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An Automatic Approach for Accurate Edge Detection of Concrete Crack Utilizing 2D Geometric Features of Crack

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Abstract

The automatic edge detection of cracks on concrete structures plays an important role in the damage assessment process for cracked structures. In this paper, we proposed an automatic method for accurate edge detection of concrete cracks from real 2D images of concrete surfaces containing noisy and unintended objects. In the 2D image of a damaged concrete surface, cracks are usually observed as tree-like topology dark objects of which the branches are line-like and have local symmetry across their center axes. We utilize these two geometric properties of cracks to detect crack edges and discriminate them with edges of other unintended objects. The novel automatic crack edge detection is composed of two sequential stages. In the first stage, cracks are enhanced by a novel phase symmetry-based crack enhancement filter (PSCEF) based on their symmetric and line-like properties while non-crack objects are removed. Estimated crack center-lines are then obtained by thresholding the filtered images and applying morphological thinning algorithm to the binary image. In the second stage, the estimated center lines of the detected cracks are fitted by cubic splines and the pixel intensity profiles in the directions perpendicular to the splines are used to determine the edge points. The edge points are linked together to form the desired continuous crack edges. Various experiments of real concrete crack images are used to demonstrate the excellent performance of the proposed method.

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References

  1. Hutchinson, T. C., & Chen, Z. (2006). Improved image analysis for evaluating concrete damage. Journal of Computing in Civil Engineering, 20(3), 210–216.

    Article  Google Scholar 

  2. Liu, Z. W., Suandi, S. A., Ohashi, T., & Ejima, T. (2002). A tunnel crack detection and classification system based on image processing. Machine Vision Applications in Industrial Inspection X, Proceedings of SPIE, 4664, 145–152.

    Article  Google Scholar 

  3. Ito, A., Aoki Y., Hashimoto, S. (2002). Accurate extraction and measurement of fine cracks from concrete block surface image. Proceedings of IEEE 28th Annual Conference of the Industrial Electronics Society. Sevilla, Spain. vol. 3, pp. 2202–2207.

  4. Yamaguchi, T., & Hashimoto, S. (2006). Automated Crack Detection For Concrete Surface Image Using Percolation Model and Edge Information. Proceedings of IECON 32nd Annual Conference on IEEE Industrial Electronics Society. Paris, France. pp. 3355–3360.

  5. De Schutter, G. (2002). Advanced monitoring of cracked structures using video microscope and automated image analysis. NDT & E International, 35(4), 209–212.

    Article  Google Scholar 

  6. Gonzalez, R., & Woods, R. (2002). Digital image processing (2nd ed.). New Jersey: Prentice-Hall.

    Google Scholar 

  7. Marr, D., & Hildreth, E. (1980). Theory of edge detection. Proceedings of the Royal Society of London, 207(1980), 187–217.

    Article  Google Scholar 

  8. Canny, J. (1986). A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8, 679–698.

    Article  Google Scholar 

  9. Rosenfeld, A. (1970) A Nonlinear Edge Detection Technique, Proc. IEEE. pp. 814–816.

  10. Demigny, D. (2002). On optimal linear filtering for edge detection. IEEE Transactions on Image Processing, 11, 728–1220.

    Article  Google Scholar 

  11. Black, M., Sapiro, G., Marimont, D., & Heeger, D. (1988). Robust anisotropic diffusion. IEEE Trans. on Image Processing, 7, 421–432.

    Article  Google Scholar 

  12. Jeong, H., & Kim, C. I. (1992). Adaptive determination of filter scales for edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(5), 579–585.

    Article  Google Scholar 

  13. Bao, P., Zhang, L., & Wu, X. (2005). Canny edge detection enhancement by scale multiplication. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(9), 1485–1490.

    Article  Google Scholar 

  14. Fujita, Y., & Hamamoto, Y. (2011). A robust automatic crack detection method from noisy concrete surfaces. Machine Vision and Applications, 22, 245–254.

    Article  Google Scholar 

  15. Yamaguchi, T., & Hashimoto, S. (2010). Fast crack detection method for large-size concrete surface images using percolation-based image processing. Machine Vision and Applications, 21, 797–809.

    Article  Google Scholar 

  16. Abdel-Qader, I., Abudayyeh, O., & Kelly, M. E. (2003). Analysis of edge detection techniques for crack identification in bridges. Journal of Computing in Civil Engineering, 17(3), 255–263.

