高速铁路隧道衬砌裂缝图像快速采集系统研究
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摘要
我国正处于高速铁路大量建设和投入运营的时期。受到线路平顺性的要求,高速铁路沿线大量使用桥梁和隧道。在未来相当长的时间内,我国都将面临着大量隧道需要定期检修、维护的状况。由于高速铁路隧道自身的特点,检测工作仅依靠普通的人工检测或人工仪器检测无法满足要求。本文基于以上背景,研究了利用图像采集和处理技术的高速铁路隧道衬砌裂缝自动检测系统。
本文对高速铁路隧道衬砌裂缝自动检测系统的工作原理进行深入研究,并对系统进行了总体设计,包括图像采集子系统和图像处理分析子系统。提出了系统的相关技术指标要求,针对自动检测系统选取了合适的衬砌裂缝评价指标。
本文针对隧道图像采集特点,对图像采集系统进行了硬件选型,使图像采集系统能够满足检测的各项要求,设计了图像采集系统的采集方案。
针对采集到的裂缝图像,进行了增强和分割处理,通过实验选取了合适的增强和分割方法,并对分割后的裂缝图像进行了形态学处理,为后续图像分析打下基础。
通过读取数字图像的方法,提取了裂缝的特征指标,并根据几何特征对图像中裂缝和其他干扰物进行了筛选和区分。
本文通过搭建一套高速铁路隧道衬砌裂缝的图像快速采集系统,并通过图像处理技术对图像进行分析,为我国高速铁路隧道衬砌检测提供了一种可靠的方法,在保证高速铁路安全运营方面有重要意义。
In the current period, a large amount of high-speed railway is in construction and operating in our country. By the requirement of smoothness, high-speed railway using a large number of bridges and tunnels. For a long time in the future, our country will be faced with the status that a large number of tunnels need regular maintenance. Due to the characteristics of the high-speed railway tunnel, detecting work that only rely on artificial detection or manual detection equipment cannot meet the requirements. Based on the above background, this thesis studies the automatic detection system for cracks in high-speed railway tunnel lining which using the image acquisition and processing technology.
In this thesis, the principle of automatic detection system for cracks in high-speed railway tunnel lining was deeply researched. And this thesis has gives the overall design of the system, including image acquisition subsystem and image processing subsystem. Appropriate indexes of lining crack evaluation was selected for automatic detection system.
According to the characteristics of image acquisition system for tunnel lining, this thesis selected the hardware for image acquisition system which make the image acquisition system can meet the detection requirements. And the acquisition scheme of image acquisition system is designed.
In the light of the crack image, suitable methods were selected for the enhancement and segmentation of cracks image through experiments, and mathematical morphology method was used for the crack image after segmentation. These steps lay the foundation for subsequent image analysis.
By reading the digital image, the characteristic indexes of crack were extracted, and using the geometric features to discriminate between cracks and other distractors in the images.
In this thesis, a set of image acquisition system for cracks in high-speed railway tunnel lining was built.and the crack images were analysed with image processing technology. The system provide a reliable way for the detection of high-speed railway tunnel lining. The system has important significance to ensure the safe operation of high-speed railway.
本文对高速铁路隧道衬砌裂缝自动检测系统的工作原理进行深入研究,并对系统进行了总体设计,包括图像采集子系统和图像处理分析子系统。提出了系统的相关技术指标要求,针对自动检测系统选取了合适的衬砌裂缝评价指标。
本文针对隧道图像采集特点,对图像采集系统进行了硬件选型,使图像采集系统能够满足检测的各项要求,设计了图像采集系统的采集方案。
针对采集到的裂缝图像,进行了增强和分割处理,通过实验选取了合适的增强和分割方法,并对分割后的裂缝图像进行了形态学处理,为后续图像分析打下基础。
通过读取数字图像的方法,提取了裂缝的特征指标,并根据几何特征对图像中裂缝和其他干扰物进行了筛选和区分。
本文通过搭建一套高速铁路隧道衬砌裂缝的图像快速采集系统,并通过图像处理技术对图像进行分析,为我国高速铁路隧道衬砌检测提供了一种可靠的方法,在保证高速铁路安全运营方面有重要意义。
In the current period, a large amount of high-speed railway is in construction and operating in our country. By the requirement of smoothness, high-speed railway using a large number of bridges and tunnels. For a long time in the future, our country will be faced with the status that a large number of tunnels need regular maintenance. Due to the characteristics of the high-speed railway tunnel, detecting work that only rely on artificial detection or manual detection equipment cannot meet the requirements. Based on the above background, this thesis studies the automatic detection system for cracks in high-speed railway tunnel lining which using the image acquisition and processing technology.
