隧道衬砌裂缝特征提取方法研究

doi:10.13722/j.cnki.jrme.2014.0930

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摘要衬砌裂缝图像的分割和信息提取是裂缝自动检测系统中的重点。本文在采集图片大量试验的基础上对裂缝信息采集流程进行优化。其中，针对衬砌图像分割，提出结合prewitt算子和otsu的梯度类间阈值法，并用数学形态学对图片进行进一步优化，从而得到裂缝区域完整而杂质区域较少的二值图像；针对裂缝特征提取，通过对试验图片集合特征的分析确定描绘子为集中度、外接矩形长宽比、外接矩形占有率、平均宽度等。应用实例表明，梯度类间阈值法能够更好地实现对图像的分割，较常用的otsu算法更能适应复杂背景的裂缝图片。通过对优化图像的不同区域进行描绘特征提取统计，可形成具有一定代表性的图片库，为实现裂缝自动识别奠定基础。

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关键词 ： Prewitt算子, otsu最大类间阈值, 二值图像, 特征提取

Abstract：The segmentation of lining crack image and characteristic extraction are the key points for the automatic crack detection. Based on theenormoustest of the collected images，the digital image processinganalysis is optimized，and some algorithms are improved. For the image segmentation，this paper proposes a newbinaryzation algorithm combining Prewitt operator with Otsu threshold. The binary image processed by the new algorithm，combined with mathematical morphology，can result in the one with more complete fracture zone and less interference regions.For characteristic extraction，area，perimeter，compactness，rectangle degree，Feret’s occupy rate，and the mean width are defined as the morphological characteristics.The real application results showthat the integtated algorithm can achieve better image segmentation，and it is more suitable for crack image with complex background. The characteristics extracted fromdifferent regions in the imagecan form a representative information base andlay a foundation for automatic identification of cracks.

Key words： Prewitt operator Otsu threshold image binaryzation characteristic extraction

引用本文:

王睿1，漆泰岳1，雷波1，李阳2，朱鑫1. 隧道衬砌裂缝特征提取方法研究[J]. 岩石力学与工程学报, 2015, 34(06): 15-15.
WANG Rui1，QI Taiyue1，LEI Bo1，LI Yang2，ZHU Xin1. STUDY ON THE CHARACTERISTIC EXTRACTION OF TUNNEL LINING CRACKS. , 2015, 34(06): 15-15.

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http://www.rockmech.org/CN/10.13722/j.cnki.jrme.2014.0930 或 http://www.rockmech.org/CN/Y2015/V34/I06/15

