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Image-Based Damage Assessment for Underwater Inspections [Hardback]

(University College Cork, Ireland), (Trinity College, Dublin, Ireland), (Trinity College, Dublin, Ireland), (University of Nantes, France)
  • Formāts: Hardback, 216 pages, height x width: 234x156 mm, weight: 476 g, 11 Tables, black and white; 8 Line drawings, color; 6 Line drawings, black and white; 48 Halftones, color; 54 Halftones, black and white; 56 Illustrations, color; 60 Illustrations, black and white
  • Izdošanas datums: 20-Aug-2018
  • Izdevniecība: CRC Press
  • ISBN-10: 1138031860
  • ISBN-13: 9781138031869
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Image-Based Damage Assessment for Underwater InspectionsPalielināt
  • Formāts: Hardback, 216 pages, height x width: 234x156 mm, weight: 476 g, 11 Tables, black and white; 8 Line drawings, color; 6 Line drawings, black and white; 48 Halftones, color; 54 Halftones, black and white; 56 Illustrations, color; 60 Illustrations, black and white
  • Izdošanas datums: 20-Aug-2018
  • Izdevniecība: CRC Press
  • ISBN-10: 1138031860
  • ISBN-13: 9781138031869
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9781138031869.html
Keywords:

Inspection is crucial to the management of ageing infrastructure. Visual information on structures is regularly collected but very little work exists on its organised and quantitative analysis, even though image processing can significantly enhance these inspection processes and transfer real financial and safety benefits to the managers, owners and users. Additionally, new opportunities exist in the fast evolving sectors of wind and wave energy to add value to image-based inspection techniques.

This book is a first for structural engineers and inspectors who wish to harness the full potential of cameras as an inspection tool. It is particularly directed to the inspection of offshore and marine structures and the application of image-based methods in underwater inspections. It outlines a set of best practice guidelines for obtaining imagery, then the fundamentals of image processing are covered along with several image processing techniques which can be used to assess multiple damage forms: crack detection, corrosion detection, and depth analysis of marine growth on offshore structures. The book provides benchmark performance measures for these techniques under various visibility conditions using an image repository which will help inspectors to envisage the effectiveness of the techniques when applied. MATLAB® scripts and access to the underwater image repository are included so readers can run these techniques themselves.

Practising engineers and managers of infrastructure assets are guided in image processing based inspection. Researchers can use this book as a primer, and it also suits advanced graduate courses in infrastructure management or on applied image processing.

