ROAD CRACK EXTRACTION WITH ADAPTED FILTERING AND MARKOV MODEL-BASED SEGMENTATION - Introduction and Validation

S. Chambon, C. Gourraud, J.-M. Moliard, P. Nicolle

2010

Abstract

The automatic detection of road cracks is important in a lot of countries to quantify the quality of road surfaces and to determine the national roads that have to be improved. Many methods have been proposed to automatically detect the defects of road surface and, in particular, cracks: with tools of mathematical morphology, neuron networks or multiscale filter. These last methods are the most appropriate ones and our work concerns the validation of a wavelet decomposition which is used as the initialisation of a segmentation based on Markovian modelling. Nowadays, there is no tool to compare and to evaluate precisely the peformances and the advantages of all the existing methods and to qualify the efficiency of a method compared to the state of the art. In consequence, the aim of this work is to validate our method and to describe how to set the parameters.

References

  1. Acosta, J., Adolfo, L., and Mullen, R. (1992). Low-Cost Video Image Processing System for Evaluating Pavement Surface Distress. TRR: Journal of the Transportation Research Board, 1348:63-72.
  2. Arbelaez, P., Fowlkes, C., and Malik, J. (2009). From contours to regions: An empirical evaluation. In Computer Vision and Pattern Recognition. To appear.
  3. Augereau, B., Tremblais, B., Khoudeir, M., and Legeay, V. (2001). A Differential Approach for Fissures Detection on Road Surface Images. In International Conference on Quality Control by Artificial Vision.
  4. Bray, J., Verma, B., Li, X., and He, W. (2006). A neural nework based technique for automatic classification of road cracks. In International Joint Conference on Neural Networks, pages 907-912.
  5. Chambon, S., Subirats, P., and Dumoulin, J. (2009). Introduction of a wavelet transform based on 2d matched filter in a markov random field for fine structure extraction: Application on road crack detection. In IS&T/SPIE Electronic Imaging - Image Processing: Machine Vision Applications II.
  6. Cheng, H., Chen, J., Glazier, C., and Hu, Y. (1999). Novel approach to pavement cracking detection based on fuzzy set theory. Journal of Computing in Civil Engineering, 13(4):270-280.
  7. Chou, J., O'Neill, W., and Cheng, H. (1995). Pavement distress evaluation using fuzzy logic and moment invariants. TRR: Journal of the Transportation Research Board, 1505:39-46.
  8. Delagnes, P. and Barba, D. (1995). A markov random field for rectilinear structure extraction in pavement distress image analysis. In International Conference on Image Processing, volume 1, pages 446-449.
  9. Elbehiery, H., Hefnawy, A., and Elewa, M. (2005). Surface defects detection for ceramic tiles using image processing and morphological techniques. Proceedings of World Academy of Science, Engineering and Technology (PWASET), 5:158-162.
  10. Fukuhara, T., Terada, K., Nagao, M., Kasahara, A., and Ichihashi, S. (1990). Automatic pavement-distresssurvey system. Journal of Transportation Engineering, 116(3):280-286.
  11. Iyer, S. and Sinha, S. (2005). A robust approach for automatic detection and segmentation of cracks in underground pipeline images. Image and Vision Computing, 23(10):921-933.
  12. Kaseko, M. and Ritchie, S. (1993). A neural network-based methodology for pavement crack detection and classification. Transportation Research Part C: Emerging Technologies information, 1(1):275-291.
  13. Koutsopoulos, H. and Downey, A. (1993). Primitive-based classification of pavement cracking images. Journal of Transportation Engineering, 119(3):402-418.
  14. Lee, H. and Oshima, H. (1994). New Crack-Imaging Procedure Using Spatial Autocorrelation Function. Journal of Transportation Engineering, 120(2):206-228.
  15. Petrou, M., Kittler, J., and Song, K. (1996). Automatic surface crack detection on textured materials. Journal of Materials Processing Technology, 56(1-4):158-167.
  16. Ritchie, S., Kaseko, M., and Bavarian, B. (1991). Development of an intelligent system for automated pavement evaluation. TRR: Journal of the Transportation Research Board, 1311:112-119.
  17. 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).
  18. Song, K., Petrou, M., and Kittler, J. (1992). Wigner based crack detection in textured images. In International Conference on Image Processing and its Applications, pages 315-318.
  19. Subirats, P., Fabre, O., Dumoulin, J., Legeay, V., and Barba, D. (2006). Automation of pavement surface crack detection with a matched filtering to define the mother wavelet function used. In European Signal Processing Conference, EUSIPCO.
  20. Tanaka, N. and Uematsu, K. (1998). A crack detection method in road surface images using morphology. In Machine Vision Applications, pages 154-157.
  21. Wang, K., Li, G., and Gong, W. (2007). Wavelet-based pavement distress image edge detection with ” trous” algorithm. In Transportation Research Record, Annual Meeting, volume 2024, pages 73-81.
  22. Zhou, J., Huang, P., and Chiang, F. (2005). Wavelet-based pavement distress classification. TRR: Journal of the Transportation Research Board, 1940:89-98.
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Paper Citation


in Harvard Style

Chambon S., Gourraud C., Moliard J. and 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 - Volume 2: VISAPP, (VISIGRAPP 2010) ISBN 978-989-674-029-0, pages 81-90. DOI: 10.5220/0002848800810090


in Bibtex Style

@conference{visapp10,
author={S. Chambon and C. Gourraud and J.-M. Moliard and P. Nicolle},
title={ROAD CRACK EXTRACTION WITH ADAPTED FILTERING AND MARKOV MODEL-BASED SEGMENTATION - Introduction and Validation},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010)},
year={2010},
pages={81-90},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002848800810090},
isbn={978-989-674-029-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010)
TI - ROAD CRACK EXTRACTION WITH ADAPTED FILTERING AND MARKOV MODEL-BASED SEGMENTATION - Introduction and Validation
SN - 978-989-674-029-0
AU - Chambon S.
AU - Gourraud C.
AU - Moliard J.
AU - Nicolle P.
PY - 2010
SP - 81
EP - 90
DO - 10.5220/0002848800810090