IJDW Volume 4 • Number 1 • January-June 2012 • pp. 51-58

SURFACE DEFECTS DETECTION FOR CERAMIC TILES USING IMAGEPROCESSING AND MORPHOLOGICAL TECHNIQUESRashmi Mishra1 and C.L. Chandrakar2

1,2SSCET, Bhilai, 1E-mail: ranu.rashmi@gmail.com, 2E-mail: chandrakar79@gmail.com

Abstract: Quality control in ceramic tile manufacturing is hard, labour intensive and it is performed in aharsh industrial environment with noise, extreme temperature and humidity. It can be divided into colouranalysis, dimension verification, and surface defect detection, which is the main purpose of our work. Defectsdetection is still based on the judgment of human operators while most of the other manufacturing activitiesare automated so, our work is a quality control enhancement by integrating a visual control stage usingimage processing and morphological operation techniques before the packing operation to improve thehomogeneity of batches received by final users. An automated defect detection and classification techniquethat can ensured the better quality of tiles in manufacturing process as well as production rate.Keywords: Quality Control, Defects detection, Visual control, Image processing, Morphological operation

1. INTRODUCTION

THE ceramic tiles manufacturing process has nowbeen completely automated with the exception ofthe final stage of production concerned with visualinspection. This automated classification methodhelps us to acquire knowledge about the pattern ofdefect within a very short period of time and also todecide about the recovery process so that thedefected tiles may not be mixed with the fresh tiles.This paper is concerned with the problem ofautomatic inspection of ceramic tiles using computervision. It must be noted that detection of defect intextured surfaces is an important area of automaticindustrial inspection that has been largelyoverlooked by the recent wave of research inmachine vision applications. Humans are able to findsuch defects without prior knowledge of the defect-free pattern. Defects are viewed as in-homogeneitiesin regularity and orientation fields. Two distinct butconceptually related approaches are presented. Thefirst one defines structural defects as regions ofabruptly falling regularity, the second one asperturbations in the dominant orientation. Bothmethods are general in the senses that each of themis applicable to a variety of patterns and defects.Human judgment is, as usual, influenced byexpectations and prior knowledge. However, thisproblem is not specific to structural defects. In manydetection tasks for example, edge detection, there isa gradual transition from presence to absence. On

the other hand, in “obvious” cases most naïveobservers agree that the defect is there, even whenthey cannot identify the structure. Such a monitoringtask is of course tedious, subjective and expensivebut it is based on a long experience and can utilizethe huge appreciation and recognition abilities of thehuman brain.

Any machine vision system will neveradvantageously replace the visual inspection if it isnot able to:

1. Analyze the colour of the product withreliability.

2. Detect every type of manufacturing defects,with at least the same accuracy as the humaneye.

3. Measure with high precision the dimensionsof the tiles

The defect detection operation induces that theentire surface of every tile must be imaged andanalyzed. The goal of the inspection is to give astatistical analysis of the production batches. So, allbatches of tiles will be imaged individually withoutany sampling operation. The image acquisitionachieved directly on line, in the real time. The imageanalysis algorithm must be fast enough to follow theproduction rate. This paper aims to create a visualsystem that is capable of detecting the surface defectsfor the fired ceramic tiles. That ensure the productsare free from defects for the classifying process.

52 Rashmi Mishra and C.L. Chandrakar

Classifying process must be effectively, objectivelyand repeatedly, with sufficient rapidness and lowcosts. It must have the ability to adapt autonomouslyto changes in materials. The techniques used rangefrom Long crack, Crack, Blob, Pin-hole and Spotdetectors algorithms for plain, and textures tiles. Thistherefore reduces the number of complaints tiles. Thepresented inspection procedures have beenimplemented and tested on a number of tiles usingsynthetic and real defects.

The results suggested that the performance isadequate to provide a basis for a viable commercialvisual inspection system which we will see it in thenext sections.

We generally have found total eight types ofdefects from the existing defect detection methods.These types of defects are shown in the followingTable 1.

3. IMAGE ACQUISITION AND CAPTURINGIMAGE ACQUISITION

Image acquisition is the process of obtaining adigitized image from a real world source. Each stepin the acquisition process may introduce randomchanges into the values of pixels in the image whichis referred to as noise.

