detection of pavement distress using laser technology.pdf

7/27/2019 detection of pavement distress using laser technology.pdf

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DETECTIO

Submit

for the

M2

DEPA

OF PAVEMENT DISTR

LASER TECHNOLOGY

SEMINAR REPORT

ted in partial fulfillment of the requirem

award of M.Tech Degree in Civil Enginee

of University of Kerala

Submitted by

BHAGEERATHY K P

Traffic and Transportation Engineerin

Roll No: 122607

TMENT OF CIVIL ENGINEE

COLLEGE OF ENGINEERING

TRIVANDRUM

2013

SS USING

ents

ring

RING


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DEPA

This is to cePAVEMENT DIST

of the work done by

fulfillment of the requ

(Traffic and Transpor

year 2013.

Guided by

Dr. Manju V S

Associate Professor

Department of Civil E

College of EngineeriTrivandrum

TMENT OF CIVIL ENGINEE

COLLEGE OF ENGINEERING

TRIVANDRUM

2013

CERTIFICATE

rtify that this seminarreport entitled

ESS USING LASER TECHNOLOGY i

Bhageerathy K P under my guidance t

irements for the award of M.Tech Degree i

tation Engineering) under the University o

Profe

Dr.

ngg. Depart

ng Colle

RING

ETECTION OF

a bonafide record

wards the partial

Civil Engineering

Kerala during the

sor (PG Studies)

Satyakumar

Professor

ent of Civil Engg.

ge of EngineeringTrivandrum


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ACKNOWLEDGEMENT

I am sincerely indebted to my guide Dr. Manju V. S., Associate Professor,

Department of Civil Engineering, College of Engineering Trivandrum, for her

valuable guidance and suggestions in preparing this seminar report.

I would also like to thank Prof. Jyothis Thomas, Professor and Head,

Department of Civil Engineering, Dr. M. Satyakumar, Professor (P.G Studies),

Prof. Jayaprakash Jain, Staff Advisor and Prof. Leema Peter, Assistant Professor

(Project coordinator), Department of Civil Engineering, for their encouragement and

support.

I would also like to express my sincere gratitude to all my friends who

supported and helped me in completing this report.

BHAGEERATHY K. P.


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ABSTRACT

There are several reasons which contribute towards pavement distresses.

Detection and repair of distresses on time is very much necessary for preventing the

failure of pavements. Usual method of detection of distresses using human

observations is extremely tedious and prone to errors. To overcome the limitations of

visual evaluation, several attempts have been made to automate the process of

detection. One such technology is by using laser scanners. They can be used

effectively for getting 3D pavement surface data.

Two case studies were dealt with for understanding the application of laser

technology in distress detection. The first study deals with the detection of potholes

and severity classification using laser technology. The second study evaluates the

feasibility of using laser technology to detect cracks. Both studies show that laser

technology is very promising for pavement distress detection.


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CONTENTS

Page No.

1. INTRODUCTION 1

1.1 General 1

1.2 Objectives 2

2. PAVEMENT DISTRESSES 2

2.1 General 2

2.2 Types of pavement distresses 2

2.2.1 Surface defects 2

2.2.2 Cracks 3

2.2.3 Deformation 6

2.2.4 Disintegration 8

3. LASER TECHNOLOGY 9

3.1 General 9

3.2 Basic concept 9

3.3 Principle of 3D laser scanners 10

4. CASE STUDY 1 12

4.1 General 12

4.2 Need for the study 12

4.3 Methodology 12

4.4 Image processing techniques for detecting potholes

using laser pattern 13

4.4.1 Multi-window median filtering 13

4.4.2 Tile partitioning 14

4.4.3 Laser line deformation detection approach 15

4.5 Pothole severity classification 16

4.6 Experimental results 18


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4.7 Concluding remarks 19

5. CASE STUDY 2 20

5.1 General 20

5.2 System set-up 20

5.3 Experimental tests 22

5.3.1 Controlled test procedure 22

5.3.2 Field test procedures 25

5.4 Findings and concluding remarks 27

6. CONCLUSIONS 28

REFERENCES 29


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LIST OF FIGURES

Figure No. Title Page No.

Fig 2.1 Fatty surface 3

Fig 2.2 Streaking 3

Fig 2.3 Alligator crack 4

Fig 2.4 Longitudinal crack 4

Fig 2.5 Edge crack 5

Fig 2.6 Shrinkage crack 5

Fig 2.7 Slippage 6

Fig 2.8 Rutting 6

Fig 2.9 Corrugation 7

Fig 2.10 Potholes 9

Fig 3.1 Illustration of optical triangulation principle 10

Fig 3.2 Principle of 3D laser scanners 11

Fig 4.1 Deformed laser pattern on detecting a pothole 13

Fig 4.2 Four masks used for filtering 13

Fig 4.3 Image thresholding 14

Fig 4.4 Template laser line 15

Fig 4.5 Template matching method for pothole shape

estimation 16

Fig 4.6 Architecture of the neural network 18


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Fig 4.7 Results extracted from image database 19

Fig 5.1 Sensing vehicle integrated at the Georgia

Institute of Technology 21

Fig 5.2 Laser crack measurement system and

projection of laser 21

Fig 5.3 Visualization of 3D pavement surface data 22

Fig 5.4 A gap between two solid wood boards to simulate

a crack with known width 23

Fig 5.5 Two lighting conditions 24

Fig 5.6 Crack segmentation results on simulated cracks 24

Fig 5.7 Test results on a crack with low intensity contrast 26

LIST OF TABLES

Table No. Title Page No.

