隧道衬砌图像清晰度影响因素的模型试验研究

doi:10.13722/j.cnki.jrme.2017.0034

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Abstract

Using orthogonal experiment analysis，this paper presents a model test study on factors affecting image sharpness of tunnel lining via a mobile platform equipped with linear array camera，light of either LED or LASER，industry computer etc. Four factors are considered in this paper，which are type of light source，illumination intensity of object surface，exposure time and surface diameter. An algorithm which utilizes both the slope of the magnitude spectrum and the total spatial variation is implemented to measure the local perceived sharpness in some images acquired in the test. Significance and optimal levels of the four factors are calculated by analysis of variance. We also discuss about changing laws of image sharpness under different testing factor levels successively. The test results show that the order of principal factors affecting image sharpness are exposure time，type of light source，illumination intensity of object surface，surface diameter，among which surface diameter is an undistinguished factor. The best combination of optimal levels is LED，6 klux，90 μs. This study can show a delighted help for inspection equipment of tunnel lining defects based on CV and ML.

Key words： tunnelling engineering tunnel lining image sharpness model test defect inspection computer vision

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	Articles by authors
	LI Qingtong1
	HUANG Hongwei1
	2
	XUE Yadong1
	2
	LUO Tailai1
	WU Changrui1

Cite this article:

LI Qingtong1,HUANG Hongwei1,2, et al. Model test study on factors affecting image sharpness of tunnel lining[J]. , 2017, 36(S2): 3915-3926.

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https://rockmech.whrsm.ac.cn/EN/10.13722/j.cnki.jrme.2017.0034 OR https://rockmech.whrsm.ac.cn/EN/Y2017/V36/IS2/3915

