Automatic Pavement-Crack Detection and Segmentation Based on Steerable Matched Filtering and an Active Contour Model
Publication: Journal of Computing in Civil Engineering
Volume 31, Issue 5
Abstract
Cracks are an important symptom of pavement deterioration and deficiency. Accurate and complete information regarding pavement cracks is critical to determining pavement maintenance schedules, methods, and budgets. Two-dimensional (2D) pavement images are used in practice for crack detection and segmentation. Automatic crack detection and segmentation based on 2D images are challenging because of (1) low contrast between cracks and surrounding pavement; (2) complicated patterns of cracks; and (3) intensity inhomogeneity along cracks. To address these challenges, this paper presents a novel method to automatically detect and segment pavement cracks from 2D images. Specifically, the proposed method starts with the use of a steerable matched filter to generate a crack saliency map, which enhances the contrast between cracks and surrounding pavement and captures crack discontinuity and curvature. Analysis of the crack saliency map leads to a coarse crack region and rough estimates of crack properties. The coarse crack region is then fed into a region-based active contour model, and a level set evolution method is employed to implement the model for crack segmentation. The estimated crack properties provide information to automatically adjust the parameters of the active contour model for effective and efficient crack segmentation. The proposed method was tested using 65 pavement images with various cracks. The proposed method achieved average precision of 92.6%, recall of 85.1%, and F-measure of 88.7%.
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Acknowledgments
This research was funded by the National Science Foundation (NSF) via Grant CMMI Nos. 1265895 and 1462638. The authors gratefully acknowledge NSF’s support. Any opinions, findings, conclusions, and recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of NSF, Purdue University, and the Georgia Institute of Technology.
References
Alekseychuk, O. (2006). “Detection of crack-like indications in digital radiography by global optimisation of a probabilistic estimation function.” Ph.D. thesis, Bundesanstalt fuer Materialforschung und -pruefung, Berlin.
ASCE. (2013). 2013 report card for America’s infrastructure, Washington, DC, 1–74.
Cord, A., and Chambon, S. (2012). “Automatic road defect detection by textural pattern recognition based on AdaBoost.” Comput. Aided Civil Infrastruct. Eng., 27(4), 244–259.
FHWA (Federal Highway Administration). (2015). “National performance management measures; assessing pavement condition for the National Highway Performance Program and bridge condition for the National Highway Performance Program.” Federal Register, 80(2), 326–393.
Freeman, W. T., and Adelson, E. H. (1991). “The design and use of steerable filters.” IEEE Trans. Pattern Anal. Mach. Intell., 13(9), 891–906.
Gavilán, M., et al. (2011). “Adaptive road crack detection system by pavement classification.” Sensors, 11(10), 9628–9657.
Guan, H., et al. (2015). “Iterative tensor voting for pavement crack extraction using mobile laser scanning data.” IEEE Trans. Geosci. Remote Sens., 53(3), 1527–1537.
Huang, J., Liu, W., and Sun, X. (2014). “A pavement crack detection method combining 2D with 3D information based on Dempster-Shafer theory.” J. Comput. Aided Civil Infrastruct. Eng., 29(4), 299–313.
Huang, Y., and Xu, B. (2006). “Automatic inspection of pavement cracking distress.” J. Electr. Imaging, 15(1), 013017.
Jacob, M., and Unser, M. (2004). “Design of steerable filters for feature detection using canny-like criteria.” IEEE Trans. Pattern Anal. Mach. Intell., 26(8), 1007–1019.
Jiang, C., and Tsai, Y. J. (2015). “Enhanced crack segmentation algorithm using 3D pavement data.” J. Comput. Civil Eng., 04015050.
Kamaliardakani, M., Sun, L., and Ardakani, M. K. (2014). “Sealed-crack detection algorithm using heuristic thresholding approach.” J. Comput. Civil Eng., 30(1), 04014110.
Koch, C., Jog, G. M., and Brilakis, I. (2012). “Automated pothole distress assessment using asphalt pavement video data.” J. Comput. Civil Eng., 27(4), 370–378.
Li, C., Kao, C. Y., Gore, J. C., and Ding, Z. (2008). “Minimization of region-scalable fitting energy for image segmentation.” IEEE Trans. Image Process., 17(10), 1940–1949.
Li, G., He, S., Ju, Y., and Du, K. (2014). “Long-distance precision inspection method for bridge cracks with image processing.” Autom. Constr., 41, 83–95.
Li, Q., and Liu, X. (2008). “Novel approach to pavement image segmentation based on neighboring difference histogram method.” Proc., Congress on Image and Signal Processing, IEEE, New York, 792–796.
Li, Q., Zou, Q., Zhang, D., and Mao, Q. (2011). “FoSA: F* seed-growing approach for crack-line detection from pavement images.” Image Vision Comput., 29(12), 861–872.