[1]	尹兰，何小元. 基于数字图像处理技术的混凝土表面裂缝特征测量和分析[D]. 南京：东南大学，2006.(YIN Lan，HE Xiaoyuan. Measurement and analysis of crackk feature of concrete surface based on digital image processing techniques[D]. Southeast University，2006.(in Chinese)) " target="_blank">
[9]	KABIR S，RIVARD P，HE D C，et al. Damage assessment for concrete structure using image processing techniques on acoustic borehole imagery[J]. Construction and Building Materials，2009，23(10)：3 166-3 174. " target="_blank">
[11]	SINHA S K，FIEGUTH P W. Automated detection of cracks in buried concrete pipe images[J]. Automation in Construction，2006，15(1)：58-72.
[17]	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).(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)：39-43.(in Chinese)) " target="_blank">
[18]	LIU Z W，SUANDI S A，OHASHI T，et al. Tunnel crack detection and classification system based on image processing[C]// Conference on Machine Vision Applications in Industrial Inspection X，2002：145-152. " target="_blank">
[20]	谢昌荣，张郭晶. 路面裂缝检测图像处理算法的研究[J]. 中外公路，2009，6：1 671-2 579. " target="_blank">
[22]	GONZALEZ R C，WOODS R E，EDDINS S L. Digital image processing using MATLAB[M]. Pearson Education India，2004. " target="_blank">
[24]	曾燕华. 基于计算机视觉的桥梁表面缺陷检测技术研究[D]. 广东：广东工业大学，2005.(ZENG Yanhua. Research on CV-based brigde surface block detection and measurement technology[D]. Dissertation for Master?s Degree，Guangdong University of Technology，2005.(in Chinese)) " target="_blank">
[7]	KAWAMURA K，MIYAMOTO A，NAKAMURA H，et al. Proposal of a crack pattern extraction method from digital images using an interactive genetic algorithm[C]// Proceedings-Japan Society of Civil Engineers. Dotoku gakkai，2003：115-132. " target="_blank">
[14]	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))
[5]	HUTCHINSON T C，CHEN Z Q. Improved image analysis for evaluating concrete damage[J]. Journal of Computing in Civil Engineering，2006，20(3)：210-216. " target="_blank">
[13]	储江伟，初秀民，王荣本，等. 沥青路面破损图象特征提取方法研究[J]. 中国图象图形学报：A 辑，2004，8(10)：1 211-1 217.(CHU Jiangwei，CHU Xiumin，WANG Rongben，et al. Research on asphalt pavement surface distress image feature extraction method[J]. Journal of Image and Graphics，2004，8(10)：1 211-1 217.(in Chinese)) " target="_blank">
[15]	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.
[16]	YU S N，JANG J H，HAN C S. Auto inspection system using a mobile robot for detecting concrete cracks in a tunnel[J]. Automation in Construction，2007，16(3)：255-261.
[2]	王平让，黄宏伟，薛亚东. 基于图像局部网格特征的隧道衬砌裂缝自动识别[J]. 岩石力学与工程学报，2012，31(5)：991-999.(WANG Pingrang，HUANG Hongwei，XUE Yadong. Automatic recognition of cracks in tunnel lining based on characteristics of local grids in images[J]. Chinese Journal of Rock Mechanics and Engineering，2012，5：991-999.(in Chinese))
[4]	ITO A，AOKI Y，HASHIMOTO S. Accurate extraction and measurement of fine cracks from concrete block surface image[C]// IECON 02 [Industrial Electronics Society，IEEE 2002 28th Annual Conference of the]. IEEE，2002，3：2 202-2 207. " target="_blank">
[6]	YAMAGUCHI T，NAKAMURA S，SAEGUSA R，et al. Image‐Based Crack Detection for Real Concrete Surfaces[J]. IEEJ Transactions on Electrical and Electronic Engineering，2008，3(1)：128-135. " target="_blank">
[10]	FUJITA Y，HAMAMOTO Y. A robust automatic crack detection method from noisy concrete surfaces[J]. Machine Vision and Applications，2011，22(2)：245-254.
[12]	JAHANSHAHI M R，MASRI S F. Adaptive vision-based crack detection using 3D scene reconstruction for condition assessment of structures[J]. Automation in Construction，2012，22：567-576. " target="_blank">
[3]	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 Standard Compilation Group of People?s Republic of China. JTGH12—2003 Highway tunnel maintenance technical specification[S]. Beijing：China Communications Press，2003.(in Chinese)) " target="_blank">
[8]	ABDEL-QADER I，ABUDAYYEH O，KELLYM E. Analysis of edge-detection techniques for crack identification in bridges[J]. Journal of Computing in Civil Engineering，2003，17(4)：255-263. " target="_blank">
[19]	李清泉，胡庆武. 基于图像自动匀光的路面裂缝图像分析方法[J]. 公路交通科技，2010，27(4)：1-5.(LI Qingquan，HU Qingwu. A pavement crack image analysis approach based on automatic image dodging[J]. Journal of Highway and Transportation Research and Development，2010，27(4)：1-5.(in Chinese))
[23]	肖旺新，张雪，黄卫，等. 路面破损自动识别的一种新算法[J]. 公路交通科技，2005，22(11)：75-78.(XIAO Wangxin，ZHANG Xue，HUANG Wei，et al. One new algorithm of automatic classification for pavement distress[J]. Journal of Highway and Transportation Research and Development，2005，22(11)：75-78.(in Chinese))
[21]	李晋惠. 用图像处理的方法检测公路路面裂缝类病害[J]. 长安大学学报(自然科学版)，2004，3：1 671-8 879.(LI Jinhui. Pavement crack diseases detecting by image processing algorithm[J]. Journal of Chang’an University：Natural Science Edition，2004，3：1 671-8 879.(in Chinese)) " target="_blank">
[25]	杨帆，周又玲. 基于轮廓特征的路面裂缝图像拼接[J]. 中国科技信息，2005，22A：88-89.(YANG Fan，ZHOU Youling. Pavement crack image stitching based on contour feature[J]. China Science and Technology Information，2005，22A：88-89.(in Chinese)) " target="_blank">