Image-Based Damage Assessment for Underwater Inspections

231 Pages·2018·90.346 MB·English

by  Bidisha Ghosh, Michael O’Byrne, Franck Schoefs, Vikram Pakrashi

Image-Based Damage
Assessment for
Underwater Inspections
http://taylorandfrancis.com
Image-Based Damage
Assessment for
Underwater Inspections
Bidisha Ghosh
Michael O’Byrne
Franck Schoefs
Vikram Pakrashi
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Contents
Preface xi
Authors xiii
1 Introduction 1
1.1 Aim of this book 1
1.2 Imaging in inspections 3
1.2.1 Advantages of image-processing
as an inspection tool 4
1.2.2 Limitations 4
1.3 Sample applications of image-processing techniques 5
1.3.1 Measuring the width and length of cracks 5
1.3.2 Automatic corrosion detection 6
1.3.3 Bridge vibration assessment 7
1.3.4 3D shape recovery of marine
growth colonized structure 7
1.4 Book outline 9
1.5 Summary 10
References 10
2 Inspection methods and image analysis 11
2.1 Introduction 11
2.2 Inspection of marine structures 11
2.3 Status of inspection processes 13
2.3.1 Types of inspections 13
2.3.1.1 Routine inspections 14
2.3.1.2 Principal inspections 14
2.3.1.3 Special inspections 15
2.3.2 Underwater inspections 16
v
vi Contents
2.3.3 Visual inspections carried out by divers 18
2.3.4 Underwater non-destructive testing (NDT) tools 19
2.3.4.1 Electromagnetic methods 20
2.3.4.2 Ultrasonic methods 21
2.3.4.3 Radiographic methods 21
2.3.4.4 Acoustic emission 21
2.3.4.5 Vibration analysis 21
2.4 Conventional photo collection procedures 22
2.5 Underwater photography 24
2.6 Scope for integrating image-based
techniques into inspections 25
2.7 Using image-processing data for subsequent analysis 26
2.8 Conclusion 27
References 27
3 Fundamentals of image acquisition and imaging protocol 29
3.1 Introduction 29
3.2 Camera 29
3.2.1 Sensor 30
3.2.1.1 Sensor size 30
3.2.1.2 Pixel count 32
3.2.1.3 Dynamic range 32
3.2.1.4 Sensor technology 32
3.2.2 Lens 33
3.2.2.1 Focal length 33
3.2.2.2 Aperture 35
3.2.2.3 Types of lenses 36
3.2.2.4 Other lens features 36
3.2.2.5 Filters and lens ports 37
3.3 Camera settings 37
3.3.1 Image archiving 38
3.3.2 Focusing 39
3.3.3 Aperture, ISO, and shutter speed 40
3.3.4 HDR 42
3.4 Guidelines for obtaining good quality
imagery for quantitative analysis 43
3.4.1 Collection protocol 43
3.4.1.1 Photographic lighting 43
3.4.1.2 Turbidity 44
3.4.1.3 Underwater stereo image acquisition 45
Contents vii
3.4.1.4 Logistical considerations 46
3.4.1.5 Combined underwater protocol 47
3.5 Conclusion 47
References 48
4 Fundamentals of image analysis and interpretation 49
4.1 Introduction 49
4.2 Image representation 49
4.2.1 Image types and pixel bit-depth 50
4.2.2 Color spaces 52
4.3 Pre-processing algorithms 54
4.3.1 Point operations 55
Histograms 55
Contrast enhancement 57
4.3.2 Neighborhood operations 58
Filtering 58
4.3.3 Image restoration/enhancement
methods using multiple images 65
Generating HDR imagery 65
Noise suppression by image averaging 69
4.3.4 Geometric transformations 70
4.4 Camera calibration 72
4.5 Summary 76
References 76
5 Crack detection 79
5.1 Introduction 79
5.2 Crack detection technique 80
5.3 Performance evaluation under various conditions 87
5.3.1 Test imagery 88
5.3.2 Results 89
5.4 Extracting physical properties of detected cracks 93
5.5 Summary 93
References 94
6 Surface damage detection 97
6.1 Introduction 97
6.2 Types of damage encountered in marine environment 98
viii Contents
6.3 Color based damage detection techniques 98
6.3.1 Surface damage detection method 99
6.3.1.1 Identification 102
6.3.1.2 Clustering-based filtering 103
6.3.1.3 Support vector machine enhancement 106
6.3.2 Technique evaluation and comparison
with other methods 107
6.4 Texture analysis 109
6.4.1 Methodology 109
6.4.1.1 Texture characteristics map 109
6.4.1.2 GLCM features 111
6.4.1.3 Descriptive statistics and
Shannon entropy 115
6.4.1.4 Support vector machine classification 118
6.5 Comparison of color and texture based methods 121
6.6 Discussion and conclusion 122
References 123
7 3D Imaging 127
7.1 Introduction 127
7.2 Approaches for obtaining 3D information 128
7.3 Stereo imaging 129
7.3.1 Calibration 131
7.3.1.1 Checkerboard-based calibration 132
7.3.1.2 Self-calibration 136
7.3.2 Rectification 137
7.3.3 Stereo correspondence algorithm 139
7.3.3.1 Matching cost computation 140
7.3.3.2 Belief propagation on a
Markov random field 142
7.4 Triangulation 148
7.5 Surface reconstruction 149
7.6 Summary 150
References 151
8 Repository and interpretation 155
8.1 Introduction 155
8.2 Experimental set-up 156
8.2.1 Contents of the repository 157
8.2.2 Controlled and partially controlled images 159
Contents ix
8.2.3 Damage type 159
8.2.3.1 Cracks 159
8.2.3.2 Surface damage 160
8.2.3.3 Shape information 161
8.2.4 Turbidity and lighting 163
8.2.5 Surface type 164
8.3 Online portal of ULTIR 167
8.4 ROC-based performance evaluation of algorithms 168
8.4.1 Crack detection 172
8.4.2 Surface damage 175
8.4.3 3D shape recovery using stereo vision 178
8.5 Conclusions 182
References 182
9 Examples of future applications 185
9.1 Introduction 185
9.2 Integrating image data into subsequent analyses 186
9.3 Virtual reality inspections and spherical image acquisition 192
9.3.1 Spherical image acquisition 194
9.4 Deep learning based damage detection 196
9.5 Video analysis 198
9.5.1 Equipment and set-up 199
9.5.2 Video tracking technique 200
9.5.3 Tracking challenges 200
9.5.4 Results 201
9.5.5 Smartphones 201
9.6 Use of existing image archives from past inspections 203
9.7 Summary 205
References 206
10 Conclusions 207
10.1 Summary 207
10.2 Limitations and future research directions 208
Index 211