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PAVEMENT CRACK IDENTIFICATION

Mohammad Hidayat Bin Isa

TE 250 M697 2010

Bachelor of Engineering with Honours (Electronics and Computer Engineering)

2010

BORANG PENGESAHAN STATUS TESIS

Judul: Pavement Crack Identification

SESI PENGAJIAN: 2009/2010

Sava MOHAMMAD HIDAYAT BIN ISA (HURUF BESAR)

mengaku membenarkan tesis * ini disimpan di Pusat Khidmat Maklumat Akademik, Universiti Malaysia Sarawak dengan syarat-syarat kegunaan seperti berikut:

I. Tesis adalah hakmilik Universiti Malaysia Sarawak. 2. Pusat Khidmat Maklumat Akademik, Universiti Malaysia Sarawak dibenarkan membuat salinan untuk

tujuan pengajian sahaja. 3. Membuat pendigitan untuk membangunkan Pangkalan Data Kandungan Tempatan. 4. Pusat Khidmat Maklumat Akademik, Universiti Malaysia Sarawak dibenarkan membuat salinan tesis ini

sehagai bahan pertukaran antara institusi pengajian tinggi. 5. '" Si la tandakan (I) di kotak yang berkenaan

F--ý Sl1LIT (Mengandungi maklumat yang berdaijah keselamatan atau ke)entingan Malaysia seperti yang termaktub di dalam AKTA RAHSIA RASMI 1972).

a TERHAD (Mengandungi maklumat TERHAD yang telah ditentukan oleh organisasi/ badan di mana penyelidikan dijalankan).

0 TIDAK TERHAD Disahkan oleh

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(TAND NGAN PENYELIA)

Alamat tctap: .

No. 6 Jalan Telipot 4A/2 Puan Kuryati Kipli Seksýen 3. Bukit Sentosa Nama Penyelia 48300 Rawang. SELANGOR.

Tarikh: :2

CATATAN M "R

Tarikh: 30 JUN 2010

Tesis dimaksudkan sebagai tesis bagi ljazah Doktor Falsafah. Sarjana dan Sarjana Muda. Jika tesis ini SULIT atau TERNAD, sila lampirkan surat daripada pihak berkuasa/organisasi berkenaan dengan menyatakan sekali sebab dan tempoh tesis ini perlu dikelaskan sebagai SULIT dan TERNAD.

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APPROVAL PAGE

The following final year project:

Title : Pavement Crack Identification

Writer Mohammad Hidayat Bin Isa

Matrik : 16657

is being read and approved by:

3o ytUVE 2 010

Madam Kuryati Kipli DATE

SUPERVISOR

Pavement Crack Identification

By

Mohammad Hidayat Bin Isa

This project is submitted to the

Faculty of Engineering, Universiti Malaysia Sarawak

in partial fulfilment of the requirements for the degree for

Bachelor of Engineering with Honours

(Electronics and Computer Engineering)

2010

DEDICATION

Dedicated to my beloved parents who inspired me a lot..

ii

ACKNOWLEDGEMENT

First of all, I would like to convey my very special thanks and gratitude to my

supervisor, Madam Kuryati Binti Kipli who has guided me throughout this final year

project. Her valuable advices and her willingness to spare her precious time in guiding

and assisting me have motivated me to work hard all way through this research.

I also want to say thanks to Mr. Ron Aldrino Chan @ Ron Buking for his idea

and valuable suggestion which allowed me to gain broader understanding for my project.

Besides that, my highest appreciations go to my family members and my fellow

friends for the full support they had given me. Last but not least i would like to thank

everyone who had directly or indirectly contributed to this research.

III

ABSTRAK

Projek ini menunjukkan pengecaman retakan jalan raya berturap. Pengecaman

retakan jalan raya berturap ini direkabentuk daripada kombinasi imej pemprosesan dan

rangkaian saraf. Projek ini adalah untuk membantu penilaian kejuruteraan jalan berturap.