Table 4.1 Distress classification guideline 18

Table 4.2 Severity level comparison 19

Table 5.1 Scores for the controlled tests 25

Table 5.2 Scores for the second field test 27


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1

1. INTRODUCTION

1.1 General

Quantification of pavement distresses like cracks, potholes etc. is one of the

most important tasks in determining optimal strategies of pavement maintenance.

These distresses, which are caused by several reasons, curtail the service life of

pavements. Once initiated, distress increases in severity and extent, allowing water to

ingress the pavement. Over the past years, a significant amount of efforts has been

spent on developing methods to objectively evaluate the condition of pavements. For

the inspection of the surface distress of highway pavements, the most widely used

method to conduct such surveys is based on human observation. This approach is

extremely labour-intensive, prone to errors, and poses hazards. To overcome the

limitations of the subjective visual evaluation process, several attempts have been

made to develop automatic procedures. Most systems use optic images and vision

technology to automate the process. However, due to the irregularities of pavement

surfaces, there has been a limited success in accurately detecting distresses and

classifying distress types. In addition, most systems require complex algorithm withhigh levels of computing power.

With the advancement in sensor technology, an advanced 3D laser system has

become available. The 3D laser scanning is one of the exceptionally versatile and

efficient technologies for accurately capturing large sets of 3D coordinates. 3D laser

scanner uses a technique that employs reflected laser pulses to create accurate digital

models of existing objects. For 3D survey, detection of pavement distresses, such as

potholes or patches, is possible application where laser scanner technology excels.

The advancement of the scanner has invoked many applications such as civil

engineering, natural hazard investigations, heritage, landscape design, and tunnel and

cave survey, and pipelines. The most popular applications include archeology, as-built

surveying, re-modeling of tunnels, bridges, and other civil structures, topographic

mapping for base maps, engineering design, and mining fields, and reconstruction of

traffic accidents, and urban planning.


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2

1.2 Objectives

The objectives of the study are:

i. To know about various distresses occurring on pavements.

ii. To understand the concept of laser technology.

iii. To study about the applications of laser technology in detection of distresses in

pavement.

2. PAVEMENT DISTRESSES

2.1 General

This chapter describes the various distresses that occur in pavements.

2.2 Types of Pavement Distresses

The various types of pavement distresses are grouped as:

Surface defects: which include fatty surfaces, smooth surfaces, streaking and

hungry surfaces

Cracks: under which hair line cracks, alligator cracks, longitudinal cracks, edge

cracks, shrinkage cracks, and reflection cracks comes

Deformation: which includes grouped slippage, rutting, corrugations, shoving,

shallow depressions, and settlements and upheavals

Disintegration: covering slipping, loss of aggregates, raveling, potholes and edge

breaking

2.2.1 Surface defects

These are associated with the surfacing layers and may be due to excessive or

deficient quantities of bitumen in these layers.

Fatty surface:

Fatty surface shown in Fig 2.1 results when the bituminous binder moves

upward in the surfacing and collects as a film on the surface. The causes for a fatty

surface are excessive binder, loss of cover aggregates in surface dressing, non uniform

spreading of cover aggregates, too heavy prime or tack coat and excessive heavy axle

load.


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3

Fig 2.1 Fatty surface (Source: Google image)

Smooth surface:

A smooth surface has a very low skid resistance and becomes very slippery

when wet. A primary cause for the smooth surface is the polishing of aggregates

under traffic. Also excessive binder can result in a smooth surface.

Streaking:

Streaking is characterized by the appearance of alternate lean and heavy lines

of bitumen either in longitudinal or transverse direction. It is shown in Fig 2.2.

Fig 2.2 Streaking (Source: Google image)

Hungry surface:

Hungry surface is characterized by the loss of aggregates from the surface or

the appearance of fine cracks. One of the reasons for hungry surface is the use of less

bitumen in the surfacing. Sometimes this condition may also appear due to use of

absorptive aggregates in the surfacing.

2.2.2 Cracks

Formation of cracks is a common defect in bituminous surfaces. The crack

pattern can, in many cases, indicate the cause of the defect. The common types of

cracks are:


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4

Hair-line crack:

These appear as short and fine cracks at close intervals on the surface. These

cracks are caused by insufficient bitumen content, excessive filler at the surface and

improper compaction.

Alligator crack:

These appear as interconnected cracks forming a series of small blocks which

resemble the skin of an alligator as shown in the Fig 2.3. Main reasons for alligator

cracks are excessive deflection of the surface over unstable subgrade, excessive

overload by heavy vehicles, inadequate pavement thickness ageing of binder etc.