A ceramic tile image is captured and stored intothe computer for further processing. This may beachieved by taking a photograph with a conventionalcamera, having the film made into a print andscanning the print into a computer.

We intended to create images that are moresuitable the human visual perception object detectionand target recognition. We used the principles ofImage processing and morphological operations onthe ceramic tiles images.

Therefore, we get new images that contain thesurface defect only to make easier for the detectingprocess and classification operation via the judgmentof the operator.

4. IMAGE CAPTURING

The ceramic tiles have been captured through theonline camera held on the line production at theindustry. The image captured will convert to anotherkinds of images (Binary, and Gray scale) to besuitable for the various defect detection algorithmsused for the different types of defects.

A bag of tricks is used rather than standardalgorithms and formal mathematical properties willbe discussed like Edge detection, Morphologyoperations, Noise reduction, smoothing process,Histogram equalization, and intensity adjustment.

The effects of unequal lighting and of the spacesensitivity of the TV camera CCD are correctedanalyzing a sample tile made of white Plexiglaswhose image has been previously divided in 8x8sectors. This number represents a compromisebetween spatial resolution distribution andcomputing time.

4.1 Image Enhancement

Actually, image enhancement technique is to makethe image clearer so that various operations can beperformed easily on the image. For this, at first thecaptured image is converted to the gray level image.

Contrast stretching (often called normalization)is a simple image enhancement technique that

Table 1 Types of Ceramic Tile Defects

Name of Defects Description

Crack Break down of tilePinhole Scattered isolated black-white pinpoint spotBlob Water drop spot on tile surfaceSpot Discontinuity of color on surfaceCorner Break down of tile cornerEdge Break down of edgeScratch Generally scratch on surfaceGlaze Blurred surface on tile

2. METHODOLOGY

This project aims to create a visual system that iscapable of detecting the surface defects for the firedceramic tiles. That ensure the products are free fromdefects for the classifying process. Classifyingprocess must be effectively, objectively andrepeatedly, with sufficient rapidness and low costs.It must have the ability to adapt autonomously tochanges in materials. The flowchart of my methodis shown below:

Figure 1: General Flowchart

Surface Defects Detection for Ceramic Tiles Using Image Processing and Morphological Techniques 53

attempts to improve the contrast in an image bystretching’ the range of intensity values it containsto span a desired range of values, e.g. the full rangeof pixel values that the image type concerned allows.Low contrast images can be found due to the poorillumination, lack of dynamic range in the imagingsensor, or due to the wrong setting of the lens.

The idea behind the contrast stretching is toincrease the dynamic range of intensity level in theprocessed image. The general process of the contraststretching operation on gray scale image is to applythe following equation on each of the pixels in the inputimage to form the corresponding output image pixel:

( , ) ( , ) min)max min

inO x y I x y i

where, O(x, y) represents the output image,I(x, y) represents the xth pixel in the yth column inthe input image. In this equation, ni represents thenumber of intensity levels, I represents the initialintensity level, “min” and “max” represent theminimum intensity value and the maximumintensity value in the current image respectively.Here “no. of intensity levels” shows the total numberof intensity values that can be assigned to a pixel.

5. EDGE DETECTION

An edge may be regarded as a boundary betweentwo dissimilar regions in an image. These may bedifferent surfaces of the object, or perhaps aboundary between light and shadow falling on asingle surface. In principle, an edge is easy to findsince differences in pixel values between regions arerelatively easy to calculate by considering gradients.

Many edge extraction techniques can be brokenup into two distinct phases:

• Finding pixels in the image where edges arelikely to occur by looking for discontinuitiesin gradients.

• Linking these edge points in some way toproduce descriptions of edges in terms oflines curves etc.

Thresholding produces a segmentation thatyields all the pixels that, in principle, belong to theobject or objects of interest in an image. Analternative to this is to find those pixels that belongto the borders of the objects.

Applying the Defect Detection Process: All pre-processing operations are applied to the referenceimage, stored in the computer database to comparewith the test image.

Let, the resulting image is I2. Now we considerI1 as the resulting image found from the test imageafter applying all pre-processing operations. Wepropose here a new technique. We store I1 and I2 asmatrix form to a file. Let, these two matrices arenamed as m1 and m2. Then we count the totalnumber of black pixels (in binary, it is representedas 1) in m1 and that in m2. These two are thencompared. If the number of black pixels in m1 isgreater than the number of black pixels in m2 thenwe can make decision that defect is found in the testimage, otherwise we can say that no defect is presentto the test image.