Penilaian jalan berturap di Malaysia masih menggunakan kaedah tradisional, kaedah ini

mempunyai kekurangan seperti proses yang perlahan, memerlukan tenaga kerja

profesional, dan kaedah ini adalah subjektif yang boleh menyebabkan penilaian yang

tidak jitu. Tambahan pula, kaedah lama ini boleh membahayakan peninjau dengan

keadaan lalu lintas yang sesak. Sistem ini menggunakan pemprosessan imej untuk

mendapatkan maklumat dan menjadikan input untuk rangkaian saraf buatan. Beberapa

kombinasi pemprosessan imej digunakan untuk mendapatkan maklumat yang terbaik

pada imej data, seperti imej berskala kelabu, pengesanan pinggir, dan operasi morfologi.

Seterusnya, rangkaian saraf buatan direka dengan melatih imej data sebagai input data

mengikut sasaran data. Projek ini juga memfokuskan pembinaan perisian, pengantaraan

pengguna bergrafik (GUI) adalah cara alternatif untuk pengguna berkomunikasi dengan

sistem yang mesra pengguna, selesa dan interaktif. Sebagai kesimpulan, kombinasi

pemprosessan imej, rangkaian saraf dan GUI boleh membezakan dan mengenalpasti

retakan jalan raya berturap. Tambahan pula, ia memberi kaedah alternatif untuk

pengecaman retakan jalan raya berturap.

iv

ABSTRACT

This project presents the pavement crack identification. It is designed to identify

pavement crack that involves the combination of image processing and neural network.

This project is used to help the engineering evaluation on the road pavement Malaysia,

road pavement evaluation is still using the traditional method. This method has it

shortcoming such as slow process, an experience labor needed and the method of

pavement crack evaluation is subjective that can result inaccurate evaluation.

Furthermore, the old method can endangered the surveyor with the high volume of

traffic. This system used image processing to extract information from the image as an

input for the Artificial Neural Network (ANN). There several combination of image

processing is used to find the best way of extracting the data image, such as grayscale

image, edge detection, and morphology operation. Next, the artificial neural network

created by training the data image as the data input with correspond to the target data.

This project also focus on the software development, Graphical User Interface (GUI) is

an alternative way for user to interact with the system that user friendly, comfort and

interactive for the users. In conclusion, the combination of image processing, neural

network and GUI is capable to distinguish and identify a pavement crack. Furthermore,

it gives an alternative method to identify pavement crack.

V

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LIST OF TABLES

Tables

2.1 Comparison of Training Algorithm in Backpropagation

3.1 Table Show a Part of the Target Data

4.1 Results of Training Neural Network Correspond to

Mean Square Error (MSE)

Page

21

36

47

vi

LIST OF FIGURES

Figures Page

2.1 Cross Section of Typical Flexible Road Pavement

2.2 Types of Cracks

3.1 General Flow Chart for Methodology

3.2 Flow Chart for Image Processing

3.3 Sample Crocodile Cracks after Image Resize

3.4 Sample Crocodile after Grayscale Image Processing

3.5 Sample Crocodile after Edge Detection Process

3.6 Sample Crocodile after Image Dilation Processing

3.7 Sample Crocodile after Image Filling Processing

3.8 Flow Chart for Neural Network Training

3.9 Sample of GUI Design

4.1 Edge Detection `Sobel'

4.2 Edge Detection `Prewitt'

4.3 Edge Detection `Roberts'

4.4 Edge Detection `Canny'

4.5 Edge Detection `uint8'

4.6 Results for 10 hidden neurons

4.7 Results for 20 hidden neurons

4.8 Results for 30 hidden neurons

4.9 Results for 40 hidden neurons

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4.10 Command Line for Load Data and Target 48