Fig 2.3 Alligator crack (Source: Google image)

Longitudinal crack:

These cracks appear more or less, on a straight line along the road. The

reasons may be due to alternate wetting and drying beneath the shoulder owing to

poor drainage or due to depressions in the pavement edge. It is shown in Fig 2.4.

Fig 2.4 Longitudinal crack (Source: Google image)


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5

Edge crack:

They are found parallel to the outer edge of the pavement as shown in Fig 2.5.

They are caused by lack of lateral support from the shoulder, settlement of underlying

material inadequate surface drainage, shrinkage, frost heave etc.

Fig 2.5 Edge crack (Source: Google image)

Shrinkage crack:

These are cracks appearing in the transverse direction, or as interconnected

cracks forming a series of large blocks. The pavement itself appears to have no

deterioration or deformation, but it is the top surfacing that seems to have become old

and cracked. The primary cause for such cracks is the shrinkage of the bituminous

layer itself with the age. It is shown in Fig 2.6.

Fig 2.6 Shrinkage crack (Source: Google image)

Reflection crack:

They are the sympathetic cracks that appear in the bituminous surfacing over

joints and cracks in the pavement underneath. The pattern may be longitudinal,

transverse, diagonal or block. They occur most frequently on overlays on cement

concrete pavements or on cement-soil bases. They may also occur in overlays on

flexible pavements where cracks in the old pavement have not been properly repaired.


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6

2.2.3 Deformation

Any change in the shape of the pavement from its original shape is a deformation.

Slippage:

It is the relative movement between the surface layer and the layer beneath. It

is characterized by the formation of crescent-shaped cracks as shown in Fig 2.7, that

point in the direction of the thrust of the wheels on the pavement surface. It is caused

by unusual thrust of wheels in a particular direction, inadequacy of tack coat or prime

coat, lack of bond between the surface and the lower course due to excessive

deflection etc.

Fig 2.7 Slippage (Source: Google image)

Rutting:

It is a longitudinal depression or groove in the wheel tracks as shown in Fig

2.8. Ruts are usually of the width of wheel path. If rutting is accompanied by adjacent

bulging, it may be the sign of subgrade movement or weak pavement. The causes of

rutting are heavy channelized traffic, inadequate compaction of the mix, improper mix

design, weak pavement, intrusion of subgrade clay into base layer etc.

Fig 2.8 Rutting (Source: Google image)


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7

Corrugation:

It is the formation of fairly regular undulations across the bituminous surface

as shown in Fig 2.9. It cause discomfort to motorists. Its causes are lack of stability in

the mix, excessive binder, high proportion of fines, round and smooth aggregates, soft

binder and faulty laying of surface course.

Fig 2.9 Corrugation (Source: Google image)

Shoving:

It is a form of plastic movement within the layer resulting in localized bulging

of the pavement surface. Shoving occurs characteristically at points where traffic

starts and stops such as intersections, bus-stops etc. The first indication of shoving is

the formation of slippage cracks which are crescent shaped cracks. Shoving can becaused by lack of stability in the mix, lack of bond between bituminous surface and

underlying layer, heavy traffic movement of a start and stop type, use of non-volatile

oil on roller wheels etc.

Shallow depression:

They are localized low areas of limited size, dipping about 25mm or more

below the desired profile, where water will normally collect. If not rectified in time,

they may lead to further deterioration of the surface and cause discomfort to traffic.

They are caused by the settlement of lower pavement layers.

Settlement and upheaval:

They are characterized by large deformations of the pavement. They are

extremely uncomfortable to traffic and cause serious reduction in speed. The causes

are inadequate compaction of the fill at locations behind bridge abutments, excessive

moisture in the subgrade, inadequate pavement thickness and frost heave conditions.


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8

2.2.4 Disintegration

There are some defects which if not rectified immediately, result in the disintegration

of the pavement into small, loose fragments. Disintegration, if not arrested in the early

stages, may necessitate complete rebuilding of the pavement.

Stripping:

It is characterized by the separation of bitumen adhering to the surfaces of the

aggregate particles in the presence of moisture. This may lead to loss of bond and

subsequently to loss of strength and materials from the surface. The reasons for

stripping are use of hydrophilic aggregates, inadequate mix composition, continuous

contact of water with the coated aggregate, over heating of aggregate or binder,

presence of dust or moisture on aggregate when it comes in contact with the bitumen,

occurrence of rain or dust storm immediately after construction, opening the road to

fast traffic before the binder has set, use of improper grade of bitumen, ageing of

bitumen etc.

Loss of aggregate:

It occurs in surfaces which have been provided with surface dressing. The

surface presents a rough appearance, with some portions having aggregates intact andothers where aggregates have been lost. The loss of aggregates can occur due to

ageing and hardening of binder, stripping of binder from aggregates due to wet

weather, wet or dusty aggregate, insufficient binder, aggregate having no affinity to

the binder, insufficient rolling before opening to traffic, cold-spraying of bitumen or

delaying the spreading of aggregates over sprayed bitumen.