[1]	王如路. 上海轨道交通隧道结构安全性分析[J]. 地下工程与隧道，2011，(4)：37-43.(WANG Rulu. Structural safety analysis of shanghai rail transit tunnel[J].Underground Engineering and Tunnel，2011，(4)：37-43.(in Chinese))
[2]	LEE S Y，SANG H L，DONG I S，et al. Development of an inspection system for cracks in a concrete tunnel lining[J]. Canadian Journal of Civil Engineering，2007，34(8)：966-975.
[3]	MONTEROR，VICTORESJ G，MARTINEZS，et al. Past，present andfuture of robotic tunnel inspection[J]. Automation in Construction，2015，59：99-112.
[4]	刘学增，叶康. 隧道衬砌裂缝的远距离图像测量技术[J]. 同济大学学报：自然科学版，2012，40(6)：829-836.(LIU Xuezeng，YE Kang. A long-distance image measuring technique for crack on tunnel lining[J].Journal of Tongji University：Natural Science，2012，40(6)：829-836.(in Chinese))
[5]	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，2012，31(5)：991-999.(in Chinese))
[6]	王耀东，余祖俊，白彪，等. 基于图像处理的地铁隧道裂缝识别算法研究[J]. 仪器仪表学报，2014，35(7)：1 489-1 496. (WANG Yaodong，YU Zujun，BAI Biao，et al. Research on image processing based subway tunnel crack identification algorithm[J]. Chinese Journal of Scientific Instrument， 2014，35(7)：1489-1496.(in Chinese))
[7]	王睿，漆泰岳，雷波，等. 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(6)：1 211-1 217.(WANG Rui，QI Taiyue，LEI Bo，et al. Characteristic extraction of cracks of tunnel lining[J]. Chinese Journal of Rock Mechanics and Engineering，2015，34(6)：1 211-1 217.(in Chinese))
[8]	AI Q，YUAN Y，BI X. Acquiring sectional profile of metro tunnels using charge-coupled device cameras[J].Structure andInfrastructure Engineering，2016，12(9)：1-11.
[9]	HUANG H W，SUN Y，XUE Y D，et al. Inspection equipment study for subway tunnel defects by grey-scale image processing[J]. Advanced Engineering Informatics，2017，32：188-201.
[10]	YAMAGUCHI T，HASHIMOTO S. Practical image measurement of crack width for real concrete structure[J]. Electronics and Communicationsin Japan，2010，92(10)：1-12.
[1]	VICTORES J G，MARTÍNEZ S，JARDÓN A，et al. Robot-aided tunnel inspection and maintenance system by vision and proximity sensor integration[J]. Automation in Construction，2011，20(5)：629-636.
[2]	HOF R D. MIT technology review：10 breakthrough technologies-deep learning[EB/OL]. (2013-4-23)[2017-1-3]. https：//www.technologyrevite.com/s/513696/deep-learning/
[3]	LECUN Y，BENGIO Y，HINTON G. Deep learning[J]. Nature，2015，521：436-444.
[4]	PROTOPAPADAKIS E，DOULAMIS N. Image based approaches for tunnels¢defects recognition via robotic inspectors[C]//Proceedings of the 11th International Symposium on Visual Computing. Las Vegas，Nevada，U.S.：Springer International Publishing，2015：706-716.
[5]	CHA Y J，Choi W，BUYUKOZTURK O. Deep learning-based crack damage detection using convolutional neural networks[J]. Computer-Aided Civil and Infrastructure Engineering，2017，32(5)：361-378.
[6]	GARCIA-GARCIA A，ORTS-ESCOLANO S，OPREA S，et al. A review on deep learning techniques applied to semantic segmentation[J].arXiv preprint，arXiv：1704.06857，2017：1-23.
[11]	李祚林，李晓辉，马灵玲，等. 面向无参考图像的清晰度评价方法研究[J]. 遥感技术与应用，2011，26(2)：239-246.(LI Zuolin，LI Xiaohui，MA Lingling，et al. Research of definition assessment based on no-reference digital image quality[J] Remote Sensing Technology and Application，2011，26(2)：239-246.(in Chinese))
[12]	王平让，黄宏伟，薛亚东. 隧道衬砌裂缝自动检测性能影响因素模型试验研究[J]. 岩石力学与工程学报，2012，31(8)：1705-1714.(WANG Pingrang，HUANG Huangwei，XUE Yadong. Model test study of factors affecting automatic defection performance of cracks in tunnel lining[J]. Chinese Journal of Rock Mechanics and Engineering，2012，31(8)：1705-1714.(inChinese))
[13]	周建，段中兴，陈胜，等. 隧道裂缝自动识别性能影响因素的研究[J]. 公路，2016，(5)：216-222.(ZHOU Jian，DUAN Zhongxing，CHEN Sheng，et al. Study of factors affecting automatic detection performance of cracks in tunnel[J]. Highway，2016，(5)：216-222.(in Chinese))
[14]	白福忠. 视觉测量技术基础[M].北京：电子工业出版社，2013：37-75.(BAI Fuzhong. Technique basis of vision measurement[M]. Beijing：Publishing House of Electronics Industry，2013：37-75.(in Chinese))
[15]	ZHAO L，NIU L. Study on key techniques of image processing and automatic recognition of tunnel cracks[C]// Proceedings of the 2012 International Conference of Logistics Engineering and Management. Chengdu：ASCE，2012：427-433.
[16]	王平让. 隧道衬砌裂缝机器视觉检测方法及结构安全评估研究[博士学位论文][D]. 上海，同济大学，2013：67-77.(WANGPingrang. Study on machine vision inspection method for cracks and structural safety assessment in tunnel lining[Ph. D. Thesis][D]. Shanghai：Tongji University，2013：67-77.(in Chinese))
[17]	VU C T，PHAN T D，CHANDLER D M. S3：a spectral and spatial measure of local perceived sharpness in natural images[J]. Image Processing IEEE Transactions on Image Processing，2012，21(3)：934-945.
[18]	WIERER J J，TSAO J Y，SIZOV D S. Comparison between blue lasers and light-emitting diodes for future solid-state lighting[J]. Laser andPhotonics Reviews，2013，7(6)：963-993.
[19]	KOPPARAPUS K. Lighting design for machine vision application[J]. Image and Vision Computing，2006，24(7)：720-726.
[20]	中国土木工程学会. 土木工程应用技术[M].北京：中国城市出版社，2015：18-24.(China Civil Engineering Society. Civil engineering application technology[M]. Beijing：China City Press，2015：18-24.(in Chinese))
[21]	SUN Y，XUE Y D，HUANG H W.Inspection equipment study on subway tunnel defects[C]//Proceedingsof the 2016 World Tunnel Congress.San Francisco，U.S.：International Tunneling and UndergroundSpace Association，2016：1-9.
[22]	中华人民共和国国家标准编写组. GB 50157—2013地铁设计规范[S].北京：中国建筑工业出版社，2013：94.(The National Standards Compilation Group of the People¢s Republic of China. GB 50157—2013 Code for design of metro[S]. Beijing：China Architecture and Building Press，2013：94.(in Chinese))