % in the defect statistics which is considered to be negligible, while no variation

was seen in the grading of the images thus giving a consistent result.

TABLE 5.21 : Grading of Multiple Images of Regions with same defect

Image.

No.

Main

Defect

Present

Fabric

Sample

GSM Total Area of defect in sq.

cm as per software

Objectionable Area of

defect in sq. cm as per

software

Grading as

per System

I1 I2 I3 I1 I2 I3 I1 I2 I3

1 Drop/Bond

Pt. Fusion

NS7 120 24.33 24.40 24.71 15.78 18.21 19.38 B B B

2 Pin Hole NS6 85 6.74 8.29 8.22 0.00 0.00 0.00 A A A

3 Wrinkle NS9 60 42.24 44.18 45.48 19.68 25.11 34.05 B B B

4 Hard

Filament

NS3 60 44.20 41.07 43.18 29.89 36.47 35.18 B B B

5 Hole NS5 60 259.38 250.33 248.41 258.12 241.35 248.41 D D D

6 Calender

Cut

NS10 60 15.85 14.75 14.18 3.48 0.00 2.83 A A A


151

FIGURE 5.77 : Comparison of Total Area between Multiple Images of Regions with

same defect

FIGURE 5.78 : Comparison of Objectionable Area Multiple Images of Regions with

same defect

0

50

100

150

200

250

300


Pin Hole Wrinkle Hard Filament Hole Calender Cut

Comparison of Total Area of defect between Multiple Images (sq. cm)

I1

I2

I3

0

50

100

150

200

250

300


Pin Hole Wrinkle Hard Filament Hole Calender Cut

Comparison of Objectionable Area of defect between Multiple Images

(sq. cm)

I1

I2

I3


152

5.5.4.3 Grading of other Spunbond Images than the Studied Ones:

The developed algorithm was thus found reliable and was tested for a number of images of

other fabric regions. Some of the images with highlighted defective regions and the

grading so obtained have been shown in Figures 5.79 – Figures 5.88.



153




154




155




156




157


TABLE 5.22 : Comparison between the Grading achieved with developed System as

against Manual - visual grading

Test Image Grading As Per Software Manual - visual Grading

1 C Major

2 C Major

3 B Minor

4 B Minor

5 A No Faults

6 B Major

7 A No Faults

8 A No Faults

9 A No Faults

10 B Minor

It can be seen from Table 5.22 that Test Image 1 & 2 have been graded as ‗C‘ quality

which matches perfectly with the severity of the defective region. Also Test Image 5, 7, 8

& 9 which had no defective regions had been graded as ‗A‘ quality. The grading obtained

for Test Image 3, 4 & 10 was also found to be perfect considering the severity of the

defect. Test Image 6 had little deviation from the actual severity as it was graded of ‗B‘

quality by the system. It can be concluded that 9 out of 10 Test Images gave perfect results


158

and therefore it can be said that the algorithm is able to detect the defective regions and the

results show the variability of the order of only 5-10 % which is considered to be negligible.

It is evident from this section that the developed system is able to detect the defective

regions quite well as well grade the fabrics according to the severity of the defects for both

the woven geotextiles and spunbond nonwovens.

CHAPTER – 6

Conclusion & Future Work

6.1 Objectives Achieved:

Successfully designed & developed prototype of device well supported with the user

friendly software module to help the users:

In selection of proper quality of nonwoven/functional fabrics for specific

end use applications

To avoid unnecessary wastage of time and materials, which otherwise

would be due to wrong selection of materials for any specific application

Mainly dealing with the development of functional textiles having very high growth

potential during the days to come

Conclusion & Future Work

160

6.2 Conclusions:

• Designed & developed prototype device for monitoring the quality of

nonwoven/functional textiles.

• Prepared algorithm for development of software module most suitable for different

varieties of fabrics.

• Tested nonwoven fabrics for different quality parameters and validate the results so

obtained by capturing multiple images of same fabric samples using image

processing technique. The results show the variability of the order of only 5-10 %

which is considered to be negligible.

• Tested other functional fabrics for different quality parameters and validate the

results so obtained by capturing multiple images of same fabric samples using

image processing technique. The results show the variability of the order of only 5-

12 % which is considered to be negligible.