Ravelling:

Ravelling is generally associated with premixed bituminous layers. It is

characterized by the progressive disintegration of the surface due to the failure of the

binder to hold the materials together. The raveling process generally starts from the

downwards or from edge inward. It usually begins with the blowing off of the fine

aggregates leaving behind pock marks on the surface. The reasons for raveling are

inadequate compaction, construction during wet or cold weather, use of inferior

quality of aggregates, insufficient binder in the mix, ageing of binder, poor

compatibility of binder and aggregate, over heating of mix and improper coating of

aggregates by the binder.


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9

Pot-hole:

Pot-holes are bowl-shaped holes of varying sizes as shown in Fig 2.10 in a

surface layer or extending into the base course caused by localized disintegration of

material. They usually appear after rain. The reasons for formation of potholes are

ingress of water into the pavement through the surfacing course, lack of proper bond

between the bituminous surfacing and base, insufficient bitumen content, too thin a

bituminous surface which is unable to withstand the heavy traffic, too much or too

few fines.

Fig 2.10 Potholes (Source: Google image)

Edge-breaking:

The edge of the bituminous surface gets broken in an irregular way, and if not

remedied in time, the surfacing may peel off in large chunks at the edges. The causes

of edge breaking are infiltration of water, worn out shoulders resulting in insufficient

side support to the pavement, inadequate strength at the edge of the pavement due to

inadequate compaction etc.

3. LASER TECHNOLOGY

3.1 General

This section describes the laser technology that can be used for collecting 3D

pavement profile data for detecting the distresses in pavement.

3.2 Basic Concept

Many 3D data acquisition systems are on the basis of the triangulation

principle, as shown in Fig 3.1. In such systems, a specific and often fixed pattern of


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10

illumination (i.e., structured illumination) is projected onto an object to be measured.

The structured light that is projected is a laser line for the system proposed. A digital

area scan camera with a charge-coupled device (CCD) or complementary metal oxide

semiconductor (CMOS) sensor is placed at a known distance and an oblique angle ()

with respect to the light projector. The camera takes images of the structured light.

Then the deformations of the laser line on the object are analyzed to evaluate the

depth (z-axis) for each point with a known horizontal position (x-axis) on the object.

The 3D system is usually coupled with an encoder, which enables the system to

obtain the y-axis position (i.e., the driving direction). Consequently, a complete three

dimensional set of points of the objects surface can be acquired. In addition, such 3D

systems can provide both depth and intensity information. The intersection between

the emitted structured light and the field of view of the digital camera defines the

measurement range of a 3D system.

Fig 3.1 Illustration of optical triangulation principle

(Source: Tsai and Li, 2012)

3.3 Principle of 3D Laser Scanners

A 3D laser scanner, also known as LiDAR (Light Detection And Ranging),

can be considered as an auto-scanning total station which is able to acquire thousands


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11

of points in a few seconds. The laser scanner is operating to obtain point coordinates

referenced to an internal coordinate system as shown in Fig 3.2. On basis of the mode

of platform, LiDAR can be ground-based, air-borne, or space-borne. Usually, the

effective distances between the scanner and the objects are in a short-range (< 1 m),

mid-range (1-30 m), long-range (30 m-1 km), or super-range for air-borne system

(600 m-3000 m). One of the advantages of 3D laser scanner is to measure 3D

coordinates of a complicated object in a distance which may hinder further physical

contact of the object.

Fig 3.2 Principle of 3D laser scanners

(Source: Chang et al., 2011)

Ground-based LiDAR is composed of a high pulse rate laser ranger and a

directional mirror, thus to accurately measure the range from laser head to the target

and then computed along with the mirror angles to obtain the 3D coordinates of the

target. Typical 3D scanner system is composed of three parts of components:

1. Laser ranger: including transmitter, receiver, detector, amplifier, timing counter

and other electronics. To assure that laser light pulse is transmitted and received in a

defined field of view, the light is transmitted and received in an identical path.

2. Optical or mechanical scanning components: for guiding the light in a specified

direction, e.g. rotation mirror, plan rotating mirror.

3. Control and data processing components: including computer, and softwares for

scaning control, preprocessing and post processing such as data processing and

analysis.


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12

4. CASE STUDY 1

DETECTION OF POTHOLES USING LASER

TECHNOLOGY

4.1 General

This case study done by Yu and Salari (2011) deals with the detection of potholes and

its severity measurement using laser imaging.

4.2 Need for the Study

Over the years, Automated Image Analysis Systems (AIAS) have been

developed for pavement surface analysis and management. The cameras used by most

of the AIAS are based on Charge Coupled Device (CCD) image sensors where a

visible ray is projected. However, the quality of the images captured by the CCD

cameras was limited by the inconsistent illumination and shadows caused by sunlight.

To enhance the CCD image quality, a high-power artificial lighting system can be

used, which requires a complicated lighting system and a significant power source.

