Adaptive vision-based crack detection using 3D scene reconstruction for condition assessment of structures

Automation in Construction

Volumn 22, Issue , 2012, Pages 567-576

  Jahanshahi, Mohammad R ^a   Masri, Sami F ^a  

^a University of Southern California   (United States)

Author keywords

3D scene reconstruction; Computer vision; Crack detection; Image processing; Morphological operation; Pattern classification

Indexed keywords

3D SCENE RECONSTRUCTION; CONDITION ASSESSMENTS; CRACK EDGES; CURRENT MONITORING; DEPTH PARAMETERS; DIGITAL IMAGE; EDGE-BASED APPROACH; EXPERIMENTAL TEST; INSPECTION STANDARDS; MORPHOLOGICAL OPERATIONS; ROBOTIC SYSTEMS; TARGET STRUCTURE; VISION BASED;

COMPUTER VISION; CRACK DETECTION; CRACKS; DAMAGE DETECTION; DEPTH PERCEPTION; IMAGE PROCESSING; MATHEMATICAL MORPHOLOGY; PATTERN RECOGNITION;

THREE DIMENSIONAL;

EID: 84857356877     PISSN: 09265805     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.autcon.2011.11.018     Document Type: Conference Paper

References (45)

1. K.P. Chong, N.J. Carino, and G. Washer Health monitoring of civil infrastructures Smart Materials and Structures 12 3 2003 483 493
2. A. McCrea, D. Chamberlain, and R. Navon Automated inspection and restoration of steel bridges - a critical review of methods and enabling technologies Automation in Construction 11 4 2002 351 373
3. J.M. Coffey Non-destructive testing-the technology of measuring defects CEGB Research 21 1988 36 47
4. S.K. Ho, R.M. White, and J. Lucas A vision system for automated crack detection in welds Measurement Science and Technology 1 3 1990 287 294
5. R.C. Gonzalez, R.E. Woods, and S.L. Eddins Digital Image Processing Using MATLAB 2004 Prentice Hall Upper Saddle River, NJ 0-130-08519-7
6. R.O. Duda, and P.E. Hart Pattern Classification and Scene Analysis 1973 John Wiley & Sons New York 0-471-22361-1
7. S. Tsao, N. Kehtarnavaz, P. Chan, and R. Lytton Image-based expert-system approach to distress detection on CRC pavement Journal of Transportation Engineering 120 1 1994 62 64
8. M.S. Kaseko, Z.-P. Lo, and S.G. Ritchie Comparison of traditional and neural classifiers for pavement-crack detection Journal of Transportation Engineering 120 4 1994 552 569
9. K.C. Wang, S. Nallamothu, and R.P. Elliott Classification of pavement surface distress with an embedded neural net chip Artificial Neural Networks for Civil Engineers: Advanced Features and Applications 1998 ASCE 131 161
10. M. Siegel, and P. Gunatilake Remote enhanced visual inspection of aircraft by a mobile robot IEEE Workshop on Emerging Technologies, Intelligent Measurement and Virtual Systems for Instrumentation and Measurement May 1998 ETIMVIS'98 st. Paul, MN, USA
11. L. Prasad, and S. Iyenger Wavelet Analysis with Applications to Image Procesing 1997 CRC Press LLC Boca Raton, FL 0-849-33169-2
12. J.-P. Abtoine, R. Murenzi, P. Vandergheynst, and S.T. Ali Two-Dimensional Wavelets and Their Relatives 2004 Cambridge University Press United Kingdom 0-521-62406-1
13. R.O. Duda, P.E. Hart, and D.G. Stork Pattern Classification 2nd Edition 2001 Wiley New York
14. M. Nieniewski, L. Chmielewski, and Sklodowski Jozwik Morphological detection and feature-based classification of cracked regions in ferrites Machine GRAPHICS and VISION 8 4 1999 699 712
15. P. Salembier Comparison of some morphological segmentation algorithms based on contrast enhancement application to automatic defect detection Proceedings of the EUSIPCO-90 - Fifth European Signal Processing Conference 1990 833 836
16. O. Moselhi, and T. Shehab-Eldeen Classification of defects in sewer pipes using neural networks Journal of Infrastructure Systems 6 3 2000 97 104
17. M. J. Chae, Automated interpretation and assessment of sewer pipeline, Ph.D. thesis, Purdue University (December 2001).
18. W. Benning, S. Görtz, J. Lange, R. Schwermann, and R. Chudoba Development of an algorithm for automatic analysis of deformation of reinforced concrete structures using photogrammetry VDI Berichte 1757 2003 411 418
19. I. Abdel-Qader, O. Abudayyeh, and M.E. Kelly Analysis of edge-detection techniques for crack identification in bridges Journal of Computing in Civil Engineering 17 4 2003 255 263
20. K. Alageel, and I. Abdel-Qader Harr Transform Use in Image Processing, Tech. rep 2002 Department of Electrical and Computer Engineering, Western Michigan University Kalamazoo, Michigan
21. G. Bachmann, L. Narici, and E. Beckenstein Fourier and Wavelet Analysis 2000 Springer New York, NY