6.3 Possible Future Scope:

The research work has lead to the development of a cost and quality effective solution for

the woven geotextile fabrics and spunbond nonwoven fabrics. The study offers a scope for

further research on using image processing techniques for more applications in the area of

textiles which can be listed as below:

Development of similar systems for other categories of technical textiles.

Development of similar systems for other types of nonwoven fabrics.

Influence of yarn faults on the fabric faults can also be explored using image

processing.

Classification of fabrics based on the fabric parameters.

161

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List of Publications

INTERNATIONAL:

1. ―Industrial Fabrics used in Conveyor & Power Transmission Belts‖ – paper published

in the Proceedings of 6th International Conference on ―Advances in Textiles,

Machinery, Nonwovens and Technical Textiles‖ held during 7th -9th of December

2009 at Bannari Amman Institute of Tech., Sathyamangalam, Erode District,

Tamilnadu, India , organized jointly with Texas Tech University, Nonwovens &

Advanced Materials Laboratory, The Institute of Environmental & Human Health,

Lubbock, USA

2. ―Quality Parameters for Medical Textiles and Their Assessment‖ - paper published in

the Proceedings of MEDITEX-2014 International Conference on ―Current Trends in

Medical Textile Research‖ organized by Centre of Excellence In Medical Textiles, The

South India Textile Research Association, Coimbatore, Tamil Nadu, India and

sponsored by Office of the Textile Commissioner, Ministry of Textiles, Government of

India on 1st March, 2014.

3. ―Quality Parameters for Baby Diapers and Their Assessment‖ - paper published in the

Proceedings of INDO – CZECH INTERNATIONAL CONFERENCE on

―Advancements in Specialty Textiles and their Applications in Material Engineering

and Medical Sciences (ICIC 2014) ‖ organized jointly by Department of Textile

Technology / Department of Fashion Technology, Kumaraguru College of Technology,

Coimbatore and Technical University of Liberec, Faculty of Textile Engineering,

Czech Republic during 29th-30th April, 2014.

4. ―Development of Eco Friendly and Cost Effective Solutions for Packaging Industries‖-

paper published in the Proceedings of International Conference on ―Technical Textiles

and Nonwovens‖ organized by IIT Delhi during 6-8 November, 2014 at IIT Delhi.

5. ―Development of Conductive Fabrics and their Applications in Textiles‖ – in

TEXTILE ASIA, p.29-32, Dec. 2011.

6. ―Developments in Medical Textiles for the Need of the Day‖- paper published as a

Poster at the International Conference on ―Technical Textiles and Nonwovens‖

organized by IIT Delhi during 6-8 November, 2014 at IIT Delhi.

7. ―Quality Requirements For Woven Fabrics Used As Functional Textiles‖, paper

published in the Proceedings of the Global Textile Congress organized by The Textile

177

Association (India) in association with Thailand Convention & Exhibition Centre,

Thailand Theme : ―Global Textile – Opportunities & Challenges in an Integrated

Word‖ during 13-15 February, 2015 at Ambassador Hotel (Convention Hall ),

Bangkok, Thailand.

8. ―High Performance Nonwovens for Infrastructural Developments in India‖ – paper

published in the Proceedings of the Second International Conference on Nonwovens

for High Performance Applications organized by the International Newsletters Ltd.,

UK during 4-5 March, 2015 at Novotel Hotel, Cannes, France.

NATIONAL:

9. ―Influence of Properties of Back-Up Fabrics on Properties of Synthetic Leather‖ in

Journal of the Textile Association. May-June, 2014, Vol. No. 75 No. 1 pg.39.