Lins, R. G., and Givigi, S. N. (2016). “Automatic crack detection and measurement based on image analysis.” IEEE Trans. Instrum. Meas., 65(3), 1–8.
McGhee, K. H. (2004). “Automated pavement distress collection techniques.” National Cooperative Highway Research Program Synthesis 334, Transportation Research Board, Washington, DC.
Mukherjee, S., and Acton, S. T. (2015). “Oriented filters for vessel contrast enhancement with local directional evidence.” Proc., IEEE 12th Int. Symp. on Biomedical Imaging, IEEE, New York, 503–506.
Nejad, F. M., and Zakeri, H. (2011). “An optimum feature extraction method based on wavelet–radon transform and dynamic neural network for pavement distress classification.” Expert Syst. Appl., 38(8), 9442–9460.
Nguyen, H. N., Kam, T. Y., and Cheng, P. Y. (2014). “An automatic approach for accurate edge detection of concrete crack utilizing 2D geometric features of crack.” J. Signal Process. Syst., 77(3), 221–240.
Nishikawa, T., Yoshida, J., Sugiyama, T., and Fujino, Y. (2012). “Concrete crack detection by multiple sequential image filtering.” Comput. Aided Civil Infrastruct. Eng., 27(1), 29–47.
Oliveira, H., and Correia, P. L. (2013). “Automatic road crack detection and characterization.” IEEE Trans. Intell. Transp. Syst., 14(1), 155–168.
Oliveira, H., and Lobato Correia, P. (2009). “Automatic road crack segmentation using entropy and image dynamic thresholding.” Proc., 17th European Signal Processing Conf., IEEE, New York, 622–626.
Osher, S., and Sethian, J. A. (1988). “Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations.” J. Comput. Phys., 79(1), 12–49.
Otsu, N. (1975). “A threshold selection method from gray-level histograms.” Automatica, 11(285–296), 23–27.
Ouma, Y. O., and Hahn, M. (2016). “Wavelet-morphology based detection of incipient linear cracks in asphalt pavements from RGB camera imagery and classification using circular radon transform.” Adv. Eng. Inf., 30(3), 481–499.
Prasanna, P., et al. (2016). “Automated crack detection on concrete bridges.” IEEE Trans. Autom. Sci. Eng., 13(2), 591–599.
Radopoulou, S. C., and Brilakis, I. (2016). “Automated detection of multiple pavement defects.” J. Comput. Civil Eng., 04016057.
Sun, L., Kamaliardakani, M., and Zhang, Y. (2015). “Weighted neighborhood pixels segmentation method for automated detection of cracks on pavement surface images.” J. Comput. Civil Eng., 04015021.
Tsai, Y. C., Jiang, C., and Huang, Y. (2012). “Multiscale crack fundamental element model for real-world pavement crack classification.” J. Comput. Civil Eng., 28(4), 04014012.
Tsai, Y. C., Kaul, V., and Mersereau, R. M. (2009). “Critical assessment of pavement distress segmentation methods.” J. Transp. Eng., 136(1), 11–19.
Tsai, Y. C. J., and Li, F. (2012). “Critical assessment of detecting asphalt pavement cracks under different lighting and low intensity contrast conditions using emerging 3D laser technology.” J. Transp. Eng., 138(5), 649–656.
Yeum, C. M., and Dyke, S. J. (2015). “Vision-based automated crack detection for bridge inspection.” Comput. Aided Civil Infrastruct. Eng., 30(10), 759–770.
Ying, L., and Salari, E. (2010). “Beamlet transform-based technique for pavement crack detection and classification.” Comput. Aided Civil Infrastruct. Eng., 25(8), 572–580.
Zalama, E., Gómez-García-Bermejo, J., Medina, R., and Llamas, J. (2014). “Road crack detection using visual features extracted by Gabor filters.” Comput. Aided Civil Infrastruct. Eng., 29(5), 342–358.
Zhou, J., Huang, P. S., and Chiang, F. P. (2006). “Wavelet-based pavement distress detection and evaluation.” Opt. Eng., 45(2), 027007.
Zou, Q., Cao, Y., Li, Q., Mao, Q., and Wang, S. (2012). “CrackTree: Automatic crack detection from pavement images.” Pattern Recognit. Lett., 33(3), 227–238.
Information & Authors
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Published In
Journal of Computing in Civil Engineering
Volume 31 • Issue 5 • September 2017
Copyright
©2017 American Society of Civil Engineers.
History
Received: Nov 30, 2016
Accepted: Feb 28, 2017
Published online: May 29, 2017
Published in print: Sep 1, 2017
Discussion open until: Oct 29, 2017
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