In this thesis, the principle of automatic detection system for cracks in high-speed railway tunnel lining was deeply researched. And this thesis has gives the overall design of the system, including image acquisition subsystem and image processing subsystem. Appropriate indexes of lining crack evaluation was selected for automatic detection system.
According to the characteristics of image acquisition system for tunnel lining, this thesis selected the hardware for image acquisition system which make the image acquisition system can meet the detection requirements. And the acquisition scheme of image acquisition system is designed.
In the light of the crack image, suitable methods were selected for the enhancement and segmentation of cracks image through experiments, and mathematical morphology method was used for the crack image after segmentation. These steps lay the foundation for subsequent image analysis.
By reading the digital image, the characteristic indexes of crack were extracted, and using the geometric features to discriminate between cracks and other distractors in the images.
In this thesis, a set of image acquisition system for cracks in high-speed railway tunnel lining was built.and the crack images were analysed with image processing technology. The system provide a reliable way for the detection of high-speed railway tunnel lining. The system has important significance to ensure the safe operation of high-speed railway.
引文
[1]铁道部.中长期铁路网规划(2008年调整)[D].2008.
[2]王阳.铁路项目投资控制分析与建议[J].铁路工程造价管理,2009,3：31-32.
[3]铁道部第二勘探设计院.铁路工务技术手册——隧道[M].中国铁道出版社,1995.
[4]贾志清.铁路运营隧道病害状态检测及其机理分析[D].北京：北京交通大学,2005.
[5]王睿.隧道衬砌裂缝车载检测图像分析研究[D].成都：西南交通大学,2012.
[6]Oh J, Lee A, Oh S M, etal. Design and Control of Bridge Inspection Robot System[J]. IEEE Computer Society:3634-3639.
[7]Lee J H, Lee J M, Kim H J, etal. Machine Vision System for Automatic Inspection of Bridges[J]. Congress on Image and Signal Processing:363-366.
[8]Tung P C, Hwang Y R, Wu M C. The development of a mobile manipulator imaging system for bridge crack inspection[J]. Automation in construction,2002,11(6):717-729.
[9]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.
[10]张维峰,刘萌,杨明慧.基于数字图像处理的桥梁裂缝检测技术[J].现代交通技术,2008(5)：34-36.
[11]邹大鹏.智能视频桥梁检测车工作臂避障系统的研究[D].广州：广东工业大学,2005.
[12]Pynn J, Wright A, Lodge R. Automatic identification of cracks in road surfaces[C]. Image Processing and Its Applications,1999. Seventh International Conference on (Conf. Publ. No.465). IET,1999,2:671-675.
[13]Asakura T, Kojima Y. Tunnel maintenance in Japan[J]. Tunnelling and Underground Space Technology,2003,18:161-169.
[14]Yu S, Jang J, Han C. Auto inspection system using a mobile robot for detecting concrete cracks in a tunnel[J]. Automation in Construction,2007(16):255-261.
[15]Lee S Y, Lee S H, Shin D I, etal. Development of an inspection system for cracks in a concrete tunnel lining[J]. Canadian Journal of Civil Engineering,2007, Vol.34 (8):966-975.
[16]刘晓瑞,谢雄耀.基于图像处理的隧道表面裂缝快速检测技术研究[J].地下空间与工程学报,2009(S2).
[17]王泰典,林信宏,李佳翰,等.营运中隧道衬砌裂缝影像判释暨特征化与数字化技术[J].隧道建设,2010(S1).
[18]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(06).
[19]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(08).
[20]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(05).
[21]百度百科CCD[EB/OL]. [2013-4-15]. http://baike.baidu.com/view/18579.htm.
[22]百度百科.数字图像处理[EB/OL].[2013-4-15].http://baike.baidu.com/view/286846.htm.
[23]冈萨雷斯.数字图像处理(MATLAB版)[M].第一版.电子工业出版社,2005.
[24]吴江滨,王梦恕.铁路运营隧道病害现状及检测评估[J].中国安全科学学报,2003,13(6)：49-52.
[25]代高飞.隧道典型病害及连拱隧道裂缝和渗漏水调查研究[D].上海：同济大学,2005.
[26]杨新安,黄宏伟.隧道病害与防治[M].上海：同济大学出版社,2003.
[27]侯建斌,夏永旭.公路隧道的养护及病害防治[J].公路交通科技,2006(03).
[28]张素磊.隧道衬砌结构健康诊断及技术状况评定研究[D].北京：北京交通大学,2012.
[29]罗鑫.公路隧道健康状态诊断方法及系统的研究[D].上海：同济大学,2007.
[30]中华人民共和国交通部.JTG H10-2009公路养护技术规范[S].北京：人民交通出版社,2009.