To understand this concept clearly, consider thefollowing example:

Let, we have a test image and a reference imageof equal size (5×5). Now applying preprocessingsteps on the test image we find matrix m1 whosevalue is:

Again, applying the preprocessing operations onthe reference image another matrix m2 is found:

1 0 0 0 0

0 0 1 0 0

0 0 0 0 00 0 0 0 0

0 0 0 0 0

1 0 0 1 00 0 1 0 00 0 0 0 00 0 1 0 10 0 0 1 0

Here, the number of black pixels for the referenceimage is 2 and for the test image this number is 6.So, here obviously 6>2 and we can make decisionthat defect is found on the test image. The detailedblock diagram of the proposed defect detection stepis shown in the following Figure.

Figure 2: Block Diagram of the Proposed Defect Detection Step

54 Rashmi Mishra and C.L. Chandrakar

5.1 Noise Reduction

Noise reduction is a process of removing noise froma captured image. To remove noise some filteringtechniques can be proposed as follows:

One method to remove noise is by convolving theoriginal image with a mask that represent a low-passfilter or smoothing operation. For example, theGaussian mask comprises elements determined by aGaussian function. This convolution brings the valueof each pixel into closer harmony with the values ofits neighbours. In general, a smoothing filter sets eachpixel to the average value, or a weighted average, ofitself and its nearby neighbours; the Gaussian filter isjust one possible set of weights. But smoothing filterstend to blur an image, because pixel intensity valuesthat are significantly higher or lower than thesurrounding neighbourhood would “smear” acrossthe area. Because of this blurring, linear filters areseldom used in practice for noise reduction. For theabove reason, we proposed to use a non-linear filterwhich is called median filter. It is very good atpreserving image detail if it is designed properly. Torun a median filter:

(a) Consider each pixel in the image(b) Sort neighbouring pixels into order based

upon their intensities(c) Replace the original value of the pixel with

the median value from the listA median filter is a rank-selection (RS) filter, a

particularly harsh member of the family of rank-conditioned rank-selection (RCRS) filters; a muchmilder member of that family, for example one thatselects the closest of the neighbouring values whena pixel’s value is extremely in its neighbourhood,and leaves it unchanged otherwise, is sometimespreferred, especially in photographic applications.Median filter technique is good at removing salt andpepper noise from an image, and also causesrelatively little blurring of edges, and hence is oftenused in computer vision applications.

6. MORPHOLOGICAL OPERATIONS

Morphological operations are methods forprocessing binary images also for gray scale imagesbased on shapes.

These operations take the binary and gray scaleimages as input, and return it as output. The valueof each pixel in the output image is based on thecorresponding input pixel and its neighbours. Bychoosing the neighbourhood shape appropriately,

you can construct a morphological operation that issensitive to specific shapes in the input image. Asbinary images frequently result from segmentationprocesses on gray level images, the morphologicalprocessing of the binary result permits theimprovement of the segmentation result. Defects areextracted from the background by thresholding theimage and classified according to size and shapeparameters. Existing machines commonly detect thefollowing defaults:

1. Chips (edges and corners)2. Cracks3. Scratches4. Glaze faults5. Holes and pitting6. LumpsThe sensitivity of the imaging system is linked to

the local roughness contrast induced by the defect; ithas nothing to do with the colour contrast. Becausethey rely on two independent physical properties ofthe material, colour defects and surface defectinspection are complementary. The tiles used in ourexperiments are of size 200 × 200mm and are eitherplain or textured. In the testable images, some defectsmay not be easily visible and we have randomlyencircled some of them for saliency. In most defectimages, a dilation operation is carried out to enhancethe results. All detected faults correspond to true faults.

7. DETECTION ALGORITHMS FOR FIREDCERAMIC TILES

We will see in this section a number of techniquesdeveloped for the detection of multifarious range ofceramic tile defects. Figure 3 shows the first part ofthe algorithm which takes the captured image forthe defective tile then the output is an intensityadjustable histogram equalized image to be the inputto the second part of the algorithm. The second partof the main algorithm include many of individualcomplementary algorithms differs due to the variouskinds of defects.

