Use of a Self-Organizing Map for Crack Detection in Highly Textured Pavement Images
Publication: Journal of Infrastructure Systems
Volume 21, Issue 3
Abstract
A study on using an unsupervised learning technique, called a self-organizing map (SOM) or Kohonen map, for the detection of road cracks from pavement images is described in this paper. The main focus is on highly textured road images that make the crack detection very difficult. Road images are split into smaller rectangular cells, and a representative data set is generated for each cell by analyzing image texture and color properties. Texture and color properties are combined with a Kohonen map to distinguish crack areas from the background. Using this technique, cracks are detected to a precision of 77%. The algorithm also resulted in a recall of 73% despite the background having very strong visual texture. The technique applied here shows a great deal of promise despite the images being captured in an uncontrolled environment devoid of state-of-the-art image-acquisition setups. The results are also benchmarked against an advanced algorithm reported in a recent research paper. The benchmarking shows that the proposed algorithm performs better in terms of reducing the false positives in crack detection.
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References
Aborisade, D. O., and Ojo, J. A. (2011). “Novel defect detection technique in random textured tiles.” Int. J. Sci. Eng. Res., 2(10), in press.
Aurenhammer, F. (1991). “Voronoi diagrams—A survey of a fundamental geometric data structure.” ACM Comput. Surv., 23(3), 345–405.
Chambon, S. (2011). “Detection of points of interest for geodesic contours: Application of road images for crack detection.” Int. Joint Conf. on Computer Vision Theory and Applications, VISAPP, France.
Chambon, S., and Moliard, J.-M. (2011). “Automatic road pavement assessment with image processing: Review and comparison.” Int. J. Geophys., 2011, 1–20.
Dong, L., Yu, G., Ogunbona, P., and Li, W. (2008). “An efficient iterative algorithm for image thresholding.” Pattern Recognit. Lett., 29(9), 1311–1316.
Engelbrecht, A. P. (2007). Computational intelligence: An introduction, Wiley, Sussex, England.
Ferne, B., Wright, A., and Pynn, J. (2003). “The development of HARRIS—A system for road surface condition monitoring at traffic speed.” TRL Annual Research Review 2002, Transport Research Laboratory (TRL), U.K., 39–49.
Haralick, R. M., Shanmugam, K., and Dinstein, I. (1973). “Textural features for image classification.” IEEE Trans. Syst. Man Cybern., 3(6), 610–621.
Huang, Y., and Xu, B. (2006). “Automatic inspection of pavement cracking distress.” J. Electron. Imaging, 15(1), 13–17.
Iivarinen, J., Pakkanen, J., and Rauhamaa, J. (2004). “A SOM-based system for web surface inspection.” Machine Vision Applications in Industrial Inspection XII, SPIE 2004 5303, 178–187.
Koua, E. L., and Kraak, M. J. (2004). “Geo-visualization to support the exploration of large health and demographic survey data.” Int. J. Health Geographics, 3(1), 12–13.
Koutsopoulos, H. N., and El Sanhouri, I. (1991). “Methods and algorithms for automated analysis of pavement images.”, Transportation Research Board, Washington, DC.
Lee, H. (1991). “Accuracy, precision, repeatability and compatibility of the Pavedex PAS 1 automated distress measuring device.”, Transportation Research Board, Washington, DC, 136–143.
Martins, L. A. O. (2010). “Automatic detection of surface defects on rolled steel using computer vision and artificial neural networks.” Proc., IECON 2010—36th Annual Conf. on IEEE Industrial Electronics Society, IEEE, U.K., 1081–1086.
Mathavan, S., Rahman, M., and Kamal, K. (2012). “Application of texture analysis and the Kohonen map for region segmentation of pavement images for crack detection.”, Transportation Research Board, Washington, DC, 150–157.
MATLAB [Computer software]. Cambridge, U.K., Mathworks.
Na, W., and Tao, W. (2012). “Proximal support vector machine based pavement image classification.” 5th Int. Conf. on Advanced Computational Intelligence, IEEE, U.K., 686–688.
Niskanen, M., Kauppinen, H., and Silven, O. (2002). “Real-time aspects of SOM-based visual surface inspection.” Proc., SPIE Machine Vision Applications in Industrial Inspection X, Vol. 4664, International Society for Optical Engineering, 123–134.
Oliveira, H., and Correia, P. L. (2008). “Supervised strategies for crack detection in images of road pavement flexible surfaces.” Proc., 16th European Signal Processing Conf. (EUSIPCO), Lausanne, Switzerland, 25–29.
Oliveira, H., and Correia, P. L. (2009). “Automatic road crack segmentation using entropy and image dynamic thresholding.” 17th European Signal Processing Conf. (EUSIPCO 2009), Glasgow, Scotland.
Portilla, J., Navarro, R., Nestares, O., and Tabernero, A. (1996). “Texture synthesis-by-analysis method based on a multiscale early-vision model.” Opt. Eng., 35(8), 2403–2417.
Rababaah, H. (2005). “Asphalt pavement crack classification: A comparative study of three AI approaches: Multilayer perceptron, genetic algorithms and self-organizing maps.” M.S. thesis, Indiana Univ. South Bend, IN.
Saar, T., and Talvik, O. (2010). “Automatic asphalt pavement crack classification using neural networks.” 12th Biennial Baltic Electronics Conf., IEEE, U.K., 345–348.
Sorncharean, S., and Phiphobmongkol, S. (2008). “Crack detection on asphalt surface image using enhanced grid cell analysis.” 4th IEEE Int. Symp. on Electronic Design, Test and Applications, IEEE, U.K.
Tolba, A. S., and Abu-Rezeq, A. N. (1997). “A self-organizing feature map for automated visual inspection of textile products.” Comput. Ind., 32(3), 319–333.
Tuceryan, M., and Jain, A. K. (1998). “Texture analysis.” The handbook of pattern recognition and computer vision, 2nd Ed., C. H. Chen, L. F. Pau, and P. S. P. Wang, eds., World Scientific Publishing, London, 207–248.
Valpola, H. (2000). “Bayesian ensemble learning for nonlinear factor analysis.” Ph.D. thesis, Helsinki Univ. of Technology, Finland.
Xie, X. (2008). “A review of recent advances in surface defect detection using texture analysis techniques.” Comput. Vision Image Anal., 7(3), 1–22.
Xu, G., Ma, J., Liu, F., and Niu, X. (2008). “Automatic recognition of pavement surface crack based on BP neural network.” Int. Conf. on Computer and Electrical Engineering, IEEE, U.K., 19–22.
Zakeri, H., Nejad, F. M., Fahimifar, A., Torshiz, A. D., and Zarandi, M. H. F. (2013). “A multi-stage expert system for classification of pavement cracking.” IFSA World Congress and NAFIPS Annual Meeting, IEEE, U.K., 1125–1130.
Zou, C., Cao, Y., Li, Q., Mao, Q., and Wang, S. (2012). “CrackTree: Automatic crack detection from pavement images.” Pattern Recognit. Lett., 33(3), 227–238.
Zuo, Y., Wang, G., and Zuo, C. (2013). “The segmentation algorithm for pavement cracking images based on the improved fuzzy clustering.” Appl. Mech. Mater., 319, 362–366.
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Published In
Journal of Infrastructure Systems
Volume 21 • Issue 3 • September 2015
Copyright
© 2014 American Society of Civil Engineers.
History
Received: Nov 18, 2013
Accepted: Sep 3, 2014
Published online: Nov 3, 2014
Discussion open until: Apr 3, 2015
Published in print: Sep 1, 2015
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