Volume 12 Issue 4
. 
Turn off MathJax
Article Contents
Article Navigation >  Journal of Traffic and Transportation Engineering  > 2012  >  12(4): 9-16
LI Gang, HE Shuan-hai, DU Kai, LIU Wei, DU Qin-wen. Modified C-V model algorithm of crack extraction for bridge substructure[J]. Journal of Traffic and Transportation Engineering, 2012, 12(4): 9-16. doi: 10.19818/j.cnki.1671-1637.2012.04.002
Citation: LI Gang, HE Shuan-hai, DU Kai, LIU Wei, DU Qin-wen. Modified C-V model algorithm of crack extraction for bridge substructure[J]. Journal of Traffic and Transportation Engineering, 2012, 12(4): 9-16. doi: 10.19818/j.cnki.1671-1637.2012.04.002
shu

Modified C-V model algorithm of crack extraction for bridge substructure

doi: 10.19818/j.cnki.1671-1637.2012.04.002
  • 1.

    School of Electronic and Control Engineering, Chang'an University, Xi'an 710064, Shaanxi, China

  • 2.

    School of Highway, Chang'an University, Xi'an 710064, Shaanxi, China

  • 3.

    School of Automobile, Chang'an University, Xi'an 710064, Shaanxi, China

Funds:

National Natural Science Foundation of China 60806043

Industrial Application Technology Research and Development Projects of Xi'an Science Technology Bureau CXY1127

Special Fund for Basic Scientific Research of Central Colleges CHD2011JC033

Special Fund for Basic Scientific Research of Central Colleges CHD2011JC180

Special Fund for Basic Scientific Research of Central Colleges CHD2011JC083

More Information
  • Author Bio:

    LI Gang(1975-), Male, Peixian, Jiangsu, Lecturer of Chang'an University, PhD, Research on Bridge Detection, +86-29-82334551, lglg930@163.com

  • Received Date: 2012-02-18
  • Publish Date: 2012-08-25
    Abstract
  • The crack image segmentation of bridge substructure was studied by utilizing a modified C-V model.Crack clip, image filling and rotation transformation were applied for the precise extraction of crack width.The crack images of existing concrete bridge structure were taken in different illuminations, and test results were compared by using modified C-V model algorithm, adaptive threshold algorithm, morphology algorithm, C-V model and Canny algorithm.Analysis result indicates that the misclassification rate of modified C-V model algorithm is 3.02%, the operation time is 89 ms, and the values are minimum compared with other methods.Based on the comparative test on 1 000 crack images of bridge structure, the accuracy rate of crack detection is greater than 90.8%, and the mean error of crack width is less than 0.03 mm.So the modified algorithm can effectively improve detection accuracy rate, and reduce operation time.

     

    • FullText(HTML)
  • loading
    • References(16)
  • [1]
    DILENA M, MORASSI A. Dynamic testing of a damaged bridge[J]. Mechanical Systems and Signal Processing, 2011, 25(1): 1485-1507. https://www.sciencedirect.com/science/article/pii/S0888327011000094
    [2]
    KIM C W, KAWATANI M, OZAKI R, et al. Recovering missing data transmitted from a wireless sensor node for vibration-based bridge health monitoring[J]. Structural Engin-eering and Mechanics, 2011, 38(4): 417-428. doi: 10.12989/sem.2011.38.4.417
    [3]
    ABDEL-QADER I, ABUDAYYEH O, KELLY M E. Analy-sis of edge-detection techniques for crack identification in bridges[J]. Journal of Computing in Civil Engineering, 2003, 17(4): 255-263. doi: 10.1061/(ASCE)0887-3801(2003)17:4(255)
    [4]
    HUTCHINSON T C, CHEN Zhi-qiang. Improved imageanalysis for evaluating concrete damage[J]. Journal of Com-puting in Civil Engineering, 2006, 20(3): 210-216. doi: 10.1061/(ASCE)0887-3801(2006)20:3(210)
    [5]
    NAVON E, MILLER O, AVERBUCH A. Color image segmentation based on adaptive local thresholds[J]. Image and Vision Computing, 2005, 23(1): 69-85. doi: 10.1016/j.imavis.2004.05.011
    [6]
    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. doi: 10.1111/j.1467-8667.2006.00445.x
    [7]
    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. doi: 10.1016/j.autcon.2006.05.003
    [8]
    SINHA S K, FIEGUTH P W. Automated detection ofcracks in buried concrete pipe images[J]. Automation in Con-struction, 2006, 15(1): 58-72. doi: 10.1016/j.autcon.2005.02.006
    [9]
    ZOU Qin, CAO Yu, LI Qing-quan, et al. CrackTree: auto-matic crack detection from pavement images[J]. PatternRecognition Letters, 2012, 33(3): 227-238. https://www.sciencedirect.com/science/article/pii/S0167865511003795
    [10]
    OH J K, JANG G, OH S, et al. Bridge inspection robot sys-tem with machine vision[J]. Automation in Construction, 2009, 18(7): 929-941. doi: 10.1016/j.autcon.2009.04.003
    [11]
    ZHU Zhen-hua, GERMAN S, BRILAKIS I. Visual retrieval of concrete crack properties for automated post-earthquake structural safety evaluation[J]. Automation in Construction, 2011, 20(7): 874-883. doi: 10.1016/j.autcon.2011.03.004
    [12]
    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. doi: 10.1007/s00138-009-0189-8
    [13]
    YANG Ming-yu, DING Huan, ZHAO Bo, et al. Chan-Vese model image segmentation with neighborhood information[J]. Journal of Computer-Aided Design & Computer Graphics, 2001, 23(3): 413-418. https://www.researchgate.net/publication/284555111_Chan-Vese_model_image_segmentation_with_neighborhood_information
    [14]
    XIAO Chun-xia, CHU Yu, ZHANG Qing. Texture image segmentation using level set function evolved by Gaussian mixture model[J]. Chinese Journal of Computers, 2010, 33(7): 1295-1304. doi: 10.3724/SP.J.1016.2010.01295
    [15]
    LI Chun-ming, KAO C Y, GORE J C, et al. Minimization of region-scalable fitting energy for image segmentation[J]. IEEE Transactions on Image Processing, 2008, 17(10): 1940-1949. doi: 10.1109/TIP.2008.2002304
    [16]
    ZHANG Kai-hua, ZHANG Lei, SONG Hui-hui, et al. Activecontours with selective local or global segmentation: a newformulation and level set method[J]. Image and Vision Com-puting, 2010, 28(6): 668-676. https://www.sciencedirect.com/science/article/pii/S0262885609002303
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Get Citation
    PDF
    XML

    Article Metrics

    Article views (1028) PDF downloads(1080) Cited by()
    Proportional views
    Related

    Copyright《Journal of Traffic and Transportation Engineering》编辑部陕ICP备05001904号-1

    Address ：Editorial Department of Journal of Traffic and Transportation Engineering, Chang 'an University, Middle Section of South Second Ring Road, Xi 'an, Shaanxi（710064） Tel：029-82334388 Email：jygc@chd.edu.cn

    All visit:Today's visit:

    Supported by: Beijing Renhe Information Technology Co. Ltd    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return