10. ―A Review of Detection of Structural Variability in Textiles using Image Processing

and Computer Vision‖ in Journal for Research| Volume 01| Issue 12 | February 2016

ISSN: 2395-7549, pg. 46-50

Patents: Filed Provisional patent for ―Fabric Quality Monitoring Device‖ with Application

No. 201621014065, Transaction ID- N-0000972722.

http://www.textileassociationindia.org/images/191/33-41.pdf

178

Appendix A

179

180

181

Appendix B

Script for learning of Images

%% %spunbond clear all, close all I1=imread('Still0024ns7.jpg') %convert gray g=rgb2gray(I1) figure(1), imshow(g) cg = adapthisteq(g); figure(2), imshow(cg); gd=im2double(g) cgd=im2double(cg) m1=mean2(g) sd1=std2(g) m2=mean2(cg) sd2=std2(cg) % msgbox(sprintf('meanImg=%g\nsdimage=%g\nmean2cg=%g\nsdimage2=%g', m1,

sd1, m2, sd2)) T1=graythresh(g) T2=graythresh(cg) figure(3), imhist(g) figure(4), imhist(cg) u_max1=mean2(max(g)) u_min1=mean2(min(g)) v_max2=mean2(max(cg)) v_min2=mean2(min(cg)) % figure(5), surfc(g) % figure(6), surfc(cg) % figure(7), boxplot(g) %% %geoclear all, close all I1=imread('Still0027g5s.jpg') %convert gray7 g=rgb2gray(I1) figure(1), imshow(g) B = imgaussfilt(g, 1.5) cg=gradientweight(B, 1.5) figure(2), imshow(cg); m1=mean2(g) sd1=std2(g) m2=mean2(cg) sd2=std2(cg) % msgbox(sprintf('meanImg=%g\nsdimage=%g\nmean2cg=%g\nsdimage2=%g', m1,

sd1, m2, sd2)) T1=graythresh(g) T2=graythresh(cg) % figure(3), imhist(g) % figure(4), imhist(cg) u_max1=mean2(max(g)) u_min1=mean2(min(g)) v_max2=mean2(max(cg)) v_min2=mean2(min(cg)) % figure(5), surfc(g) % figure(6), surfc(cg) % figure(7), boxplot(g)

182

Script for Defect Detection

function varargout = Finalnew_QC_all(varargin) % FINALNEW_QC_ALL MATLAB code for Finalnew_QC_all.fig % FINALNEW_QC_ALL, by itself, creates a new FINALNEW_QC_ALL or

raises the existing % singleton*. % % H = FINALNEW_QC_ALL returns the handle to a new FINALNEW_QC_ALL or

the handle to % the existing singleton*. % % FINALNEW_QC_ALL('CALLBACK',hObject,eventData,handles,...) calls

the local % function named CALLBACK in FINALNEW_QC_ALL.M with the given input

arguments. % % FINALNEW_QC_ALL('Property','Value',...) creates a new

FINALNEW_QC_ALL or raises the % existing singleton*. Starting from the left, property value pairs

are % applied to the GUI before Finalnew_QC_all_OpeningFcn gets called.

An % unrecognized property name or invalid value makes property

application % stop. All inputs are passed to Finalnew_QC_all_OpeningFcn via

varargin. % % *See GUI Options on GUIDE's Tools menu. Choose "GUI allows only

one % instance to run (singleton)". % % See also: GUIDE, GUIDATA, GUIHANDLES

% Edit the above text to modify the response to help Finalnew_QC_all

% Last Modified by GUIDE v2.5 20-Jul-2016 13:06:57

% Begin initialization code - DO NOT EDIT gui_Singleton = 1; gui_State = struct('gui_Name', mfilename, ... 'gui_Singleton', gui_Singleton, ... 'gui_OpeningFcn', @Finalnew_QC_all_OpeningFcn, ... 'gui_OutputFcn', @Finalnew_QC_all_OutputFcn, ... 'gui_LayoutFcn', [] , ... 'gui_Callback', []); if nargin && ischar(varargin{1}) gui_State.gui_Callback = str2func(varargin{1}); end

if nargout [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:}); else gui_mainfcn(gui_State, varargin{:}); end

183

% End initialization code - DO NOT EDIT

% --- Executes just before Finalnew_QC_all is made visible. function Finalnew_QC_all_OpeningFcn(hObject, eventdata, handles,

varargin) % This function has no output args, see OutputFcn. % hObject handle to figure % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) % varargin command line arguments to Finalnew_QC_all (see VARARGIN)

% Choose default command line output for Finalnew_QC_all handles.output = hObject;