[31]中华人民共和国住房和城乡建设部.GB 50010-2010混凝土结构设计规范[S].北京：中国建筑工业出版社,2010.
[32]中华人民共和国铁道部.TB/T 2820.2-1997铁路桥隧建筑物劣化评定标准[S].北京：中国铁道出版社,1997.
[33]中华人民共和国住房和城乡建设部.GB 50292-1999民用建筑可靠性鉴定标准[S].北京：中国建筑工业出版社,1999.
[34]中华人民共和国铁道部.TB 10003-2005铁路隧道设计规范[S].北京：中国铁道出版社,2005.
[35]李治国,张玉军.衬砌开裂隧道的稳定性分析及治理技术[J].现代隧道技术,2004(01).
[36]Liu Z, Suandi S A, Ohashi T, et al. Tunnel crack detection and classification system based on image processing[C]. Electronic Imaging 2002. International Society for Optics and Photonics,2002:145-152.
[37]王华牢,刘学增,李宁等.纵向裂缝隧道衬砌结构的安全评价与加固研究[J].岩石力学与工程学报,2010(01).
[38]Russell H A, Gilmore J. Inspection policy and procedures for rail transit tunnels and underground structures[M]. Transportation Research Board,1997.
[39]Federal Highway Administ ration & Federal Transit Administ ration. Highway and rail t ransit tunnel inspection manual,2003.
[40]关宝树.日本铁路隧道维修管理技术的现状[J].隧道译从,1993(6)：1-8.
[41]道路隧道维持管理便览[M].东京：日本道路协会,1993.
[42]Park S W, Shin Y S, Oh Y S, etal. A guideline on condition assessment of existing old railway tunnels[J]. Tunnelling and Underground Space Technology,2006,21(3): 329-330.
[43]范斌.高速铁路隧道空气动力学效应及其对支护结构耐久性影响[D].大连：大连交通大学,2010.
[44]狄彩云.基于车载线阵CCD相机的路面数据获取与处理研究[D].首都师范大学,2007.
[45]刘玉.车载式线路全断面检测系统研究[D].北京交通大学,2010.
[46]龙梅.高速公路路面检测系统与路面破损算法研究及系统实现[D].武汉：武汉理工大学,2008.
[47]秦襄培MATLAB图像处理与界面编程宝典[M].第一版.北京：电子工业出版社,2009.
[48]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),2004,3：22-23.
[49]王龙云.路面裂缝检测算法研究[D].南京邮电大学,2012.
[50]Gonzalez R C, Woods R E, Eddins S L. Digital image processing using MATLAB[M]. Tennessee:Gatesmark Publishing,2009.
[51]Chang-Xia Ma,Chun-Xia Zhao,Ying-kun Hou.Pavement Distress Detection Based on Nonsubsampled Contourlet Transform[J]. IEEE.2008:28-31.
[52]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.
[53]蔡光明.高速铁路桥梁底面裂缝的视频检测系统研究[D].北京交通大学,2011.
[2]王阳.铁路项目投资控制分析与建议[J].铁路工程造价管理,2009,3：31-32.
[3]铁道部第二勘探设计院.铁路工务技术手册——隧道[M].中国铁道出版社,1995.
[4]贾志清.铁路运营隧道病害状态检测及其机理分析[D].北京：北京交通大学,2005.
[5]王睿.隧道衬砌裂缝车载检测图像分析研究[D].成都：西南交通大学,2012.
[6]Oh J, Lee A, Oh S M, etal. Design and Control of Bridge Inspection Robot System[J]. IEEE Computer Society:3634-3639.
[7]Lee J H, Lee J M, Kim H J, etal. Machine Vision System for Automatic Inspection of Bridges[J]. Congress on Image and Signal Processing:363-366.
[8]Tung P C, Hwang Y R, Wu M C. The development of a mobile manipulator imaging system for bridge crack inspection[J]. Automation in construction,2002,11(6):717-729.
[9]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.
[10]张维峰,刘萌,杨明慧.基于数字图像处理的桥梁裂缝检测技术[J].现代交通技术,2008(5)：34-36.
[11]邹大鹏.智能视频桥梁检测车工作臂避障系统的研究[D].广州：广东工业大学,2005.
[12]Pynn J, Wright A, Lodge R. Automatic identification of cracks in road surfaces[C]. Image Processing and Its Applications,1999. Seventh International Conference on (Conf. Publ. No.465). IET,1999,2:671-675.
[13]Asakura T, Kojima Y. Tunnel maintenance in Japan[J]. Tunnelling and Underground Space Technology,2003,18:161-169.
[14]Yu S, Jang J, Han C. Auto inspection system using a mobile robot for detecting concrete cracks in a tunnel[J]. Automation in Construction,2007(16):255-261.