Figure 3: First Part of the Algorithm

Now we will see the different complementaryindividual algorithms for the detection of variable

Surface Defects Detection for Ceramic Tiles Using Image Processing and Morphological Techniques 55

defects. These algorithms take the intensity adjustedhistogram equalized image as the input image. Theinput image in these algorithms passes throughmany stages to get the final image which includeonly the defect. These stages differ from algorithmto another due to the kinds of defects.

Figure 4 shows the Crack and Long Crack defectdetection algorithm. The input image converted to ablack/white image. An edge detection operation hasbeen done to detect the defect pixels producingapproximately areas to the defects so we follow it by afill gaps operation to discriminate the defect pixels.Some morphology operations have been done todiscriminate the defect pixels more accurately followedby Noise reduction and Smoothing object processingto give a clear image containing the defect only.

The same operations have been applied to theother kinds of defects detection algorithms butwithout the same arrange and the number ofprocessing cycles to the images.

Figure 5 shows the Spot, Longitudinal Spot, andDepression Spot detection algorithm.

Figure 4: Crack Defect, and Long Crack Defect DetectionAlgorithm

Algorithm to Determine Crack Defects

Let c_length as the range of crack.

Step 1. Check every pixel coordinate (i, j) fromleft to right up to the last pixel element.

Step 2. If any (i, j) has value 1 then

(a) Consider its adjacent eight pixels and findwhich are 1.

(b) If any adjacent pixel has value 1 then Currentpixel coordinate will be updated to it.

(c) Apply the backtracking process to find outall connected pixels and count the length.

Step 3. Apply step 2 to all pixels and for eachpixels.

Step 4. Counting all length of the connectedpixels found from step 2 and step 3, find out themaximum number and set it to c_count.

Step 5. Finally, apply step 2 to specify the crackdefected pixel coordinates so that other types ofdefects are not affected to it.

Step 6. If c_count > c_length, then make decisionthat crack is found, otherwise crack is not found.

Figure 5: Spot, Longitudinal Spot, and Depression SpotDetection Algorithm

Algorithm to Determine Spot Defects

Let, matx as size of spot, row as the maximumnumber of image pixels along any row and col asthe maximum number of image pixels along anycolumn.

Step 1. Let, start=(matx/2)+1; Here start is themiddle element of (matx*matx) .

Step 2. Check every pixel coordinate (i, j) fromleft to right up to the last pixel element.

for row consider the range from start to row-start+1

for column consider the range from start to col-start+1

(a) If any pixel coordinate (i, j) is 2, then

(i) Considering it as the middle element andcheck the total (matx*matx) elements aroundit to find out how many 2 exists into theseregion.

(ii) Let, the total number of 2 is equal to s_length.

(iii) If s_length = (matx*matx), then Makedecision that spot defect is found and exitfrom loop.

(b) Otherwise, switch to next pixel coordinate at step 2.

Step 3. After searching every pixel coordinate, ifthere is nos_length matches to (matx*matx), thenmake decision that spot defect is not found.

56 Rashmi Mishra and C.L. Chandrakar

8. PIN HOLE DEFECT DETECTION:

It is easier to detect the Pin-hole defects. That byapplying some morphological operations directly tothe input image followed by SDC morphologicaloperation (morphology operations specialized forgray scale images). Finally in this algorithm theimage passes to Noise reduction processing to get aclear image for the defect.

Figure 6 shows the Pin-hole defect detectionalgorithm.

Step 6. Finally, check value of p_count. Ifp_count>0, then pinhole is found, otherwise notfound.

Because the blob defects always have no littlepixels so it has a discriminated area needs only todisplay it. The input image complemented by aninverse operation to display more clearly the blobdefect pixels followed by some morphologicaloperations and nose reduction processing to get afinal image for the blob defect pixels only. A fill gapsoperation may be added to the final image to increasethe clearance of the defects pixels than others.

Figure 7 shows Blob defect detection algorithm.

Figure 6: Pin Hole Defect Detection Algorithm

Algorithm to Determine Pinhole Defects

Let, p_count as a variable for pinhole count,c_range as the range of corner, e_range as the rangeof edge and row as the maximum number of imagepixels along any row and column as the maximumnumber of image pixels along any column.