% Update handles structure guidata(hObject, handles);

% UIWAIT makes Finalnew_QC_all wait for user response (see UIRESUME) % uiwait(handles.figure1);

% --- Outputs from this function are returned to the command line. function varargout = Finalnew_QC_all_OutputFcn(hObject, eventdata,

handles) % varargout cell array for returning output args (see VARARGOUT); % hObject handle to figure % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA)

% Get default command line output from handles structure varargout{1} = handles.output;

% -------------------------------------------------------------------- function Untitled_1_Callback(hObject, eventdata, handles) % hObject handle to Untitled_1 (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA)

% -------------------------------------------------------------------- function Untitled_2_Callback(hObject, eventdata, handles) % hObject handle to Untitled_2 (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA)

% -------------------------------------------------------------------- function Untitled_5_Callback(hObject, eventdata, handles) % hObject handle to Untitled_5 (see GCBO) for geo % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) % getting image file global im1 im1a [path,user_cance]=imgetfile(); if user_cance msgbox(sprintf('Error'),'Error','Error'); return

184

end im1=imread(path); axes(handles.axes1); imshow(im1);

% -------------------------------------------------------------------- function Untitled_6_Callback(hObject, eventdata, handles) % hObject handle to Untitled_6 (see GCBO) %%process for geo % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) %processing Geotexrile Fabric global im1 g=rgb2gray(im1) [rim cim]=size(g) totalimagearea=rim*cim B = imgaussfilt(g, 1.5) cg=gradientweight(B, 1.5) % figure(2), imshow(cg) m1=mean2(g) m2=mean2(cg) [mm nn]=size(cg); for i=1:mm for j=1:nn if cg(i,j)>0.2 new(i,j)=1; else new(i,j)=0; end end end % figure(3), imshow(new) if m2<0.1 bwk=im2bw(new) else bwk=im2bw(~new) end % figure(4), imshow(bwk)

%% cc = bwconncomp(bwk); L = labelmatrix(cc); rgb = label2rgb(L); % imshow(rgb) k1=regionprops(L, 'Area', 'BoundingBox') no=cc.NumObjects %% %using area, majoraxis.... allBlobAreas=[k1.Area] max1=max(allBlobAreas) bwk1=bwareafilt2(bwk,[49 max1], 10, 'largest') cc1=bwconncomp(bwk1) L1=labelmatrix(cc1) stats=regionprops(L1, 'Area', 'BoundingBox',

'MajorAxisLength','Perimeter') n2=cc1.NumObjects MA=[stats.MajorAxisLength] oriarea=[stats.Area] sumoriarea=sum(oriarea) peri=[stats.Perimeter] % figure(5), imshow(bwk1) bwk1=imfill(bwk1,'holes')

185

figure(6), imshow(bwk1)

%% %eliminating non defect regions DT1=('nil');defectno1=0 DT2=('nil');defectno2=0 DT3=('nil');defectno3=0 A=0 B=0 C=0 D=0 for j=1:n2 op1(j)=[stats(j).BoundingBox(1)] op2(j)=[stats(j).BoundingBox(2)] lth(j)=[stats(j).BoundingBox(3)] bth(j)=[stats(j).BoundingBox(4)] bbarea(j)=lth(j)*bth(j) if peri(j)<500 i(j)=j else i(j)=0 end end %% %detecting defect type & frequency for j=1:n2 if i(j)~=0 & bth(j)<40 DT1='Warp Wise-Missing End/Stain Line' defectno1=defectno1+1 else if i(j)~=0 & lth(j)<40 DT2='Weft Wise-Missing Pick/Weft Crease' defectno2=defectno2+1 else if i(j)~=0 %&peri(j)<500 DT3='Circular-Pinhole/Stain spots/Slubs/Gout' defectno3=defectno3+1 end end end

if i(j)~=0& MA(j)>50 & MA(j)<=280 %& peri(j)<500 A=A+1 elseif i(j)~=0& MA(j)>280 & MA(j)<=570 %& peri(j)<500 B=B+1 elseif i(j)~=0& MA(j)>570 & MA(j)<=850 %& peri(j)<500 C=C+1 elseif i(j)~=0& MA(j)>850% & peri(j)<500 D=D+1 end

end

defectno=A+B+C+D

%% %Grading if A>B & A>C & A>C & defectno<=2 | sumoriarea<800 fabgrade='A' else if B>A & B>C & B>D & defectno<=3 fabgrade='B' else if C>A & C>B & C>D | defectno<=6