Preface xi
Authors xiii
1 Introduction 1(10)
1.1 Aim of this book
1(2)
1.2 Imaging in inspections
3(2)
1.2.1 Advantages of image-processing as an inspection tool
4(1)
1.2.2 Limitations
4(1)
1.3 Sample applications of image-processing techniques
5(4)
1.3.1 Measuring the width and length of cracks
5(1)
1.3.2 Automatic corrosion detection
6(1)
1.3.3 Bridge vibration assessment
7(1)
1.3.4 3D shape recovery of marine growth colonized structure
7(2)
1.4 Book outline
9(1)
1.5 Summary
10(1)
References
10(1)
2 Inspection methods and image analysis 11(18)
2.1 Introduction
11(1)
2.2 Inspection of marine structures
11(2)
2.3 Status of inspection processes
13(9)
2.3.1 Types of inspections
13(3)
2.3.1.1 Routine inspections
14(1)
2.3.1.2 Principal inspections
14(1)
2.3.1.3 Special inspections
15(1)
2.3.2 Underwater inspections
16(2)
2.3.3 Visual inspections carried out by divers
18(1)
2.3.4 Underwater non-destructive testing (NDT) tools
19(10)
2.3.4.1 Electromagnetic methods
20(1)
2.3.4.2 Ultrasonic methods
21(1)
2.3.4.3 Radiographic methods
21(1)
2.3.4.4 Acoustic emission
21(1)
2.3.4.5 Vibration analysis
21(1)
2.4 Conventional photo collection procedures
22(2)
2.5 Underwater photography
24(1)
2.6 Scope for integrating image-based techniques into inspections
25(1)
2.7 Using image-processing data for subsequent analysis
26(1)
2.8 Conclusion
27(1)
References
27(2)
3 Fundamentals of image acquisition and imaging protocol 29(20)
3.1 Introduction
29(1)
3.2 Camera
29(8)
3.2.1 Sensor
30(3)
3.2.1.1 Sensor size
30(2)
3.2.1.2 Pixel count
32(1)
3.2.1.3 Dynamic range
32(1)
3.2.1.4 Sensor technology
32(1)
3.2.2 Lens
33(4)
3.2.2.1 Focal length
33(2)
3.2.2.2 Aperture
35(1)
3.2.2.3 Types of lenses
36(1)
3.2.2.4 Other lens features
36(1)
3.2.2.5 Filters and lens ports
37(1)
3.3 Camera settings
37(6)
3.3.1 Image archiving
38(1)
3.3.2 Focusing
39(1)
3.3.3 Aperture, ISO, and shutter speed
40(2)
3.3.4 HDR
42(1)
3.4 Guidelines for obtaining good quality imagery for quantitative analysis
43(4)
3.4.1 Collection protocol
43(6)
3.4.1.1 Photographic lighting
43(1)
3.4.1.2 Turbidity
44(1)
3.4.1.3 Underwater stereo image acquisition
45(1)
3.4.1.4 Logistical considerations
46(1)
3.4.1.5 Combined underwater protocol
47(1)
3.5 Conclusion
47(1)
References
48(1)
4 Fundamentals of image analysis and interpretation 49(30)
4.1 Introduction
49(1)
4.2 Image representation
49(5)
4.2.1 Image types and pixel bit-depth
50(2)
4.2.2 Color spaces
52(2)
4.3 Pre-processing algorithms
54(18)
4.3.1 Point operations
55(3)
Histograms
55(2)
Contrast enhancement
57(1)
4.3.2 Neighborhood operations
58(7)
Filtering
58(7)
4.3.3 Image restoration/enhancement methods using multiple images
65(5)
Generating HDR imagery
65(4)
Noise suppression by image averaging
69(1)
4.3.4 Geometric transformations
70(2)
4.4 Camera calibration
72(4)
4.5 Summary
76(1)
References
76(3)
5 Crack detection 79(18)
5.1 Introduction
79(1)
5.2 Crack detection technique
80(7)
5.3 Performance evaluation under various conditions
87(6)
5.3.1 Test imagery
88(1)
5.3.2 Results
89(4)
5.4 Extracting physical properties of detected cracks
93(1)
5.5 Summary
93(1)
References
94(3)
6 Surface damage detection 97(30)
6.1 Introduction
97(1)
6.2 Types of damage encountered in marine environment
98(1)
6.3 Color based damage detection techniques
98(11)
6.3.1 Surface damage detection method
99(8)
6.3.1.1 Identification
102(1)
6.3.1.2 Clustering-based filtering
103(3)
6.3.1.3 Support vector machine enhancement
106(1)
6.3.2 Technique evaluation and comparison with other methods
107(2)
6.4 Texture analysis
109(12)
6.4.1 Methodology
109(20)
6.4.1.1 Texture characteristics map
109(2)
6.4.1.2 GLCM features
111(4)
6.4.1.3 Descriptive statistics and Shannon entropy
115(3)
6.4.1.4 Support vector machine classification
118(3)
6.5 Comparison of color and texture based methods
121(1)
6.6 Discussion and conclusion
122(1)
References
123(4)
7 3D Imaging 127(28)
7.1 Introduction
127(1)
7.2 Approaches for obtaining 3D information
128(1)
7.3 Stereo imaging
129(19)
7.3.1 Calibration
131(6)
7.3.1.1 Checkerboard-based calibration
132(4)
7.3.1.2 Self-calibration
136(1)
7.3.2 Rectification
137(2)
7.3.3 Stereo correspondence algorithm
139(17)
7.3.3.1 Matching cost computation
140(2)
7.3.3.2 Belief propagation on a Markov random field
142(6)
7.4 Triangulation
148(1)
7.5 Surface reconstruction
149(1)
7.6 Summary
150(1)
References
151(4)
8 Repository and interpretation 155(30)
8.1 Introduction
155(1)
8.2 Experimental set-up
156(11)
8.2.1 Contents of the repository
157(2)
8.2.2 Controlled and partially controlled images
159(1)
8.2.3 Damage type
159(4)
8.2.3.1 Cracks
159(1)
8.2.3.2 Surface damage
160(1)
8.2.3.3 Shape information
161(2)
8.2.4 Turbidity and lighting
163(1)
8.2.5 Surface type
164(3)
8.3 Online portal of ULTIR
167(1)
8.4 ROC-based performance evaluation of algorithms
168(14)
8.4.1 Crack detection
172(3)
8.4.2 Surface damage
175(3)
8.4.3 3D shape recovery using stereo vision
178(4)
8.5 Conclusions
182(1)
References
182(3)
9 Examples of future applications 185(22)
9.1 Introduction
185(1)
9.2 Integrating image data into subsequent analyses
186(6)
9.3 Virtual reality inspections and spherical image acquisition
192(4)
9.3.1 Spherical image acquisition
194(2)
9.4 Deep learning based damage detection
196(2)
9.5 Video analysis
198(5)
9.5.1 Equipment and set-up
199(1)
9.5.2 Video tracking technique
200(1)
9.5.3 Tracking challenges
200(1)
9.5.4 Results
201(1)
9.5.5 Smartphones
201(2)
9.6 Use of existing image archives from past inspections
203(2)
9.7 Summary
205(1)
References
206(1)
10 Conclusions 207(4)
10.1 Summary
207(1)
10.2 Limitations and future research directions
208(3)
Index 211
Dr. Michael OByrne (B.E., University College Cork Ireland) is a post-doctoral researcher in the School of Engineering at University College Cork, Ireland. His research interests are image based Non-Destructive Testing techniques for monitoring offshore structures. His doctorate investigated Automatic Detection of Damage using Image Based Techniques in Underwater Marine Structures. It involved using the latest research in image processing to detect and quantify damage that affects offshore structures, such as cracks, corrosion and bio-fouling. Currently, Dr. OByrne is developing new image processing techniques for infrastructure maintenance management and also looking at how underwater image processing based inspection can help estimating changes in hydrodynamic loads to structures due to bio-fouling.

Dr. Bidisha Ghosh, Assistant Professor, Trinity College Dublin, is an expert of statistical modelling, artificial intelligence techniques and data analysis. She applies these techniques to transportation networks, hydrological networks and infrastructure management. Her work in image processing relates to structural damage detection, infrastructure management, traffic monitoring, crash-barrier design and the development of a benchmark repository for such purposes.

Professor Franck Schoefs, from the University of Nantes, France, is a leading figure in the field of structural reliability and inspection-led maintenance management. He works on probabilistic modelling of inspections results and on site measurements from structural health monitoring. Major applications of his work are in bridge engineering, offshore structures and marine renewable energy. He is an expert of probabilistic modelling of marine growth on offshore structures.

Dr. Vikram Pakrashi is a Chartered Engineer and the director of Dynamical Systems and Risk Laboratory, School of Engineering, University College Cork. His research interests strongly feature infrastructure maintenance management and Structural Health Monitoring. Dr. Pakrashi has experience of inspecting, instrumenting and assessing numerous damaged structures at different levels of complexity and detail.