Step 1. Set, temp_a =c_range , and temp_b=e_range

Step 2. Divide the total searching area for pinholeinto three regions.

Step 3. For left side region,

for row consider the range from temp_a+1 torowc_range-1

for column consider the range from temp_b+1to c_range

(a) Check every pixel coordinates whether it is 0 ornot.

(b) If it is true then(i) For each coordinate (i,j) check all of its eight

neighbours.(ii) If (i,j–1), (i,j+1), (i–1,j), (i+1,j) position values

are 1 and the rest are 0, thenP_count will be incremented by 1.

Step 4. For right side region, range for row isfrom temp_a+1 to row-c_range-1 and range forcolumn is from coltemp_a+1 to col-e_range. The restsare same as step-3.

Step 5. For other middle side elements, range forrow is from temp_b+1 to row-e_range and range forcolumn is from col-temp_a+1 to col-c_range. Therests are same as step-3.

Algorithm to Determine Blob Defects: Let, matxas size of blob, row as the maximum number ofimage pixels along any row and column as themaximum number of image pixels along anycolumn.

Step 1. Let, start=(matx/2)+1; Here start is themiddle element of (matx*matx) .

Step 2. Check every pixel coordinate (i,j) fromleft to right up to the last pixel element.

for row consider the range from start to row-start+1

for column consider the range from start to col-start+1

(a) If any pixel coordinate (i,j) is 2, then(i) Considering it as the middle element and

check the total (matx*matx) elements aroundit to find out how many 2 exists into theseregion.

(ii) Let, the total number of 2 is equal to b_length.(iii) If b_length = (matx*matx), then Make

decision that blob defect is found and exitfrom loop.

(b) Otherwise, switch to next pixel coordinate at step2.

Figure 7: Blob Defect Detection Algorithm

Surface Defects Detection for Ceramic Tiles Using Image Processing and Morphological Techniques 57

Step 3. After searching every pixel coordinate, ifthere is no b_length matches to (matx*matx), thenmake decision that blob defect is not found.

9. EXPECTED OUTPUT

We had described in the previous section optimalCrack, Long-Crack, Pin-hole, Blob, and spotdetection algorithms used independently also aspost-processing stages to our other techniques. It isa very accurate approach but it is computationallydemanding. We apply these algorithms on a severalnumber of tiles, which are plain tiles and texturedtiles. In addition, we apply these algorithms on adozen of tiles images. These tiles images have thesame kind of defect whereas the operating conditions(speed of the line with all its irregularities, vibrationsetc.) were similar to real conditions. That is forlogistic reasons to see if the algorithm give the sameresult or not and see the differences. When applyingthe individual algorithms for defect detection wefound the results as in the next figures.

Figure 8 and 9 show the defective tile imagecontaining Crack defect and Isolated Crack defecttile image using Image processing and Morphologyoperations Crack defect detection algorithm.

DETECTION ALGORITHM

Figure 14 and 15 show the defective tile imagecontaining Pin-hole defect and Isolated Pin-holedefect tile image using Image processing andMorphology operations Pin-hole defect detectionalgorithm.

Figure 8 and 9

10. DETECTION ALGORITHM

Figures 10 and 11 show the defective tile imagecontaining Long crack defect and Isolated Long crackdefect tile image using Image processing andMorphology operations Long crack defect detectionalgorithm.

Figure 10 and 11

Figure 12 and 13 show the defective tile imagecontaining Blob defect and Isolated Blob defect tileimage using Image processing and Morphologyoperations Blob defect detection algorithm.

Figure 12 and 13

Figure 14

Figure 15

We analyze dozens of tiles including varioustypes of defects. We choose one tile image from eachdozen of defective tile images to drag it in this paper.Other typical conditions of ceramic factories (dust,high temperatures, etc) were taken into accountduring design by applying the first part of ImageProcessing and Morphology operation detectionalgorithm which gives an accurate and clear image tobe processed in all complementary individualalgorithms.

We success in designing individual algorithmsthat can detect almost all kinds of defects in the firedceramic tiles. When we tried to apply all individualalgorithms as a unique algorithm or an overallalgorithm regardless the sequence of thesealgorithms on the previous defective tile images, weget good results but not as when we had appliedindividual algorithms. These results are not accurate

58 Rashmi Mishra and C.L. Chandrakar

and clear like the previous results. In addition somedefect pixels disappeared therefore it is not describedthe defect accurately which affect in classification orsorting process that depend on the area of defect.