4.3 Methodology

The proposed laser-based optical system consisted of an active light

source that projects a line pattern of laser beams onto the pavement surface, a camera

for capturing images, and the image processing algorithms that identify the potholes,

as shown in Fig 4.1. After the pavement images were captured, regions corresponding

to potholes were represented by a matrix of square tiles and the estimated shape of

pothole is determined. Following the pothole detection, a feed-forward neural network

is used to determine its severity. The vertical, horizontal distress measures, the total

number of distress tiles and the depth index information are calculated providing input

to a three-layer feed-forward neural network for pothole severity. To validate the

system, actual pavement pictures were taken from pavements both in highway and

local roads, and experiments were done.


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Fig 4.1

4.4 Image Processing

The main

regions in the image.

image is searched fo

different image proces

4.4.1 Multi-window

The removal of impuvideo sequences are c

laser line. The laser

undesired external lig

to perform noise redu

known nonlinear filte

region of an image. Si

liner structures in th

directional median val

Fig 4.2 Four

13

Deformed laser pattern on detecting a pot

(Source: Yu and Salari, 2011)

Techniques for Detecting Potholes Using

aim of the image processing module is to

fter extracting the laser line from the backg

any deformation in the shape of the lase

sing techniques for detecting potholes using

edian filtering

lse noise is an important issue in potholeaptured as image frames, the frames are sc

line is affected by the superposition of a

ting. A multi-window median filter is appli

tion in an image. The standard median (M

r that eliminates the noise and performs

nce the detection of pavement distress invol

pavement image, a multi-stage median f

ues as represented by masks in Fig 4.2, is co

asks used for filtering (Source: Yu and S

hole

aser Pattern

extract laser color

round, the resulting

r line pattern. The

a laser pattern are:

etection. After theanned to detect the

certain amount of

d in the initial step

D) filter is a well-

ell in the smooth

es the detection of

ilter which uses 4

sidered.

lari, 2011)


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14

The multi-stage median filter could be used to reduce the noise while preserving

much of the detail in the 2-D image and produces comparable results with the

standard median filter.

4.4.2 Tile partitioning

Thresholding:

Image tiling starts with binarizing the image using a thresholding operation.

Thresholding is a widely used technique for image segmentation and feature

extraction. In many applications of image processing, the gray levels of pixels

belonging to the object are substantially different from the pixels belonging to the

background. During the thresholding process, individual pixels in an image are

marked as object pixels if their value is greater than some threshold value and as

background pixels if lower. In this study, a laser line pixel is given a value of 0

while a background pixel is given a value of 1. Finally, a binary output image is

created, as shown in Fig 4.3.

Fig 4.3 Image thresholding (Source: Yu and Salari, 2011)

Noise removal:

In this step, morphological closing is applied in order to fill small holes,

bridge the thin gaps in the binary image, connect nearby laser line pixels without

changing the laser line area significantly, and smooth the boundaries. The noise in the

binary image is reduced by labeling connected components and counting the number

of connected pixels. The operation scans the image and groups them together into

components based on pixel connectivity, i.e. all pixels in connected component share

similar pixel intensity values and are connected with each other. Based on the number

of pixels in a connected component, any connected components less than a pre-

defined value would be considered as noise and removed from the image.


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Tile partitioning:

The method p

than pixels. The outp

called tiles. Each

pavement surface. T

complexity relative t

background noise bec

be classified as a poth

each tile is classified

classify a tile as a las

mean value of each tivalue is considered as

labeled with 0. In th

4.4.3 Laser line defor

The laser line

deformed pattern. Fo

produces a pattern wit

The deformation of th

detect the deformatio

predefined laser line t

then compared with th

Fig 4.4

15

roposed in this study relies on sub-images

t image from the previous step is divided i

ile is 40x40 pixels which covers a 2x2

e tile-based method significantly reduces

pixel-based computations. As a result, it

use a few noise pixels alone would not be s

ole tile. After the binary image is sub-divid

as either a laser line tile or non-laser line ti

er line tile is based on the global mean val

le. Any tile that has a mean value lower tha laser line tile and would be labeled 1; ot

is way, a tile-based matrix is generated.

mation detection approach

in the pothole area of the image produces a

r example, the projection of a laser line

h a different shape than the projection of a la

laser pattern can reveal the presence of the

n of the laser line, a template matching

emplate is generated as shown in Fig 4.4. T

e predefined template frame to detect the def

emplate laser line (Source: Yu and Salari

of pavement rather

to 625 sub-images

inch block on the

the computational

is less affected by

fficient for a tile to

d into square tiles,

le. The decision to

ue versus the local

n the global meanerwise it would be

isible contour of a

onto a plane area

ser line onto a ball.

pothole. In order to

ethod is used. A

e input frames are

ormation.

, 2011)


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16

Template matching method:

Pothole shape estimation:

After the tile partitioning step, each frame is compared with the template

frame, tile by tile, for any deformation. The number of tiles in each row that differ

between the input frame and the template frame are calculated. If the row that has the

maximum deformation of1s is above the row that has the maximum deformation of

0s, the laser line would be intersecting an obstacle in the scene. Otherwise, the row is

determined to be an actual deformed tile row due to a pothole. The row is stored in a

new matrix as the first row. This matching process will continue until no further

deformation is detected. All rows that qualify for deformation are stored in the new

matrix. The output matrix would be an estimated shape of the pothole. The process is

shown in Fig 4.5.

