Geodesic
A robust approach for road pavement defects detection and classification
Journal of computational and engineering mathematics, Tome 3 (2016) no. 3, pp. 40-52.

Voir la notice de l'article provenant de la source Math-Net.Ru

The objective of this paper is to propose a robust approach to building a computer vision system to detect and classify pavement defects based on features, such as the contour of feature (chain code histogram, Hu-moment), the shape of an object (length, width, area). In this paper, we present a method to build an automated system to detect and classify the different types of defects such as rupture of the road edge, potholes, subsidence depressions based on image processing techniques and machine learning methods. That system includes the following steps. First step is to detect defect position (ROI) then the defect is described by its features. Finally, each defect is classified these different defect features. In our approach the following algorithms have been using: Markov Random Fields and Graph cuts method for image segmentation, Random Forests algorithm for data classification.
Keywords: feature extraction, defect pavement, defects detection, Markov random fields, graph cut, random forests, computer vision. 
@article{JCEM_2016_3_3_a4,
     author = {H. T. Nguyen and L. T. Nguyen and D. N. Sidorov},
     title = {A robust approach for road pavement defects detection and classification},
     journal = {Journal of computational and engineering mathematics},
     pages = {40--52},
     publisher = {mathdoc},
     volume = {3},
     number = {3},
     year = {2016},
     language = {en},
     url = {http://geodesic.mathdoc.fr/item/JCEM_2016_3_3_a4/}
}
TY  - JOUR
AU  - H. T. Nguyen
AU  - L. T. Nguyen
AU  - D. N. Sidorov
TI  - A robust approach for road pavement defects detection and classification
JO  - Journal of computational and engineering mathematics
PY  - 2016
SP  - 40
EP  - 52
VL  - 3
IS  - 3
PB  - mathdoc
UR  - http://geodesic.mathdoc.fr/item/JCEM_2016_3_3_a4/
LA  - en
ID  - JCEM_2016_3_3_a4
ER  - 
%0 Journal Article
%A H. T. Nguyen
%A L. T. Nguyen
%A D. N. Sidorov
%T A robust approach for road pavement defects detection and classification
%J Journal of computational and engineering mathematics
%D 2016
%P 40-52
%V 3
%N 3
%I mathdoc
%U http://geodesic.mathdoc.fr/item/JCEM_2016_3_3_a4/
%G en
%F JCEM_2016_3_3_a4
H. T. Nguyen; L. T. Nguyen; D. N. Sidorov. A robust approach for road pavement defects detection and classification. Journal of computational and engineering mathematics, Tome 3 (2016) no. 3, pp. 40-52. http://geodesic.mathdoc.fr/item/JCEM_2016_3_3_a4/

[1] A. B. Charles, K. Sauer, S. Saquib, “Markov Random Fields and Stochastic Image Models”, IEEE International Conference on Image Processing, International Conference (Washington, 1995, October 23–26), Washington, D.C., 1995

[2] J. Bray, B. Verma, X. Li, W. He, “Neural Network Based Technique for Automatic Classification of Road Cracks”, Proceeding of the International Joint Conference on Neural Networks, International Conference (Vancouver, 2006, July 16–21), Vancouver, BC, Canada, 2006, 907–912

[3] H. Chen, M. Miyojim, “Automatic Pavement Distress Detection System”, Information Sciences, 108:1-4 (1998), 219–240 | DOI

[4] Pavement Defects Dataset (accessed on 27 June 2016) https://yadi.sk/d/GEIaJHYOspQbR

[5] S. Wang, W. Tang, “Pavement Crack Segmentation Algorithm Based on Local Optimal Threshold of Cracks Density Distribution”, Proceedings of the Advanced Intelligent Computing, 7th International Conference ICIC (Zhengzhou, 2011, August 11–14), Springer, Zhengzhou, China, 2011, 298–302 | DOI

[6] C. Koch, I. Brilakis, “Pothole Detection in Asphalt Pavement Images”, Advanced Engineering Informatics, 25:3 (2011), 507–515 | DOI

[7] A. Toet, “A Morphological Pyramidal Image Decomposition”, Pattern Recog. Lett., 9 (1989), 255–261 | DOI | Zbl

[8] A. A. Lempert, D. N. Sidorov, A. V. Zhukov, “Ob odnom podkhode k optimizatsii remonta avtomobilnykh dorog v usloviyakh ogranichennogo finansirovaniya”, Obobschennye postanovki i resheniya zadach upravleniya, VII Mezhdunarodnyi simpozium (Gelendzhik, Divnomorskoe, 26–30 sentyabrya 2014), Fizmatlit, M., 2014, 114–118

[9] S. Sudakov, A. Semashko, O. Barinova, A. Konushin, V. Kinshakov, A. Krylov, “Algoritmy detektirovaniya razmetki i defektov dorozhnogo pokrytiya”, Graphicon 2008 Proceedings, Mezhdunarodnaya konferentsiya, 2008, 206–212

[10] K. Fernandes, L. Ciobanu, “Pavement Pathologies Classification Using Graph-Based Features”, IEEE International Conference on Image Processing (ICIP), International Conference ICIP (Paris, 2014, October 27–30), IEEE, Paris, France, 2014 | DOI

[11] B. S. Manjunath, Ma Wei-Ying, “Texture Features for Browsing and Retrieval of Image Data”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 18:8 (1996), 837–842 | DOI

[12] P. Delagnes, B. Dominique, “A Markov Random Fields for Rectilinear Structure Extraction in Pavement Distress Image Analysis”, International Conference (1995, January), International Conference on Image Processing, 1, 1995, 446–449 | DOI

[13] V. Kolmogorov, R. Zabih, “A Markov Random Fields for Rectilinear Structure Extraction in Pavement Distress Image Analysis”, 8th IEEE International Conference on Computer Vision, International Conference (2001, February), v. 2, 2001, 508–515 | DOI

[14] Y. Boykov, O. Veksler, Z. Ramin, “Fast Approximate Energy Minimization Via Graph Cuts”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 23:11 (2001), 1222–1239 | DOI

[15] L. Breiman, “Random Forests”, Machine Learning, 45:1 (2001), 5–32 | DOI | Zbl

[16] K. A. Ravindra, T. L. Magnanti, J. B. Orlin, Network Flows: Theory, Algorithms and Applications, Prentice Hall, 1993 | DOI | Zbl

[17] S. Geman, D. Geman, “Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 6:6 (1984), 721–741 | DOI | Zbl

[18] R language packet (accessed on 03 September 2016) https://www.r-project.org/