Automatic Road Defect Detection by Textural Pattern Recognition Based on AdaBoost

Computer-Aided Civil and Infrastructure Engineering

Volumn 27, Issue 4, 2012, Pages 244-259

  Cord, Aurélien ^a   Chambon, Sylvie ^b  

^a Gustave Eiffel University   (France)

^b UNIVERSITÉ GUSTAVE EIFFEL   (France)

Author keywords

[No Author keywords available]

Indexed keywords

DEDICATED ROAD; DEFECT DETECTION; DETECTION ALGORITHM; GROUND MOVEMENT; IMAGE DATABASE; METEOROLOGICAL CONDITION; NONLINEAR FILTER; ROAD IMAGES; ROAD SURFACES; SMALL SCALE; TASK-BASED; TEXTURAL INFORMATION; WHOLE PROCESS;

ADAPTIVE BOOSTING; ATMOSPHERIC MOVEMENTS; PATTERN RECOGNITION; ROADS AND STREETS; SUPERVISED LEARNING;

CRACKS;

ALGORITHM; GROUND MOVEMENT; IMAGE ANALYSIS; NONLINEARITY; ROAD; TEXTURE;

EID: 84858158920     PISSN: 10939687     EISSN: 14678667     Source Type: Journal    
DOI: 10.1111/j.1467-8667.2011.00736.x     Document Type: Article

References (43)