4.11 Neural Network Settings 49

4.12 Command Line to Retrieve Image from File and Resize 49

4.13 Command Line for Converting RGB Image into Grayscale

and Edge Detection 50

4.14 Command Line for Image Morphology (Dilation and Filling) 50

4.15 Command Line for Reshape the Image Array 51

4.16 Command Line for Testing with New Image Data 51

4.17 Testing Result 52

4.18 GUI for Crack Pavement Identification 52

4.19 Object Browser 53

4.20 'if ' and 'else' Function 54

VIII

LIST OF ABBREVIATION

ANN - Artificial Neural Network

GUI - Graphical User Interface

LMS - Least Mean Square

TFs - Transfer Functions

BP - Backpropagation

MATLAB - MATrix LABoratory

UNIMAS - Universiti Malaysia Sarawak

RGB - Red, Green and Blue

JKR - Jabatan Kerja Raya

MSE - Mean Square Error

IX

TABLE OF CONTENTS

Dedication

Acknowledgement

Abstrak

Abstract

List of Tables

List of Figures

List of Abbreviation

Chapter 1 INTRODUCTION

1.1 Aim and objective

1.2 Scope of Project

1.3 Limitation

1.4 Statement of Problem

1.5 Benefit of Project

Chapter 2 LITERATURE REVIEW

2.1 Bituminous Road Structure in Malaysia

2.1.1 Pavement Component

2.1.1.1 Road Base

Pages

11

III

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2.1.1.2 Sub-base

2.1.1.3 Sub-grade

2.2 Pavement Cracks

2.2.1 Crocodile Cracks

2.2.2 Block Cracks

2.2.3 Longitudinal Cracks

2.2.4 Transverse Cracks

2.2.5 Edge Cracks

2.2.6 Crescent Shaped Cracks

2.3 Digital Image Processing

2.3.1 Image Acquisition

2.3.2 Image Enhancement

2.3.3 Morphological Processing

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2.4 Artificial Neural Network 16

2.4.1 Perceptron 16

2.4.1.1 The Perceptron Learning Rule 17

2.4.1.2 Training of Perceptron 18

2.4.2 The Widrow-Hoff Learning Rule 18

2.4.3 The Backpropagation Network 19

2.4.3.1 The Backpropagation Algorithm 20

2.4.3.2 Type of Backpropagation

Algorithm 21

X1

2.4.3.2 Traning of Backpropagation

Networks

2.5 MATLAB® Software

23

24

Chapter 3 METHODOLOGY 25

3.1 Introduction 25

3.2 Image Acquisition 27

3.3 Image Processing 28

3.3.1 Image Resizes 29

3.3.2 Grayscale Image and Edge Detection 30

3.3.3 Morphology: Image Dilation 32

3.3.4 Morphology: Image Filling 32

3.4 Artificial Neural Network 34

3.4.1 Input Data Construct 35

3.4.2 Target Data Construct 36

3.4.3 Training Data Input and Target Artficial

Neural Network (ANN) 36

3.4.4 Testing the Neural Network 37

3.4.5 Graphical User Interface Design 37

Chapter 4 RESULTS, ANALYSIS AND DISCUSSIONS

4.1 Image Processing

4.2 Artificial Neural Network

xii

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4.2.1 Training Data Assemble 43

4.2.2 Create the Network Object 43

4.2.2.1 Epoch 44

4.2.2.2 Hidden Neurons 44

4.2.2.3 Training Algorithm 47

4.2.3 Testing Neural Network 48

4.2.3.1 Load Data and Target 48

4.2.3.2 Settings the Neural

Network 49

4.2.3.3 Retrieve Image and

Resize the Image 49

4.2.3.4 Convert the RGB image into

Grayscale Image and Edge

Detection 50

4.2.3.5 Morphology: Dilation and

Filling 50

4.2.3.6 Reshape the Array of the Image

and Change the Properties

of the Image Data 51

4.2.3.7 Neural Network Test on

The New Image Data 51

4.3 Graphical User Interface (GUI) 52

X111

Chapter 5 CONCLUSIONS & RECOMMENDATIONS 55

5.1 Conclusion 55

5.2 Problem Encountered 56

5.3 Recommendation for Future Research 57

REFERENCES 58

APPENDIX A 60

XIV

CHAPTER 1

INTRODUCTION

The aim this project is to analyze image processing method, to find a new

alternative way to recognize or identify pavement cracks. In Malaysia, the evaluation of

pavement crack is still using the traditional and time consuming methods. Thus a new

alternative have to be develop in order to reduce this weakness.

