基于图像局部网格特征的隧道衬砌裂缝自动识别

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (427 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract Crack is one of the most common and serious defects in tunnel lining. In light of the existing problems of conventional image recognition methods，an automatic crack recognition method in tunnel lining based on characteristics of local grids in images is presented. A lining image is firstly divided into local grids of 8 Pixel×8 Pixel. Cross-shaped templates are designed based on the characteristics of luminance difference and contrast difference between different directions in local grids. The pixel with minimum gray value in each grid can be recognized as one potential crack seed by template calculation. Discrete crack seeds are finally linked together to form an intact and continuous crack cluster using seed linking algorithm. During the linking process，the direction，length and width of cracks are measured automatically. The optimal parameters and threshold of the proposed algorithm are estimated using receiver operating characteristics(ROC) curves. The reliability and accuracy are validated by means of qualitative and quantitative analyses. Application cases show that the proposed method can achieve good effects of crack recognition，especially for the lining images containing minor cracks and leakage；and the reliability and recognition rate are higher than those of other conventional image recognition methods.

Key words： tunnelling engineering tunnel inspection crack recognition local grid receiver operating characteristics (ROC) curve

Received: 25 November 2011

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors

Cite this article:

URL:

https://rockmech.whrsm.ac.cn/EN/Y2012/V31/I5/991

[1] 中华人民共和国行业标准编写组. JTG H12—2003 公路隧道养护技术规范[S]. 北京：人民交通出版社，2003.(The Professional Standards Compilation Group of People?s Republic of China. JTG H12—2003 Technical specification of maintenance for highway tunnel[S]. Beijing：China Communications Press，2003.(in Chinese)) [2] IYER S，SINHA S K. Segmentation of pipe images for crack detection in buried sewers[J]. Computer-aided Civil and Infrastructure Engineering，2006，21(6)：395–410. [3] 马常霞，赵春霞，胡勇，等. 结合NSCT和图像形态学的路面裂缝检测[J]. 计算机辅助设计与图形学学报，2009，21(12)：1 761– 1 767.(MA Changxia，ZHAO Chunxia，HU Yong，et al. Pavement cracks detection based on NSCT and morphology[J]. Journal of Computer-aided Design and Computer Graphics，2009，21(12)：1 761–1 767.(in Chinese)) [4] 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. [5] FUJITA Y，HAMAMOTO Y. A robust automatic crack detection method from noisy concrete surfaces[J]. Machine Vision and Applications，2011，22(2)：245–254. [6] 孙波成，邱延峻. 路面裂缝图像处理算法研究[J]. 公路交通科技，2008，25(2)：64–68.(SUN Bocheng，QIU Yanjun. Pavement crack diseases recognition based on image processing algorithm[J]. Journal of Highway and Transportation Research and Development，2008，25(2)：64–68.(in Chinese)) [7] 徐志刚，赵祥模，宋焕生，等. 基于直方图估计和形状分析的沥青路面裂缝识别算法[J]. 仪器仪表学报，2010，31(10)：2 260–2 266. (XU Zhigang，ZHAO Xiangmo，SONG Huansheng，et al. Asphalt pavement crack recognition algorithm based on histogram estimation and shape analysis[J]. Chinese Journal of Scientific Instrument，2010，31(10)：2 260–2 266.(in Chinese)) [8] YAMAGUCHI T，HASHIMOTO S. Fast crack detection method for large-size concrete surface images using percolation-based image processing[J]. Machine Vision and Applications，2010，21(5)：797–809. [9] 初秀民，王荣本，储江伟，等. 沥青路面破损图像分割方法研究[J]. 中国公路学报，2003，16(3)：11–14.(CHU Xiumin，WANG Rongben，CHU Jiangwei，et al. Study of asphalt pavement surface distress image segmentation[J]. China Journal of Highway and Transport，2003，16(3)：11–14.(in Chinese)) [10] OH J K，JANG G，OH S，et al. Bridge inspection robot system with machine vision[J]. Automation in Construction，2009，18(7)：929–941. [11] YU S N，JANG J H，HAN C S，et al. Auto inspection system using a mobile robot for detecting concrete cracks in a tunnel[J]. Automation in Construction，2007，16(3)：255–261. [12] 张娟，沙爱民，孙朝云，等. 基于相位编组法的路面裂缝自动识别[J]. 中国公路学报，2008，21(2)：39–42.(ZHANG Juan，SHA Aimin，SUN Zhaoyun，et al. Pavement crack automatic recognition based on phase-grouping method[J]. China Journal of Highway and Transport，2008，21(2)：39–42.(in Chinese)) [13] SINHA S K，FIEGUTH P W. Automated detection of cracks in buried concrete pipe images[J]. Automation in Construction，2006，15(1)：58–72. [14] HUANG Y X，XU B G. Automatic inspection of pavement cracking distress[J]. Journal of Electronic Imaging，2006，15(1)：13–17. [15] 王建秀，朱合华，唐益群，等. 双连拱公路隧道裂缝成因及防治措施[J]. 岩石力学与工程学报，2005，24(2)：195–202.(WANG Jianxiu，ZHU Hehua，TANG Yiqun，et al. Cracking in twin-arc highway tunnels and its prevention measures[J]. Chinese Journal of Rock Mechanics and Engineering，2005，24(2)：195–202.(in Chinese)) [16] 潘洪科，杨林德，黄慷. 公路隧道偏压效应与衬砌裂缝的研究[J]. 岩石力学与工程学报，2005，24(18)：3 311–3 315.(PAN Hongke，YANG Linde，HUANG Kang. Research on unsymmetrical load effect and lining cracks of a highway tunnel[J]. Chinese Journal of Rock Mechanics and Engineering，2005，24(18)：3 311–3 315.(in Chinese)) [17] WANG Z，BOVIK A C. A universal image quality index[J]. IEEE Signal Processing Letters，2002，9(3)：81–84. [18] YANG M D，SU T C，PAN N F，et al. Systematic image quality assessment for sewer inspection[J]. Expert Systems with Applications，2011，38(3)：1 766–1 776. [19] CHEN L C，SHAO Y C，JAN H H，et al. Measuring system for cracks in concrete using multitemporal images[J]. Journal of Surveying Engineering，2006，132(2)：77–82. [20] FAWCETT T. An introduction to ROC analysis[J]. Pattern Recognition Letters，2006，27(8)：861–874. [21] OTSU N. A threshold selection method from gray-level histograms[J]. IEEE Transactions on Systems，Man，and Cybernetics，1979，9(1)：62–66.