Fig 4.5 Template matching method for pothole shape estimation


Depth index:

Depth information is defined based on the extent of the deformation that

affects rows in each frame. For example, in Fig 4.5 (a), deformation could be detected

in 2 rows, so the depth information of this frame would be 2. All depth information is

stored frame by frame until no further deformation is detected. The average of this

information is computed as the depth index for the detected pothole and stored for

further analysis.

4.5 Pothole Severity Classification

Distress extracted through the process as explained in the previous section can

be classified into different types of potholes with various severity levels (low,


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moderate, or high). In

distress classification.

the differences betwe

the distress type by fi

Histograms are used t

distress tiles (zeros) i

measure is determine

deformed tiles in adja

Where VD is the vert

the number of colum

computed by accumul

for adjacent image ro

Where HD is the hori

the number of rows, re

If both horizontal and

classified as a pothol

parameters (the vertic

number of distress tile

forward neural networ

input nodes, 8 hidden

The severity level of

4.1.

17

the study, a three-layer feed-forward neural

The distress measure in an image is calculat

n adjacent histogram values. The neural ne

nding the unique pattern of uniformity in th

measure the statistical information by cou

each column, row and the whole matrix.

d by accumulating the differences betwe

ent columns using equation,

ical distress measure, Hv is the vertical his

ns, respectively. Similarly, the horizontal

ating the differences between the number

s using equation,

ontal distress measure, h is the horizontal h

spectively.

vertical distress measures are having large v

e. A neural network is used for pothole

al distress measure, the horizontal distress

s and the depth index) are used to provide t

k. The architecture of the neural network w

nodes and 5 output nodes is shown in Fig 4.6

the pothole is classified according to the d

network is used for

d by accumulating

work distinguishes

ese distress values.

ting the number of

he vertical distress

n the numbers of

(4.1)

togram, and Nc is

istress measure is

f deformation tiles

(4.2)

istogram, and Nr is

alue, the distress is

lassification. Four

measure, the total

he inputs to a feed-

ich has a total of 4

.

ta shown in Table


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Table 4.1 Distre

Distress

type

Vertic

me

Low

Moderate

High

4.6 Experimental Re

The proposed algorith

images (10 images f

sample distress classif

Fig 4.7 (a) and (b) sho

and (e) shows the poth

18

Fig 4.6 Architecture of the neural networ


ss classification guideline (Source: Yu and

l distress

asure

Horizontal distress

measure

Tota

distr

5 > 5

5 > 5 4

5 > 5 >

ults

m was implemented in MATLAB R2008b o

r each distress) taken from the road surf

ication results extracted from the image data

ws two typical road surface scans of pothole

ole image represented by tiles.

Salari, 2011)

l No. of

ess tiles

Depth

index

40 1

-120 2

120 3

n a set of over 100

ce. Fig 4.7 shows

ase.

images. Fig 4.7 (d)


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19

Fig 4.7 Results extracted from image database


Table 4.2 summarizes the rating results from manual and proposed laser-based

approaches. It can be clearly seen that, in all tested samples, the severity level and the

crack type detected by the proposed method is in agreement with the level obtained

from the manual method.

Table 4.2 Severity level comparison (Source: Yu and Salari, 2011)

Sample No. Distress typeSeverity Level or Crack Type

Manual Proposed

1 Pothole Moderate Moderate

2 Pothole Moderate Moderate

4.7 Concluding Remarks

In the study, a laser based pothole detection and classification method using

advanced image processing techniques was used. It has been shown that the proposed

system allows complete automation with the evaluation of pavement potholes. In

comparison with other existing 2-D pavement distress detection and classification

methods, the proposed method has a better ability to discriminate the dark areas that

are caused by lane marks, oil spills, or shadows. The experimental results indicated

that the proposed system provides reliable and accurate results from the tested

samples.


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5. CASE STUDY 2

DETECTION OF ASPHALT PAVEMENT CRACKS

USING 3D LASER TECHNOLOGY

5.1 General

This study conducted by Tsai and Li (2012) was sponsored by the U.S. Department of

Transportation (US DOT) Research Innovative Technology Administration (RITA)

program. It deals with the evaluation of feasibility of using emerging 3D laser

technology to detect cracks under different lighting and poor intensity contrast

conditions.

5.2 System Set up

A sensing vehicle as shown in Fig 5.1 was integrated at the Georgia Institute

of Technology for collecting 3D pavement surface data. First, the 3D system,

composed of two high-performance laser profiling units, was mounted on the vehicle.

The field of view of the two units covered a full lanes width. The acquired 3D laser

profile was designed to have a 15 clockwise tilt angle to the pavements transverse

direction, as shown in Fig 5.2. This was to ensure that 3D transverse profiles canintersect with transverse cracks. Each profiling unit consisted of a 3D laser profiler

that uses a high-powered laser line projector, a custom filter, and a camera as the

detector. The profiling unit uses a light stripe, which is created by a 7W multiple

emitter laser diode and line-generating optics. The light stripe is projected onto an

objects surface, and its image is captured by the area scan camera. From the captured

image, range measurements are extracted.