1. Abas, F. & Martinez, K. (2003), Classification of painting cracks for content-based analysis, in Proceedings of the SPIE, Annual Symposium Electronic Imaging: Machine Vision Applications in Industrial Inspection XI, Santa Clara, California, 5011, 149-60.
2. Acosta, J., Adolfo, L. & Mullen, R. (1992), Low-cost video image processing system for evaluating pavement surface distress, Journal of the Transportation Research Board (Transportation Research Record), 1348, 63-72.
3. Aubert, A., Jeulin, D. & Hashimoto, R. (2000), Surface texture classification from morphological transformations, in Proceedings of the ISMM 2000, Mathematical Morphology and Its Applications to Image and Signal Processing, Palo Alto, California, 253-52.
4. Augereau, B., Tremblais, B., Khoudeir, M. & Legeay, V. (2001), A differential approach for fissures detection on road surface images, in Proceedings of the International Conference on Quality Control by Artificial Vision, Le Creusot, France.
5. Boukouvalas, C., De Natale, F., De Toni, G., Kittler, J., Marik, R., Mirmehdi, M., Petrou, M., Le Roy, P., Salgari, R. & Vernazza, G. (1998), ASSIST: automatic system for surface inspection and sorting of tiles, Journal of Materials Processing Technology, 82(1), 179-88.
6. Bray, J., Verma, B., Li, X. & He, W. (2006), A neural network based technique for automatic classification of road cracks, in Proceedings of the International Joint Conference on Neural Networks, Vancouver, BC, Canada, 907-12.
7. Chambon, S. (2010), Detection of road cracks with multiple images, in Proceedings of the International Conference on Computer Vision Theory and Applications (VISAPP), Angers, France.
8. Chambon, S., Gourraud, C., Moliard, J.-M. & Nicolle, P. (2010), Road crack extraction with adapted filtering and Markov model-based segmentation-introduction and validation, in Proceedings of the International Conference on Computer Vision Theory and Applications (VISAPP), Angers, France.
9. Chaudhuri, S., Chatterjee, S., Katz, N., Nelson, M. & Goldbaum, M. (1989), Detection of blood vessels in retinal images using two-dimensional matched filters, IEEE Transactions on Medical Imaging, 8(3), 263-69.
10. Chou, J., O'Neill, W. & Cheng, H. (1995), Pavement distress evaluation using fuzzy logic and moment invariants, Journal of the Transportation Research Board (Transportation Research Record), 1505, 39-46.
11. Cord, A., Bach, F. & Jeulin, D. (2010), Texture classification by statistical learning from morphological image processing: application to metallic surfaces, Journal of Microscopy, 239(2), 159-66.
12. Corso, D., Fioravanti, R. & Fioravanti, S. (1995), Morphological analysis of textured images for identification of thin structures, in Proceedings of the International Conference on Acoustics, Speech, and Signal Processing, Detroit, Michigan, 4, 2359-62.
13. Elbehiery, H., Hefnawy, A. & Elewa, M. (2005), Surface defects detection for ceramic tiles using image processing and morphological techniques, World Academy of Science, Engineering and Technology, 5, 158-62.
14. Freeman, W. T. & Adelson, E. H. (1991), The design and use of steerable filters, IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(9), 891-906.
15. Freund, Y. & Schapire, R. E. (1997), A decision-theoretic generalization of on-line learning and an application to boosting, Journal of Computer and System Sciences, 55(1), 119-39.
16. Fricout, G. & Jeulin, D. (2003), Texture classification from their morphological properties, in Proceedings of the International Conference on Metrology and Properties of Engineering Surface, Suede, 42-55.
17. Geman, D. & Jedynak, B. (1996), An active testing model for tracking roads in satellite images, IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(1), 1-14.
18. Guyon, I. & Elisseeff, A. (2003), An introduction to variable and feature selection, Journal of Machine Learning Research, 3, 1157-82.
19. Hadizadeh, H. & Baradaran Shokouhi, S. (2010), Random texture defect detection using 1-d hidden Markov models based on local binary patterns, IEICE Transactions on Information and Systems, E91. D(7), 1937-45.
20. Hsu, C., Chen, C., Lee, C. & Huang, S. (2001), Airport pavement distress image classification using moment invariant neural network, in Proceedings of the Asian Conference on Remote Sensing, Singapore, 1, 216-20.
21. Iyer, S. & Sinha, S. (2005), A robust approach for automatic detection and segmentation of cracks in underground pipeline images, Image and Vision Computing, 23(10), 921-33.
22. Iyer, S. & Sinha, S. K. (2006), Segmentation of pipe images for crack detection in buried sewers, Computer-Aided Civil and Infrastructure Engineering, 21(6), 395-410.
23. Jitprasithsiri, S. & Lee, H. (1996), Development of a digital image processing algorithm to compute a unified crack index for Salt Lake City, in Proceedings of the Annual Meeting of Transportation Research Record, Washington DC.
24. Kaseko, M. & Ritchie, S. (1993), A neural network-based methodology for pavement crack detection and classification, Transportation Research Part C: Emerging Technologies, 1(1), 275-91.
25. Kirschke, K. & Velinsky, S. (1992), Histogram-based approach for automated pavement-crack sensing, ASCE, Journal of Transportation Engineering, 118(5), 700-10.
26. Kittler, J., Marik, R., Mirmehdi, M., Petrou, M. & Song, J. (1994), Detection of defects in colour texture surfaces, in Proceedings of the Machine Vision Applications, Kawasaki, Kanagawa, Japan, 558-67.
27. Koutsopoulos, H. & Downey, A. (1993), Primitive-based classification of pavement cracking images, Journal of Transportation Engineering, 119(3), 402-18.
28. Lajnef, N., Rhimi, M., Mhamdi, L., Chatti, K. & Faridazar, F. (2011), Toward an integrated smart sensing system and data interpretation techniques for pavement fatigue monitoring, Computer-Aided Civil and Infrastructure Engineering, 26(7), 513-23.
29. Lee, H. & Oshima, H. (1994), New crack-imaging procedure using spatial autocorrelation function, Journal of Transportation Engineering, 120(2), 206-28.
30. Oliveira, H. & Correia, P. L. (2009), Automatic road crack segmentation using entropy and image dynamic thresholding, in Proceedings of the 17th European Signal Processing Conference (EUSIPCO 2009), Glasgow, Scotland.
31. Petrou, M., Kittler, J. & Song, K. (1996), Automatic surface crack detection on textured materials, Journal of Materials Processing Technology, 56(1-4), 158-67.
32. Schmidt, B. (2003), Automated Pavement Cracking Assessment Equipment-State of the Art, Technical Report 320, Surface Characteristics Technical Committee of the World Road Association (PIARC).
33. Sivakumar, K. & Goutsias, J. (1997), Discrete morphological size distributions and densities: estimation techniques and applications, Journal of Electronic Imaging, 6(1), 31-53.
34. Tanaka, N. & Uematsu, K. (1998), A crack detection method in road surface images using morphology, in Proceedings of the Machine Vision Applications, Fujitsu Makuhari System Laboratory, Chiba, Japan, 154-57.
35. Umesha, P., Ravichandran, R. & Sivasubramanian, K. (2009), Crack detection and quantification in beams using wavelets, Computer-Aided Civil and Infrastructure Engineering, 24(8), 593-607.
36. VisTex (2002), Available at:, accessed December 2002.
37. Wang, K., Li, G. & Gong, W. (2007), Wavelet-based pavement distress image edge detection with "a trous" algorithm, Transportation Research Record: Journal of the Transportation Research Board, 2024, 73-81.
38. Xie, X. (2008), A review of recent advances in surface defect detection using texture analysis techniques, Electronic Letters on Computer Vision and Image Analysis, 7(3), 1-22.
39. Xie, X. & Mirmehdi, M. (2007), Texems: texture exemplars for defect detection on random textured surfaces, IEEE Transactions on Pattern Analysis and Machine Intelligence, 29, 1454-64.
40. Yang, C., Tsai, Y. & Wang, Z. (2009), Algorithm for spatial clustering of pavement segments, Computer-Aided Civil and Infrastructure Engineering, 24(2), 93-108.
41. Yin, T., Lam, H.-F. & Chow, H.-M. (2010), A bayesian probabilistic approach for crack characterization in plate structures, Computer-Aided Civil and Infrastructure Engineering, 25(5), 375-86.
42. Ying, L. & Salari, E. (2010), Beamlet transform-based technique for pavement crack detection and classification, Computer-Aided Civil and Infrastructure Engineering, 25(8), 572-80.
43. Zhou, J., Huang, P. & Chiang, F. (2005), Wavelet-based pavement distress classification, Journal of the Transportation Research Board (Transportation Research Record), 1940, 89-98.