The evaluation of pavement condition is an important part in pavement

management. Accurate evaluations will ensure the management able to make precise

decision, thus yielding a better service condition. Pavement can be evaluated through the

difference types of distress experienced, such as cracking, disintegration and surfaces

deformation. Pavement engineers take times to determine the information in evaluating

the pavement and the pavement crack. The result from the evaluation will be

documented and used to determine present pavement condition, chart past performance

history, and predict future pavement performance [I].

1

1.1 Aims and Objective

Currently, computer and image processing technology has gone through major

enhancement. Thus, an alternative ways of identifying pavement crack can be achieved.

The objectives of this project are;

I. To investigate data collection technique for evaluating pavement cracks and

identified pavement crack.

II. To find the best image processing techniques for detecting pavement cracks by

using MATLAB toolbox.

III. To develop image processing program with Artificial Neural Network (ANN)

that able to identify pavement cracks by using MATLAB software.

2

1.2 Scope of Project

The scope of this project is to develop a program that can identify pavement

crack by using image processing method with ANN and develop Graphical User

Interface (GUI) for the program to be run. This project includes of capture a pavement

image using digital image camera and subsequently processing them by using computer

software to quantify pavement distress. The ANN will be use for training recognition

and GUI will simplified the recognition procedure into a simple program. The software

used for the pavement analysis is MATrix LABoratory (MATLAB) R2008b with image

processing and neural network toolbox and tool for image acquisition is using digital

camera only. Further explanation on the image processing, ANN and GUI is elaborates

in Chapter 3: Methodology. The scope can be summarized as below:

I. Utilize image digital processing to analyze the types of road pavement cracks.

II. Developed a suitable image processing to view the pavement crack(s)

III. Training sample image using neural network to learn recognize desire crack.

IV. Experiment a design proper procedure data collection, image processing, training

neural network and develop a GUI.

3

1.3 Limitation

In this project, the application using image processing technique limited to

flexible pavement (asphalt concrete surfaces) distresses analysis, which is cracking. The

inflexible pavement road cracking pavement analysis was not performed.

For image pavement acquisition, the pavement surface must be dry and the

image must be captured during the day under ambient light. This is because images

taken under direct sunlight are the best.

1.4 Statement of Problem

Engineering evaluation on the road pavement is important as the traffic user is

increasing, with this evaluation the road maintenance system can be improve. Manual

visual inspection, it time consuming and costly. Thus, new ways of recognition of crack

have to develop, in order to reduce cost.

The traditional method for road evaluation in order to monitor pavement surface

condition is the visual observation. This method only assigned to the trained engineers

as they drive along the road doing inspection. Nevertheless this method has it

shortcoming such as:

4

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I. Slow, need experience labor and there are expensive.

II. The method is subjective, which create a result that is inconsistent and inaccurate

in determining pavement condition.

111. Not flexible and did not provide an absolute measurement of the surface.

IV. Can be dangerous or risky to the surveyor due the high speed and high volume

traffic.

There is study to minimize the problem in using the traditional method for crack

recognition. Furthermore, there a number of responses for those automated, indeed the

automated is a better way of recognition of cracks but costly. Therefore a new method

for recognition has to develop, which is better than traditional method but less cost than

the automated.

5

1.5 Benefit of Project

The benefits on the project application are listed as below:

Expected outcome:

"A suitable image processing methods for crack detection.

" Successful neural network training and classification.

" Working program for image recognition of the pavement cracks.

" Graphical User Interface (GUI) pavement crack identification.

Contribution to the field:

" Pavement evaluation will be simplified than the normal ways of survey.

" More accurate road pavement road cracks identification.

" Increased access, reliability and satisfaction for end-users.

6

CHAPTER 2

LITERATURE REVIEW

This chapter gives a brief explanation on bituminous road structure and crack

pavement. Besides, this chapter includes explanation about the digital image processing

and artificial neural network.

2.1 Bituminous Road Structure in Malaysia

The bituminous road in Malaysia first constructed some time stared before the

Second World War. Originally, pavement road were only constructed by using stone

pitching, on sand or laterite sub-based covered with a layer of tar or bitumen stabilized

aggregates, as time goes by the road pavement has evolved. During the world war two,

the pavement road has been constructed with crushed stone road bases and sand sub-

based with dense bituminous surfacing and this construction method are still being

practiced until today [1].

7