With a two-unit setup, the Laser Crack Measurement System (LCMS)produces 4,160 3D data points per profile (2080 pixels2 units) covering a 4m

pavement width. The resolution in x direction (transverse profile direction) is

approximately 1 mm (4 m/4096 points). The accuracy is 0.5 mm in z direction

(elevation). The highest resolution in y direction (longitudinal) depends on the

distance measurement instrument (DMI) and the accompanying encoder. In the

integrated sensing vehicle, an encoder with 1024 pulses per revolution was installed

to trigger the acquisition of 3D continuous transverse profiles. The interval between

two 3D transverse profiles can achieve 2.3mm by using the encoder. The system can


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collect transverse pr

pavement surface dat

visualization of 3D pa

Fig 5.1 Se

Fig 5.2 Lase

21

files at 4.6mm intervals at a speed of 1

was then be acquired for detecting cracks.

vement surface data and a closer look at a cr

sing vehicle integrated at the Georgia Ins

Technology (Source: Tsai and Li, 2012)

crack measurement system and projecti


00 km/h. The 3D

Fig 5.3 shows the

ck line.

itute of

n of laser


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22

Fig 5.3 Visualization of 3D pavement surface data


5.3 Experimental Tests

Utilizing the integrated sensing vehicle, experimental tests were conducted to

consistently and quantitatively evaluate the feasibility of using 3D laser technology to

detect pavement cracks under different lighting conditions and low contrast

conditions. Two series of tests were conducted. One was the controlled laboratory test

on simulated cracks with known crack widths and depths, and the other was the field

test on real roadways. In the controlled tests, the objective was to assess the capability

of the 3D laser technology to detect different widths of cracks under different lighting

conditions. Four crack widths (1, 2, 3 and 5mm) under two extreme lighting

conditions (daytime and nighttime) were tested in the Georgia Institute of

Technologys campus laboratory. The crack depth was approximately 19 mm.

5.3.1 Controlled test procedure

A controlled gap between two solid wood boards was used to simulate a

pavement crack on the road. The width of the gap was measured before and after the

test with a caliper, as shown in Fig 5.4. Afterwards the integrated sensing vehicle was

driven over the road section by an operator to automatically collect the 3D laser data


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of both wood boards by using the 3D sensor system. With the 3D laser data, the

dynamic optimization was employed to segment the simulated cracks. Meanwhile, the

ground truth was manually digitized and extracted from the 3D laser data. The tests

were conducted during the daytime and nighttime, as shown in Fig 5.5. Table 5.1

shows the test results. Fig 5.6 shows only part of the controlled laboratory test results.

It includes four subsets of figures. Each subset of figures shows the 3D raw data on

the left and the crack segmented image produced by using the dynamic optimization

algorithm on the right. Fig 5.6(a) and 5.6(b) show the data collected for a 1mm wide

crack during the daytime and nighttime, respectively. Fig 5.6(c) and 5.6(d) show the

data collected when the simulated crack is 2mm wide. The test results of 3 and 5mm

are similar to the 2mm case.

It was observed that, the 1mm wide crack was partially captured by the 3D

laser technology, and the 2mm wide crack was fully detected. Table 5.1 lists the

quantitative scores derived from the linear buffered Hausdorff scoring method for the

cracks with different widths under two lighting conditions. For cracks with widths of

1mm, scores are approximately 64. For cracks with widths equal to or greater than

2mm, scores are better, approximately 93. Daytime and nighttime tests resulted in

similar scores. The maximum score difference was 0.2. Thus the preliminary

controlled laboratory result demonstrated that the 3D laser system is capable of

detecting cracks whose widths are equal to or wider than 2mm.

Fig 5.4 A gap between two solid wood boards to simulate a crack

with known width (Source: Tsai and Li, 2012)


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Fig 5

Fig 5.6 Cr

24

.5 Two lighting conditions: (a) daytime (b


ack segmentation results on simulated cra


nighttime

ks


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25

Table 5.1 Scores for the controlled tests


Score

Crack width

1 mm 2 mm 3 mm 5 mm

Daytime 63.9 93.6 93.1 93.3

Nighttime 64.1 93.4 93.0 93.1

5.3.2 Field test procedures

In addition to the controlled laboratory test, two field tests on actual

roadways were conducted. The first field test was to evaluate the potential of the 3D

laser system to detect cracks under low intensity contrast conditions. The second field

test was to evaluate the capability of the 3D laser system to detect cracks under

different lighting conditions, including nighttime, daytime with shadow, and daytime

with no shadow.

First test:

Fig 5.7(a) shows a roadway image with a low intensity contrast between a

crack, approximately 1 to 6 mm wide, and pavement background. The low intensity

contrast makes the crack difficult to be detected, even with the human eye. However,

the data collected using the 3D laser technology from the same area showed a more

distinct contrast between the crack and the pavement background. This is illustrated

by Fig 5.7(b) and 5.7(d) collected during day and night respectively. Fig 5.7(c) and

5.7(e) shows the corresponding crack segmentation results. The high scores from this

first test, namely 98.3 for daytime and 98.0 for nighttime demonstrated the potential

of the 3D laser technology for detecting cracks under low intensity contrast

conditions.


