Multi-level clustering algorithm for crack detection of concrete surface

DONG An-guo; ZHANG Xian-yan; XUE Hong-zhi; SONG Jun

Volume 13 Issue 6

Turn off MathJax

Article Contents

Article Navigation > Journal of Traffic and Transportation Engineering > 2013 > 13(6): 7-13

Next Previous

DONG An-guo, ZHANG Xian-yan, XUE Hong-zhi, SONG Jun. Multi-level clustering algorithm for crack detection of concrete surface[J]. Journal of Traffic and Transportation Engineering, 2013, 13(6): 7-13.

Citation:

DONG An-guo, ZHANG Xian-yan, XUE Hong-zhi, SONG Jun. Multi-level clustering algorithm for crack detection of concrete surface[J]. Journal of Traffic and Transportation Engineering, 2013, 13(6): 7-13.

DONG An-guo, ZHANG Xian-yan, XUE Hong-zhi, SONG Jun. Multi-level clustering algorithm for crack detection of concrete surface[J]. Journal of Traffic and Transportation Engineering, 2013, 13(6): 7-13.

Citation:

PDF( 577 KB)

Multi-level clustering algorithm for crack detection of concrete surface

School of Science, Chang'an University, Xi'an 710064, Shaanxi, China

More Information

Author Bio:
DONG An-guo(1964-), male, professor, +86-29-82334890, donganguo@chd.edu.cn
Received Date: 2013-07-01
Publish Date: 2013-12-25

Abstract

Abstract

In order to detect the crack and its width of concrete surface, the k-means clustering was applied for crack digital image, and binary image was got based on taking out entire suspected crack pixels from clustering results. The connected components of binary image were extracted according to the ubiety of pixels, the distance function of connected components was constructed considering connected components as clustering objects. Connected components could be clustered by spectral clustering algorithm, pseudo cracks were removed on the basis of crack features, and whole crack image was obtained. Numerical calculations of crack width were carried out twice by local rotation algorithm. Research result shows that multi-level clustering algorithm can get rid of more noises during extracting crack, and has stronger anti-noise ability compared with Canny operator and Sobel operator. When the crack width is calculated by local rotation algorithm, the average relative errors of calculated value and actual value are 3.86%and 2.40% respectively, so the algorithm has high accuracy and can be used for width calculations of all kinds of cracks.
- pavement engineering,
- crack detection,
- image processing,
- k-means clustering,
- spectral clustering

FullText(HTML)

References(14)

References

[1]	BHANDARKAR S M, LUO Xing-zhi, DANIELS R, et al. Detection of cracks in computer tomography images of logs[J]. Pattern Recognition Letters, 2005, 26(14): 2282-2294. doi: 10.1016/j.patrec.2005.04.004
[2]	CHENG H D, SHI X J, GLAZIER C. Real-time image thresholding based on sample space reduction and interpolation approach[J]. Journal of Computing in Civil Engineering, 2003, 17(4): 264-272. doi: 10.1061/(ASCE)0887-3801(2003)17:4(264)
[3]	ZOU Qin, LI Qing-quan, MAO Qing-zhou, et al. Targetpoints MST for pavement crack detection[J]. Geomatics and Information Science of Wuhan University, 2011, 36(1): 71-75. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-WHCH201101017.htm
[4]	WANG Xing-jian, QIN Guo-feng, ZHAO Hui-li. Pavement crack detection method based on multilevel denosing model[J]. Journal of Computer Applications, 2010, 30(6): 1606-1609, 1612. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-JSJY201006051.htm
[5]	DUAN Yue-hua, ZHANG Xiao-ning, LI Zhi, et al. Methods about digital representation on surface profile of concrete aggregates from 2-D to 3-D based on X-ray computed tomography[J]. China Journal of Highway and Transport, 2011, 24(6): 9-15. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201106004.htm
[6]	WAN Cheng, ZHANG Xiao-ning, HE Ling-feng, et al. Numerical prediction method for dynamic modulus of asphalt mixture[J]. China Journal of Highway and Transport, 2012, 25(4): 16-28. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201204003.htm
[7]	ABDEL-QADER I, ABUDAYYEH O, KELLY M E. Analysis of edge-detection techniques for crack identification in bridges[J]. Journal of Computing in Civil Engineering, 2003, 17(4): 255-263.
[8]	ROSALES M B, FILIPICH C P, BUEZAS F S. Crack detection in beam-like structures[J]. Engineering Structures, 2009, 31(10): 2257-2264.
[9]	ZHANG Hong, DONG An-guo, XU Zhi-gang, et al. Seeds moving algorithm and its application on bridge crack detection[C]//IEEE. 2010 3rd International Conference on Power Electronics and Intelligent Transportation System. Shenzhen: IEEE, 2010: 270-274. (in Chinese).
[10]	ZHA Xu-dong, WANG Wen-qiang. The testing method of crack width for continuously reinforced concrete pavement based on image processing technique[J]. Journal of Changsha University of Science and Technology: Natural Science, 2007, 4(1): 13-17. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-HNQG200701002.htm
[11]	LU Xiao-xia. Concrete crack width detection technology based on image processing[D]. Chengdu: University of Electronic Science and Technology of China, 2010. (in Chinese).
[12]	MACQUEEN J. Some methods for classification and analysis of multivariate observations[C]//University of California Press. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability. Berkeley: University of California Press, 1967: 281-297.
[13]	TIAN Zheng, LI Xiao-bin, GOU Yan-wei. Perturbation analysis of spectral clustering[J]. Science in China Series E: Information Sciences, 2007, 37(4): 527-543. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-JEXK200704004.htm
[14]	REGE M, DONG Ming, FOTOUHI F. Bipartite isoperimetric graph partitioning for data co-clustering[J]. Data Mining and Knowledge Discovery, 2008, 16(3): 276-312.