    Article  Google Scholar 

  17. Lee, J. H., Lee, J. M., Kim, H. J., & Moon, Y. S. (2008). Machine vision system for automatic inspection of bridges. Congress Image Signal Process, 3, 363–366.

    Article  Google Scholar 

  18. Krissian, K., Malandain, G., Ayache, N., Vaillant, R., & Trousset, Y. (1998). Model based multi-scale detection of 3-D vessels. Proceedings of 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, DC, USA. pp. 722–727.

  19. Frangi, A. F., Niessen, W. J., Vincken, K. L., & Viergever, M. A. (1998). Multi-scale vessel enhancement filtering. Medical Image Computing Computer-Assisted Intervention-MICCAI’98. Heidelberg: Springer Berlin. pp. 130–137.

  20. Li, Q., Sone, S., & Doi, K. (2003). Selective enhancement filters for nodules, vessels, and airway walls in two-and three-dimensional CT scans. Medical Physics, 30, 20–40.

    Google Scholar 

  21. Kovesi, P. (1997). Symmetry and asymmetry from local phase. Processing on Tenth Australian Joint Conference on Artificial Intellegence. Perth, Australia. pp. 185–190.

  22. Iyer, S., & Sinha, S. K. (2005). A robust approach for automatic detection and segmentation of cracks in underground pipeline images. Image and Vision Computing, 23, 921–933.

    Article  Google Scholar 

  23. Gorry, P. A. (1990). General least-squares smoothing and differentiation by the convolution (savitzky-golay) method. Analytical Chemistry, 62, 570–573.

    Article  Google Scholar 

  24. Lindeberg, T. (1998). Edge detection and ridge detection with automatic scale selection. International Journal of Computer Vision, 30(2), 117–154.

    Article  Google Scholar 

  25. Bracewell, R. (1999). The Fourier transform and its applications (3rd ed., pp. 267–272). New York: McGraw-Hill.

    Google Scholar 

  26. Kovesi, P. (1999). Image features from phase congruency. VIDERE: A Journal of Computer Vision Research, 1(3), 1–26.

  27. Hacihaliloglu, I., Abugharbieh, R., Hodgson, A. J., & Rohling, R. N. (2011). Automatic adaptive parameterization in local phase feature-based bone segmentation in ultrasound. Ultrasound in Medicine and Biology, 37(10), 1689–1703.

    Google Scholar 

  28. Felsberg, M., & Sommer, G. (2001). The monogenic signal. IEEE Transactions on Signal Processing, 49(12), 3136–3144.

    Article  MathSciNet  Google Scholar 

  29. Fabbri, R., Costa, L. d. F., Torelli, J. C. & Bruno, O.M. (2008). 2d Euclidean Distance Transforms: A Comparative Survey. ACM Computing Surveys, 40(1), 1–44.

    Google Scholar 

  30. Farin, G. (1988). Curves and surfaces for computer aided geometric design. San Diego: Academic.

    MATH  Google Scholar 

  31. Epstein, M. P. (1976). On the influence of parametrization in parametric interpolation. SIAM Journal on Numerical Analysis, 13, 261–268.

    Article  MathSciNet  MATH  Google Scholar 

  32. Floater, M. S., & Surazhsky, T. (2006). Parameterization for curve interpolation. Studies in Computational Mathematics, 12, 39–54.

    Article  MathSciNet  Google Scholar 

  33. Kovesi, P. (2010). PHASECONG3 - Computes edge and corner phase congruency in an image. School of Computer Science and Software Engineering, the University of Western Australia. http://www.csse.uwa.edu.au/~pk/research/MatlabFns/PhaseCongruency/phasecong3.m. Accessed 20 January 2012.

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Authors and Affiliations

  1. Department of Mechanical Engineering, National Chiao Tung University, 1001 Ta Hsueh Road, Hsinchu City, 30010, Taiwan, Republic of China

    Hoang-Nam Nguyen, Tai-Yan Kam & Pi-Ying Cheng

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  1. Hoang-Nam Nguyen
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  2. Tai-Yan Kam
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  3. Pi-Ying Cheng
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Correspondence to Tai-Yan Kam.

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Nguyen, HN., Kam, TY. & Cheng, PY. An Automatic Approach for Accurate Edge Detection of Concrete Crack Utilizing 2D Geometric Features of Crack. J Sign Process Syst 77, 221–240 (2014). https://doi.org/10.1007/s11265-013-0813-8

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  • DOI: https://doi.org/10.1007/s11265-013-0813-8

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