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Fig 5.7 Test results on a crack with low intensity contrast


Second test:

The second field test was conducted on State Route (SR) 80 to evaluate

the consistency of using the proposed system in detecting cracks under three different

lighting conditions: nighttime, daytime with shadows, and daytime no shadows, as

shown in Fig 5.8. Eleven test segments, including 10 longitudinal cracks (cracks A to

J) and a transverse crack (crack T), were labeled in the field. Examples of the three

lighting conditions are shown in Fig 5.9. Fig 5.10 shows the 3D raw data collected

under three lighting conditions and corresponding crack segmentation results for the

crack J. Visual observation shows that the crack can be clearly captured by the 3D

laser system. The scores obtained are listed in Table 5.2. The three scores for each

crack were close to each other. The maximum difference among three scores was also

tabulated. The average score difference was found to be 1.9. The difference is very

small. Therefore, results of field tests demonstrated that the proposed 3D laser system

can perform consistently under different lighting conditions in the field.


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Table 5.2 Scores for the second field test


Crack

name

Score

NighttimeDaytime with

shadow

Daytime with no

shadow

Score

difference

A 95.8 97.4 97.2 1.6

B 95.5 96.1 95.4 0.7

C 93.6 96.8 97.2 3.6

D 95.0 97.2 96.9 2.2

E 96.5 97.8 97.3 1.3

F 96.5 98.0 97.5 1.5

G 95.1 97.7 97.5 2.6

H 95.4 96.6 97.6 2.2

I 96.3 96.3 97.4 1.1

J 95.6 97.6 97.7 2.1

T 95.9 96.9 97.6 1.7

Average score difference 1.9

5.4 Findings and Concluding Remarks

Both controlled tests and actual road tests have demonstrated that it is

feasible to detect cracks under different lighting conditions and low contrast

conditions. Controlled tests showed that cracks with widths equal to or greater than 2

mm can be effectively detected from the pavement background, whereas 1 mm wide

cracks can be partially detected. The field tests showed that, for three lighting


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conditions, the average score difference is less than 2%. Thus the experiment shows

that the proposed 3D laser technology is very promising for crack detection.

6. CONCLUSIONS

Pavement distresses are contributed by several reasons. If not attended

properly, they may lead to progressive failure of the entire pavement. Hence there is a

high need for timely detection of pavement distresses. A 2D intensity-based imaging

system is the main data acquisition system that has been used for the past two

decades. Its intensity based data acquisition method makes it sensitive to lighting

effects. In general, the performance of distress detection is severely hampered in the

presence of shadows, lighting effects, non-uniform crack widths, and poor intensity

contrast between cracks and surrounding pavement surfaces. The shallow or thin

cracks are sometimes invisible to the 2D system. Manual inputs are required to adjust

the input parameters so that the algorithms can perform reasonably. Therefore, full

automation of pavement distress detection has remained a challenge especially for

accurate and reliable detection. With the advances in sensor technology, a 3D laser-

based pavement surface data acquisition system that can collect high resolution 3D

continuous pavement profiles for constructing pavement surfaces has become

available. This 3D laser system is different from current 2D intensity-based imaging

systems. First, the 3D laser-based system is not sensitive to lighting. Noise, such as oil

stains and poor intensity contrast, will not interfere with the segmentation algorithms

by using the acquired range data. As long as there is a distinguishable elevation

difference between a crack and its surrounding background, the segmentationalgorithm is able to capture the crack.

In order to understand the potential application of laser technology in distress

detection, two case studies were taken. The first study deals with the automatic

detection of potholes using laser-based optical system. The second study evaluated the

feasibility of using 3D laser technology to detect cracks under different lighting

conditions and low contrast conditions. Both studies showed that laser technology has

potential applications in timely and fully automated detection of distresses inpavements.


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REFERENCES

1. Chang, K, T., Chang J, R., Liu J, K. (2005). Detection of pavement distresses

using 3D laser scanning technology, ASCE 2005 International Conference on

Computing in Civil Engineering, Maxico, Cancun, July 12-17. pp. 1-11.

2. IRC: 82-1982. Code of practice for maintenance of bituminous surfaces of

highways.

3. Tsai, Y, C, J., Li, F. (2012). Critical assessment of detecting asphalt pavement

cracks under different lighting and low intensity contrast conditions usingemerging 3D laser technology.Journal of Transportation Engineering, Vol. 138

(5), pp. 649-656.

4. Wang, C, P, K. (2000). Designs and implementations of automated systems for

pavement surface distress survey. Journal of Infrastructure Systems, Vol.6 (1),

pp. 24-32.

5. Yu, X., Salari, E. (2011). Pavement pothole detection and severity measurement

using laser imaging.IEEE Journal. pp. 1-5.

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