22. S.K. Sinha, P.W. Fieguth, and M.A. Polak Computer vision techniques for automatic structural assessment of underground pipes Computer-Aided Civil and Infrastructure Engineering 18 2 2003 95 112
23. I. Giakoumis, N. Nikolaidis, and I. Pitas Digital image processing techniques for the detection and removal of cracks in digitized paintings IEEE Transactions on Image Processing 15 1 2006 178 188
24. S.K. Sinha, and P.W. Fieguth Morphological segmentation and classification of underground pipe images Machine Vision and Applications 17 1 2006 21 31
25. I. Abdel-Qader, S. Pashaie-Rad, O. Abudayyeh, and S. Yehia PCA-based algorithm for unsupervised bridge crack detection Advances in Engineering Software 37 12 2006 771 778
26. S.-N. Yu, J.-H. Jang, and C.-S. Han Auto inspection system using a mobile robot for detecting concrete cracks in a tunnel Automation in Construction 16 3 2007 255 261
27. T. Yamaguchi, and S. Hashimoto Automated crack detection for concrete surface image using percolation model and edge information 32nd Annual Conference on IEEE Industrial Electronics 2006 3355 3360
28. L.-C. Chen, Y.-C. Shao, H.-H. Jan, C.-W. Huang, and Y.-M. Tien Measuring system for cracks in concrete using multitemporal images Journal of Surveying Engineering 132 2 2006 77 82
29. Y. Fujita, and Y. Hamamoto A robust method for automatically detecting cracks on noisy concrete surfaces Next-Generation Applied Intelligence Twenty-second International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems IEA/AIE 2009 2009 76 85 Tainan, Taiwan.
30. M.R. Jahanshahi, J.S. Kelly, S.F. Masri, and G.S. Sukhatme A survey and evaluation of promising approaches for automatic image-based defect detection of bridge structures Structure and Infrastructure Engineering 5 6 2009 455 486 10.1080/15732470801945930
31. W. Guo, L. Soibelman, and J.H. Garrett JR Automated defect detection for sewer pipeline inspection and condition assessment Automation in Construction 18 5 2009 587 596 http://dx.doi.org/10.1016/j.autcon.2008.12.003
32. W. Guo, L. Soibelman, and J.H. Garrett JR Visual pattern recognition supporting defect reporting and condition assessment of wastewater collection systems Journal of Computing in Civil Engineering 23 3 2009 160 169
33. I. Brilakis, S. German, Z. Zhu, Visual pattern recognition models for remote sensing of civil infrastructure, Journal of Computing in Civil Engineering 25 (5).
34. Z. Zhu, S. German, I. Brilakis, http://www.sciencedirect.com/science/ article/pii/S0926580511000318Vi sual retrieval of concrete crack properties for automated post-earthquake structural safety evaluation, Automation in Construction 20 (7) (2011) 874-883. http://dx.doi.org/10.1016/j.autcon.2011.03. 004 doi:10.1016/j.autcon.2011.03.004. URL http://www.sciencedirect.com/science/ article/pii/S09265 80511000318
35. K. N. Snavely, Scene reconstruction and visualization from internet photo collections, Ph.D. thesis, University of Washington, Seattle, Washington, USA (2008).
36. R.A. Fisher The use of multiple measurements in taxonomic problems Annals of Eugenics 7 1936 179 188
37. I. Brilakis, H. Fathi, A. Rashidi, http://www.sciencedirect.com/science/ article/pii/S092658051100032X Progressive 3d reconstruction of infrastructure with videogrammetry, Automation in Construction 20 (7) (2011) 884-895. http://dx.doi.org/10.1016/j.autcon.2011.03.005 doi:10.1016/j.autcon.2011.03.005. URL http://www.sciencedirect.com/science/article/pii/S09265 8051100032X
38. A. Rashidi, H. Fathi, and I. Brilakis Innovative stereo vision-based approach to generate dense depth map of transportation infrastructure, transportation research record Journal of the Transportation Research Board 2215 2011 93 99
39. N. Snavely, S.M. Seitz, and R. Szeliski Photo tourism: exploring photo collections in 3d SIGGRAPH Conference Proceedings 2006 ACM Press New York, NY, USA 835 846
40. D.G. Lowe Distinctive image features from scale-invariant keypoints International Journal of Computer Vision 60 2 2004 91 110
41. M. A. Fischler, R. C. Bolles, Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography, Commun. ACM 24 (6) (1981) 381-395. http://dx.doi.org/http://doi.acm.org/10.1145/ 358669.358692 doi: http://doi.acm.org/10.1145/358669.358692.
42. J.Y. Bouguet Camera calibration toolbox for matlab 2008 http://www.vision.caltech.edu/bouguetj/calibdoc/index.html
43. H. Minkowski Volumen und oberfläche Mathematische Annalen 57 4 1903 447 495
44. G. Matheron Random Sets and Integral Geometry 1975 Wiley New York
45. N. Otsu A threshold selection method from gray-level histogrmas IEEE Transactions on Systems, Man, and Cybernetics 9 1 1979 62 66