Machine Vision and Applications

, Volume 21, Issue 5, pp 797–809

Fast crack detection method for large-size concrete surface images using percolation-based image processing

Short Paper

Abstract

The detection of cracks on concrete surfaces is the most important step during the inspection of concrete structures. Conventional crack detection methods are performed by experienced human inspectors who sketch crack patterns manually; however, such detection methods are expensive and subjective. Therefore, automated crack detection techniques that utilize image processing have been proposed. Although most the image-based approaches focus on the accuracy of crack detection, the computation time is also important for practical applications because the size of digital images has increased up to 10 megapixels. We introduce an efficient and high-speed crack detection method that employs percolation-based image processing. We propose termination- and skip-added procedures to reduce the computation time. The percolation process is terminated by calculating the circularity during the processing. Moreover, percolation processing can be skipped in subsequent pixels according to the circularity of neighboring pixels. The experimental result shows that the proposed approach efficiently reduces the computation cost.

Keywords

Crack detection Percolation Large-size image Concrete surface 

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References

  1. 1.
  2. 2.
  3. 3.
    Architectural Institute of Japan: Shrinkage Cracking in Reinforced Concrete Structures–Mechanisms and Practice of Crack Control, Architectural Institute of Japan, Tokyo (2003)Google Scholar
  4. 4.
    Dare P.M., Hanley H.B., Fraser C.S., Riedel B., Niemeier W.: An operational application of automatic feature extraction: the measurement of cracks in concrete structures. Photogram. Rec. 17(99), 453–464 (2002)CrossRefGoogle Scholar
  5. 5.
    Kawamura K., Miyamoto A., Nakamura H., Sato R.: Proposal of a crack pattern extraction method from digital images using an interactive genetic algorithm. Proc. Japan Soc. Civil Eng. 742, 115–131 (2003)Google Scholar
  6. 6.
    Abdel-Qader I., Abudayyeh O., Kelly M.E.: Analysis of edge detection techniques for crack identification in bridges. J. Comput. Civil Eng. 17(3), 255–263 (2003)CrossRefGoogle Scholar
  7. 7.
    Hutchinson T.C., Chen Z.: Improved image analysis for evaluating concrete damage. J. Comput. Civil Eng. 20(3), 210–216 (2006)CrossRefGoogle Scholar
  8. 8.
    Ito, A., Aoki, Y., Hashimoto, S.: Accurate extraction and measurement of fine cracks from concrete block surface image. In: Proceedings of IECON2002, pp. 77–82 (2002)Google Scholar
  9. 9.
    Tanaka, H., Yamada, M., Sato, R., Hashimoto, S.: Japanese Patent P2006–162477A (2006)Google Scholar
  10. 10.
    Takeda, H., Koyama, S., Horiguchi, K., Maruya, T.: Using image analysis and wavelet transform to detect cracks in concrete structures. Report of Taise Technology Center, No. 39, pp. 25 (2006)Google Scholar
  11. 11.
    Iyer S., Sinha S.K.: Segmentation of pipe images for crack detection in buried sewers. Comput. Aided Civil Infrastruct. Eng. 21(6), 395–410 (2006)CrossRefGoogle Scholar
  12. 12.
    Miwa M., Kobayashi T., Zhang X., Sato M.: Detecting cracks on the tunnel wall using watershed and graph analysis. ITE Tech. Rep. 29(59), 11–14 (2005)Google Scholar
  13. 13.
    Roli F.: Measure of texture anisotropy for crack detection on textured surfaces. Electron. Lett. 32(14), 1274–1275 (1996)CrossRefGoogle Scholar
  14. 14.
    Hatada T., Saitoh F.: Crack detection method for drain by using directional smoothing. IEEJ Trans. EIS 127(2), 241–246 (2007)CrossRefGoogle Scholar
  15. 15.
    Fujita, Y., Mitani, Y., Hamamoto, Y.: A method for crack detection on a concrete structure. In: Proceedings of the 18th International Conference on Pattern Recognition, vol. 3, pp. 901–904 (2006)Google Scholar
  16. 16.
    Yamaguchi T., Hashimoto S.: Image processing based on percolation model. IEICE Trans. Inf. Syst. E89-D(7), 2044–2052 (2006)CrossRefGoogle Scholar
  17. 17.
    VanderBrug G.J., Rosenfeld A.: Two-stage template matching. IEEE Trans. Comput. C-26(4), 384–393 (1977)CrossRefGoogle Scholar
  18. 18.
    Tanimoto S.L.: Template matching in pyramids. Comput. Graph. Image Process. 16(4), 356–369 (1981)CrossRefGoogle Scholar
  19. 19.
    Barnea D.I., Silverman H.F.: A class of algorithm for fast digital image registration. IEEE Trans. Comput. C-21, 179–186 (1972)CrossRefGoogle Scholar
  20. 20.
    Murase H., Vinod V.V.: Fast visual search using focused color matching—active search. IEICE Trans. Inf. Syst. J81-DII(9), 2035–2042 (1998)Google Scholar
  21. 21.
    Vinod V.V., Murase H.: Focused color intersection with efficient searching for object extraction. Pattern Recognit. 30(10), 1787–1797 (1997)CrossRefGoogle Scholar
  22. 22.
    Kawanishi, T., Kurozumi, T., Kashino, K., Takagi, S.: A fast template matching algorithm with adaptive skipping using inner-subtemplates distances. In: Proceedings of International Conference on Pattern Recognition, vol. 3, pp. 654–657 (2004)Google Scholar
  23. 23.
    Yamaguchi T., Nakamura S., Hashimoto S.: An efficient crack detection method using percolation-based image processing. Proc. IEEE Conf. Ind. Electron. Appl. 3, 1875–1880 (2008)Google Scholar
  24. 24.
    Stauffer D.: Introduction to Percolation Theory. 2nd edn. CRC press, New York (1994)Google Scholar
  25. 25.
    Grimmett G.: Percolation. Springer, Berlin (1999)MATHGoogle Scholar

Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  1. 1.Department of Applied Physics, School of Science and EngineeringWaseda UniversityTokyoJapan

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