[15]Lee S Y, Lee S H, Shin D I, etal. Development of an inspection system for cracks in a concrete tunnel lining[J]. Canadian Journal of Civil Engineering,2007, Vol.34 (8):966-975.
[16]刘晓瑞,谢雄耀.基于图像处理的隧道表面裂缝快速检测技术研究[J].地下空间与工程学报,2009(S2).
[17]王泰典,林信宏,李佳翰,等.营运中隧道衬砌裂缝影像判释暨特征化与数字化技术[J].隧道建设,2010(S1).
[18]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(06).
[19]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(08).
[20]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(05).
[21]百度百科CCD[EB/OL]. [2013-4-15]. http://baike.baidu.com/view/18579.htm.
[22]百度百科.数字图像处理[EB/OL].[2013-4-15].http://baike.baidu.com/view/286846.htm.
[23]冈萨雷斯.数字图像处理(MATLAB版)[M].第一版.电子工业出版社,2005.
[24]吴江滨,王梦恕.铁路运营隧道病害现状及检测评估[J].中国安全科学学报,2003,13(6)：49-52.
[25]代高飞.隧道典型病害及连拱隧道裂缝和渗漏水调查研究[D].上海：同济大学,2005.
[26]杨新安,黄宏伟.隧道病害与防治[M].上海：同济大学出版社,2003.
[27]侯建斌,夏永旭.公路隧道的养护及病害防治[J].公路交通科技,2006(03).
[28]张素磊.隧道衬砌结构健康诊断及技术状况评定研究[D].北京：北京交通大学,2012.
[29]罗鑫.公路隧道健康状态诊断方法及系统的研究[D].上海：同济大学,2007.
[30]中华人民共和国交通部.JTG H10-2009公路养护技术规范[S].北京：人民交通出版社,2009.
[31]中华人民共和国住房和城乡建设部.GB 50010-2010混凝土结构设计规范[S].北京：中国建筑工业出版社,2010.
[32]中华人民共和国铁道部.TB/T 2820.2-1997铁路桥隧建筑物劣化评定标准[S].北京：中国铁道出版社,1997.
[33]中华人民共和国住房和城乡建设部.GB 50292-1999民用建筑可靠性鉴定标准[S].北京：中国建筑工业出版社,1999.
[34]中华人民共和国铁道部.TB 10003-2005铁路隧道设计规范[S].北京：中国铁道出版社,2005.
[35]李治国,张玉军.衬砌开裂隧道的稳定性分析及治理技术[J].现代隧道技术,2004(01).
[36]Liu Z, Suandi S A, Ohashi T, et al. Tunnel crack detection and classification system based on image processing[C]. Electronic Imaging 2002. International Society for Optics and Photonics,2002:145-152.
[37]王华牢,刘学增,李宁等.纵向裂缝隧道衬砌结构的安全评价与加固研究[J].岩石力学与工程学报,2010(01).
[38]Russell H A, Gilmore J. Inspection policy and procedures for rail transit tunnels and underground structures[M]. Transportation Research Board,1997.
[39]Federal Highway Administ ration & Federal Transit Administ ration. Highway and rail t ransit tunnel inspection manual,2003.
[40]关宝树.日本铁路隧道维修管理技术的现状[J].隧道译从,1993(6)：1-8.
[41]道路隧道维持管理便览[M].东京：日本道路协会,1993.
[42]Park S W, Shin Y S, Oh Y S, etal. A guideline on condition assessment of existing old railway tunnels[J]. Tunnelling and Underground Space Technology,2006,21(3): 329-330.
[43]范斌.高速铁路隧道空气动力学效应及其对支护结构耐久性影响[D].大连：大连交通大学,2010.
[44]狄彩云.基于车载线阵CCD相机的路面数据获取与处理研究[D].首都师范大学,2007.
[45]刘玉.车载式线路全断面检测系统研究[D].北京交通大学,2010.
[46]龙梅.高速公路路面检测系统与路面破损算法研究及系统实现[D].武汉：武汉理工大学,2008.
[47]秦襄培MATLAB图像处理与界面编程宝典[M].第一版.北京：电子工业出版社,2009.
[48]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),2004,3：22-23.
[49]王龙云.路面裂缝检测算法研究[D].南京邮电大学,2012.
[50]Gonzalez R C, Woods R E, Eddins S L. Digital image processing using MATLAB[M]. Tennessee:Gatesmark Publishing,2009.
[51]Chang-Xia Ma,Chun-Xia Zhao,Ying-kun Hou.Pavement Distress Detection Based on Nonsubsampled Contourlet Transform[J]. IEEE.2008:28-31.
[52]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.
[53]蔡光明.高速铁路桥梁底面裂缝的视频检测系统研究[D].北京交通大学,2011.
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