Bridge crack measurement system based on binocular stereo vision technology

doi:10.11772/j.issn.1001-9081.2015.03.901

Journal of Computer Applications ›› 2015, Vol. 35 ›› Issue (3): 901-904.DOI: 10.11772/j.issn.1001-9081.2015.03.901

Bridge crack measurement system based on binocular stereo vision technology

WANG Lin, ZHAO Jiankang, XIA Xuan, LONG Haihui

Department of Instrument Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Received:2014-10-21 Revised:2014-11-25 Online:2015-03-10 Published:2015-03-13

基于双目立体视觉技术的桥梁裂缝测量系统

王琳, 赵健康, 夏轩, 龙海辉

上海交通大学仪器科学与工程系, 上海 200240

通讯作者: 赵健康
作者简介:王琳(1990-),女,陕西富平人,硕士研究生,主要研究方向:双目视觉、图像匹配;赵健康(1966-),男,湖南益阳人,教授,博士,主要研究方向:无人机飞行控制系统;夏轩(1987-),男,湖南湘潭人,博士研究生,主要研究方向:模式识别、卫星导航;龙海辉(1984-),男,湖南炎陵人,博士研究生,主要研究方向:系统辨识、航天器控制
基金资助:
国家863计划项目(2012AA121602)

Abstract

Abstract:

A bridge crack measurement system based on binocular stereo vision technology was proposed considering the low efficiency, high cost and low precision of bridge cracks measurement at home and abroad. The system realized by using some binocular stereo vision methods like camera calibration, image matching and three dimensional coordinates reconstruction to calculate the width and the length of bridge cracks. The measured results by binocular vision and by monocular vision system under the same conditions were compared, which show that using the binocular vision measurement system made width relative error keep within 10% and length relative error keep within 1% steadily, while the results measured by monocular vision were changed widely in different angles with a maximum width relative error 19.41% and a maximum length relative error 54.35%. The bridge crack measurement system based on binocular stereo vision can be used in practical well with stronger robustness and higher precision.

Key words: binocular vision, bridge, crack detection, image matching, camera calibration

摘要：

针对国内外桥梁裂缝测量效率低、成本高、精度低等现状,提出了一种基于双目立体视觉技术的桥梁裂缝测量系统。采用相机标定、图像匹配、三维坐标计算等双目立体视觉技术对桥梁裂缝的宽度和长度进行计算,实现了桥梁裂缝测量系统。实验通过与单目摄像机用图像处理方法计算的桥梁尺寸结果作对比,表明:对于同一条裂缝的宽度与长度,用所提方法在不同拍摄角度下计算结果差别不大,并且与真实值的相对误差分别保持在10%和1%以内,而同一条件下单目测量系统的计算结果则随着拍摄角度的变化相差很大,宽度测量误差最大达到了19.41%,长度测量误差最大达到了54.35%。说明基于双目立体视觉技术的桥梁裂缝测量系统可以应用于实际中,并且鲁棒性更强,测量结果更精确。

关键词: 双目视觉, 桥梁, 裂缝测量, 图像匹配, 相机标定

CLC Number:

TP391.41

WANG Lin, ZHAO Jiankang, XIA Xuan, LONG Haihui. Bridge crack measurement system based on binocular stereo vision technology[J]. Journal of Computer Applications, 2015, 35(3): 901-904.

王琳, 赵健康, 夏轩, 龙海辉. 基于双目立体视觉技术的桥梁裂缝测量系统[J]. 计算机应用, 2015, 35(3): 901-904.

References

[1] XU X, ZHANG X. Crack detection of concrete bridges based digital image[J]. Journal of Hunan University: Natural Science, 2013,40(7):34-40.(许薛军,张肖宁.基于数字图像的混凝土桥梁裂缝检测技术[J]. 湖南大学学报:自然科学版, 2013,40(7):34-40.)
[2] XIA J, CHEN P, CHEN Z, et al. Bridge crack detection technology based on digital image processing[J]. Electronic Instrumentation Customer, 2011,18(1):72-74.(夏巨武,陈普春,陈卓,等.基于数字图像处理的桥梁裂缝测量技术[J]. 仪器仪表用户, 2011,18(1):72-74.)
[3] YIN G, ZHAO Z, XU B. Bridge crack detection based on image processing technology[J]. Sichuan Construction Science Research, 2013,39(2):125-128.(尹冠生,赵振宇,徐兵. 基于图像处理的桥梁裂缝检测技术[J]. 四川建筑科学研究, 2013,39(2):125-128.)
[4] ZHOU Y, SUN C, SONG Y, et al. Image pre-filtering for measurement error reduction in digital image correlation[J]. Optics and Lasers in Engineering, 2014,65:46-56.
[5] GONZALEZ R C, WOODS R E. Digital image processing[M]. Upper Saddle River: Prentice-Hall, 2002:577-581.
[6] CHI J. Vision measurement technology[M]. Beijing: Mechanical Industry Press, 2011:224-225.(迟健男.视觉测量技术[M].北京:机械工业出版社,2011:224-225.)
[7] RICHARD H, ANDREW Z. Multiple view geometry in computer vision[M]. 2nd ed. Cambridge: Cambridge University Press, 2004:239-262.
[8] HAO L. 3D profile and deformation measurement based on binocular vision system[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2013:19-20.(郝丽娟.基于双目视觉的三维形貌与形变测量[D].南京:南京航空航天大学,2013:19-20.)
[9] FENG H. Research on camera calibration method in 3D reconstruction[D]. Chongqing: Chongqing Jiaotong University, 2013:10-16.(冯焕飞.三维重建中的相机标定方法研究[D].重庆:重庆交通大学,2013:10-16.)
[10] ZHANG Z. A flexible new technique for camera calibration[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000,22(11):1330-1334.
[11] TAKASHI G, KOZO S. History matching with iterative Latin hypercube samplings and parameterization of reservoir heterogeneity[J]. Journal of Petroleum Science and Engineering, 2014,114:61-73.
[12] HEIPKE C. Overview of image matching techniques[C]//Proceeding of 16th OEEPE Workshop on Application of Digital Photogrammetric Workstation. Lausanne:[s.n.], 1996:173-189.
[13] ADRIAN K, GRAY B. Learning opencv[M]. 2nd ed. Sebastopol: O'Reilly Media, 2008:78-80.
[14] DUAN R, LI Q, LI Y. Summary of image edge detection[J]. Optical Technique, 2005,31(3):1-5.(段瑞玲,李庆祥,李玉和.图像边缘检测方法研究综述[J].光学技术,2005,31(3):415-419.)
[15] LUO G. Some issues of depth perception and three dimention reconstruction from binocular stereo vision[D]. Changsha: Central South University, 2012:103-108.(罗桂娥.双目立体视觉深度感知与三维重建若干问题研究[D].长沙:中南大学,2012:103-108.)

Bridge crack measurement system based on binocular stereo vision technology

基于双目立体视觉技术的桥梁裂缝测量系统

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