186

fabgrade='C' elseif D>A & D>B & D>C | defectno>=7 fabgrade='D' end end end %% %show image axes(handles.axes1); imshow(im1), hold on; himage=imshow(bwk1) set(himage, 'AlphaData', .3)

%% % all calculations areabb=sum(bbarea) areacms=areabb/1390 areainch=areabb/9000 perdefarea=areabb/totalimagearea*100 if perdefarea>100 perdefarea=100 end

MAmax=max(MA) MAmaxinch=MAmax/95

%% f = figure('Position',[100 300 1200 250]); % create the data d = [areainch, perdefarea, MAmaxinch, defectno, {fabgrade},

{DT1},{DT2},{DT3} ]; % Create the column and row names in cell arrays rnames = {'Values'}; cnames = {'Total Defective Area','Total Percentage Defective Area',

'Length/Width of Biggest Defect' 'No. of Defects','GRADE OF

FABRIC','Defect Names'}%, 'Probable Defect Name'}; cformat = {'numeric','numeric','numeric', 'numeric','char', 'char',

'char', 'char'};

% Create the uitable t = uitable(f,'Data',d,... 'ColumnName',cnames,... 'ColumnFormat',cformat,... 'RowName',rnames);

t.Position(3) = t.Extent(3); t.Position(4) = t.Extent(4);

% -------------------------------------------------------------------- function Untitled_3_Callback(hObject, eventdata, handles) % hObject handle to Untitled_3 (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) %getting image file

global im1 im1a [path,user_cance]=imgetfile(); if user_cance msgbox(sprintf('Error'),'Error','Error');

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return end im1=imread(path); axes(handles.axes1); imshow(im1);

% -------------------------------------------------------------------- function Untitled_4_Callback(hObject, eventdata, handles) % hObject handle to Untitled_4 (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) % processing Spunbonded Fabrics global im1 %convert gray g=rgb2gray(im1) axes(handles.axes1); imshow(g) [rim cim]=size(g) totalimagearea=rim*cim cg = adapthisteq(g); m1=mean2(g) m2=round(mean2(cg)) sd2=std2(cg) k1=cg>100 % dk k2=cg<180 % lt % figure(1), imshow(k1) % figure(2), imshow(k2) %% % thresholding bwk1=~k1 bwk2=~k2 % figure(3), imshow(bwk1)% dk % figure(4), imshow(bwk2)% lt %% % dark places cc1 = bwconncomp(bwk1); L1 = labelmatrix(cc1); rgb1 = label2rgb(L1); k11=regionprops(L1, 'Area', 'BoundingBox') no1=cc1.NumObjects %% %light places cc2 = bwconncomp(bwk2); L2 = labelmatrix(cc2); rgb2 = label2rgb(L2); k12=regionprops(L2, 'Area', 'BoundingBox') no2=cc2.NumObjects %% % finding regions dk allBlobAreas1=[k11.Area] max11=max(allBlobAreas1) bwk11=bwareafilt2(bwk1,[49 max11], 5, 'largest') cc11=bwconncomp(bwk11) L11=labelmatrix(cc11) % stats1=regionprops(L11, 'Area', 'BoundingBox', 'MajorAxisLength',

'MinorAxisLength', 'EquivDiameter')

% finding regions lt allBlobAreas2=[k12.Area] max12=max(allBlobAreas2) bwk12=bwareafilt2(bwk2,[49 max12], 5, 'largest')

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cc12=bwconncomp(bwk12) L12=labelmatrix(cc12) % stats2=regionprops(L12, 'Area', 'BoundingBox', 'MajorAxisLength',

'MinorAxisLength', 'EquivDiameter')

