Journal of Korea Society of Digital Industry and Information Management (디지털산업정보학회논문지)

Korea Society of Digital Industry and Information Management (디지털산업정보학회)
권호 표지

Concrete crack detection using shape properties

형태의 특징을 이용한 콘크리트 균열 검출

  • 조범석 (명지전문대학 컴퓨터정보과) ;
  • 김영로 (명지전문대학 컴퓨터정보과)
  • Published : 2013.06.30
⟨ Previous Next ⟩

Abstract

In this paper, we propose a concrete crack detection method using shape properties. It is based on morphology algorithm and crack features. We assume that an input image is contaminated by various noises. Thus, we use a morphology operator and extract patterns of crack. It segments cracks and background using opening and closing operations. Morphology based segmentation is better than existing integration methods using subtraction in detecting a crack it has small width. Also, it is robust to noisy environment. The proposed algorithm classifies the segmented image into crack and background using shape properties of crack. This method calculates values of properties such as the number of pixels and the maximum length of the segmented region. Also, pixel counts of clusters are considered. We decide whether the segmented region belongs to cracks according to those data. Experimental results show that our proposed crack detection method has better results than those by existing detection methods.

Keywords

References

  1. A. Ito, Y. Aoki, and S. Hashimoto, "Accurate extraction and measurement of fine cracks," Processing of IECON'02, Nov. 2002, pp. 2202-2207.
  2. Y. Fujita, Y. Mitani, and Y. Hamamoto, "A method for crack detection on a concrete structure," ICPR'06, Hon Kong, Aug. 2006, pp. 901-904.
  3. N. Otsu, "A threshold selection method from gray-level histograms," IEEE Trans. Systems, Man, and Cybernetics, vol. SMC-9, no. 1, Jan. 1979, pp. 62-66.
  4. R. S. Adhikari, O. Moselhi, and A. Bagchi, "Image-based retrieval of concrete crack properties," Journal of the International Society for Gerontechnology, vol. 11, no. 2, 2012, pp. 315-321.
  5. Salembier, P, "Comparison of some morphological segmentation algorithms based on contrast enhancement. Application to automatic defect detection," Proc. of the EUSIPCO-90 Fifth European Signal Processing Conference, 1990, pp. 833-836.
  6. S. Iyer and S. K. Sinha, "A robust approach for automatic detection and segmentation of cracks in underground pipeline images," Image and Vision Computing, vol. 23, 2005, pp. 921-933. https://doi.org/10.1016/j.imavis.2005.05.017
  7. S. K. Sinha and P. W. Fieguth, "Segmentation of buried concrete pipe images," Automation in Construction, vol. 15, 2006, pp. 47-57. https://doi.org/10.1016/j.autcon.2005.02.007
  8. S. K. Sinha and P. W. Fieguth, "Automated detection of cracks in buried concrete pipe images," Automation in Construction, vol. 15, 2006, pp. 58-72. https://doi.org/10.1016/j.autcon.2005.02.006
  9. T. Yamaguchi, S. Nakamura, R. Saegusa, and S. Hashimoto, "Image based crack detection for real concrete surfaces," Trans. on Electrical and Electronic Engineering, vol. 3, 2008, pp. 128-135. https://doi.org/10.1002/tee.20244
  10. T. Yamaguchi and S. Hashimoto, "Practical image measurement of crack width for real concrete structure," Electronics and Communications in Japan, vol. 92, no. 10, 2009, pp. 605-614.
  11. S. -W. Cha and J. -H. Shin, "A study on removing the impulse noise using wavelet transformation in detail areas," KSDIM, vol. 84, no. 2, Jun. 2008, pp. 75-80.
  12. B. -S. Cho and Y. -R. Kim, "Gaussian noise estimation using adaptive filtering," KSDIM, vol. 8, no. 4, Dec. 2012, pp. 27-33.