The result of the project is a prototype analyzertechnique with some major simplifications comparedto the solutions currently available on the marketusing an algorithm for detecting the defects in firedceramic tile. The acquisition system allows thesystem to be installed without having to makemechanical or electrical modifications to the sortingline. These results in lower costs and means that thesystem can be moved when required.

11. CONCLUSION

This paper concerned with the problem of detectionof the surface defects included on the fired ceramictiles using the image processing and Morphologyoperations. By using this technique we can developthe sorting system in the ceramic tiles industries fromdepending on the human which detects the defectsmanually upon his experience and skills whichvaries from one to one to the automated systemdepending on the computer vision. That affectmainly in the classification or sorting operationwhich also done by human in the industry. Peoplecan work effectively for short periods and manydifferent operators are involved in checking the samebatch of tiles. Continuity over time is not guaranteedand may result in overall poor quality, which maycause customers to complain or even to reject thebatch. Miss-sorting is kept at an extremely low level.We success in isolating different kinds of defect inceramic tiles images. Automated sorting systemswould bring numerous benefits to the entire sectorwith major economic advantages, also guaranteeproduct quality, increase plant efficiency and reducefixed and periodic investments. The continuousmeasurement of surface defects gives line productionoperators to optimize temperature profile, speed andother operating parameters.

REFERENCES

[1] Vincent LEBRUN, “Quality Control of Ceramic Tilesby Machine Vision”, Flaw Master 3000, SurfaceInspection Ltd. 2001.

[2] C. Boukouvalas, J. Kittler, R. Marik, M. Mirmehdi andM. Petrou, “Ceramic Tile Inspection for Color andStructural Defects”. I.E.E.E. Transactions on PatternAnalysis and Machine Intelligence, 14(1), March 1998.

[3] Stewart Coe, “Automatic Tile Inspection”. SurfaceInspection Limited, International Ceramics, Bristol, U.K.,Issue 1, 2000.

[4] Costas Boukouvalas, Francesco De Natale, JosefKittler, and Roberto Salgrai. “An Integrated Systemfor Quality Inspection of Tiles”. University of Surrey,Guilford, GU2-5XH, England, 1999.

[5] Yonghuai Liu and Macros Rodrigues, “A NovelMachine Vision Algorithm for a Fast ResponseQuality Control System”. Department of ComputerScience, the University of Hull, Hull, HU6 7RX, UK 2001.

[6] Martin Coulthard, “Measuring and Classifying TileShades”. Chairman, Surface Inspection Ltd, GreatBritain, Jan., 2001.

[7] Song K. Y., Petrou M., and Kittler, “Texture CrackDetection”. Machine Vision Applications, to Appear, Jan.,1995.

[8] Esko Herrala, “SIMON; Spectral Imaging for On LineSorting”. Specim, Spectral Imaging Ltd., Finland,August 1998.

[9] Tony Lindeberg, “Edge Detection and RidgeDetection with Automatic Scale Selection”.Department of Numerical Analysis and ComputingScience, Royal Institute of Technology, InternationalJournal of Computer Vision, S-10044 Stockholm,Sweden, Jan., 1996.

[10] Mark E. Lehr and Keh-Shin Li, “Template BasisTechniques to Pattern Recognition”. University ofCalifornia Riverside, Department of Statistics Riverside,California, USA, Vol. 2825, 1996.

[11] Todd Reed and H. Wechsler, “Segmentation ofTextured Images and Gestalt Organization UsingSpatial/spatial-frequency Representations”. I.E.E.E.Transactions on Pattern Analysis and MachineIntelligence, 12(1), Jan., 1990.

[12] W. Wen, and A. Xia, “Verifying Edges for VisualInspection Purposes”. School of Mechanical andManufacturing Engineering, School of Mathematics, TheUniversity of New South Wales, NSW 2052, Australia,July 1997.

[13] Simon Baker, “Design and Evaluation of FeatureDetectors”. Doctor of Philosophy Thesis in the GraduateSchool of Arts and Sciences, Columbia University, 1998.

[14] Richard Bridge, “Computer Image Processing”.Tessella Support Services PLC, Oxon, England, IssueV1.R2.M0, June, 2003.