%% %combined defects bwk22=imadd(bwk11,bwk12) bwk22=imfill(bwk22,'holes') % figure(5), imshow(bwk22)

cc22 = bwconncomp(bwk22); L22 = labelmatrix(cc22); rgb22 = label2rgb(L22); k22=regionprops(L22, 'Area') no22=cc22.NumObjects allBlobAreas22=[k22.Area] max22=max(allBlobAreas22) bwk22a=bwareafilt2(bwk22,[49 max22], 10, 'largest') cc22a=bwconncomp(bwk22a) L22a=labelmatrix(cc22a) stats22=regionprops(L22a, 'Area', 'BoundingBox', 'MajorAxisLength') MA22=[stats22.MajorAxisLength] oriarea22=[stats22.Area] n222=cc22a.NumObjects sumoriarea22=sum(oriarea22) % figure(6), imshow(bwk22a) %% % finding small obj and eliminating them % lt(1)=0 % dk(1)=0 for j=1:n222

lth22(j)=[stats22(j).BoundingBox(3)] bth22(j)=[stats22(j).BoundingBox(4)] bbarea22(j)=lth22(j)*bth22(j)

perdifflth(j)=abs(oriarea22(j)-bbarea22(j))/bbarea22(j) if bbarea22(j)<1000 i(j)=j else i(j)=0 end end

defectno=0 bbarea22a=0

for j=1:n222 if i(j)==0 & oriarea22(j)>300 & MA22(j)>100 lth22a(j)=[stats22(j).BoundingBox(3)] bth22a(j)=[stats22(j).BoundingBox(4)] bbarea22a(j)=lth22a(j)*bth22a(j) defectno=defectno+1 else defectno=defectno+0 end end db=0 for j=1:n222

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if perdifflth(j)<0.6% & oriarea22(j)<3000 db=db+1 end end axes(handles.axes1); imshow(im1), hold on; himage22=imshow(bwk22a) set(himage22, 'AlphaData', .25) %% %all calculations areabb22=sum(bbarea22) % dk areacms22=areabb22/1390 areainch22=areabb22/9000 perdefarea22=areabb22/totalimagearea*100 if perdefarea22>100 perdefarea22=100 end objarea=sum(bbarea22a) objareacms=objarea/1390 objareainch=objarea/9000 objperdefarea22=objarea/totalimagearea*100 if objperdefarea22>100 objperdefarea22=100 end %% %output if (perdefarea22<=10 & objperdefarea22<=10) & defectno<=2 fabgrade='A' elseif (perdefarea22<=30 & objperdefarea22<=30) & defectno<=4 fabgrade='B' elseif (perdefarea22<=60 & objperdefarea22<=60) & defectno>=2 fabgrade='C' elseif (perdefarea22>=60 & objperdefarea22>=60) & defectno>=1 fabgrade='D' end

if fabgrade=='A' DT='No Objectionable Defect' elseif fabgrade=='D' DT='Holes' elseif (db==3 | db==4) & defectno>=2 DT='Drop Bond/Point Fusion' else DT='PinHole/CC/HF/Holes' end

%% f = figure('Position',[100 300 1200 250]); % create the data d = [areacms22, perdefarea22, defectno, objareacms, objperdefarea22,

{fabgrade}, {DT} ]; % Create the column and row names in cell arrays rnames = {'Values'}; cnames = {'Total Defective Area','Total Percentage Defective

Area','Number of Objectionable Defects', 'Objectionable Area','Percentage

Objectionable Defective Area', 'GRADE OF FABRIC', 'Probable Defect

Name'}; cformat = {'numeric','numeric','numeric', 'numeric','numeric', 'char'};

% Create the uitable t = uitable(f,'Data',d,...

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'ColumnName',cnames,... 'ColumnFormat',cformat,... 'RowName',rnames);

% Set width and height t.Position(3) = t.Extent(3); t.Position(4) = t.Extent(4);

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Appendix C

Documents

DEVELOPMENT OF SYSTEM FOR ONLINE/OFFLINE QUALITY … - 119997125001 - Krishma Desai.pdf · DEVELOPMENT OF SYSTEM FOR ONLINE/OFFLINE QUALITY CONTROL OF NONWOVEN FABRICS/FUNCTIONAL