ISSN 0253-2778

CN 34-1054/N

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Open AccessOpen Access JUSTC Original Paper

A bridge crack image detection and classification method based on a climbing robot

  • 1.

    Department of Automation, University of Science and Technology of China, Hefei 230031, China

  • 2.

    Institute of Advanced Manufacturing Technology, Hefei Institute of Physical Science, Chinese Academy of Sciences, Changzhou 213164 China

  • 3.

    Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2016.09.011
  • Received Date: 12 September 2015
  • Accepted Date: 29 December 2015
  • Rev Recd Date: 29 December 2015
  • Publish Date: 30 September 2016
    Abstract

  • Abstract

    Traditional bridge crack detection methods are of high cost and high risk. A bridge crack detection and classification method was proposed based on a climbing robot using image analysis with a miniature camera mounted on the robot to collect images. First, the motion blur of acquired images was removed by Wiener filtering method. Second, wavelet transform was used to enhance the fractures of the crack in the image. Third, to complete crack image recognition, the surface morphology analysis is applied to extract crack fragments and then KD-tree was used to connect them. Finally, support vector machine method was used to classify crack images based on a series of basic visual characteristics and geometric features. Comparison of geometrical characteristic classification method and BP neural network classification method, results show that our method is better.

    Abstract

    Traditional bridge crack detection methods are of high cost and high risk. A bridge crack detection and classification method was proposed based on a climbing robot using image analysis with a miniature camera mounted on the robot to collect images. First, the motion blur of acquired images was removed by Wiener filtering method. Second, wavelet transform was used to enhance the fractures of the crack in the image. Third, to complete crack image recognition, the surface morphology analysis is applied to extract crack fragments and then KD-tree was used to connect them. Finally, support vector machine method was used to classify crack images based on a series of basic visual characteristics and geometric features. Comparison of geometrical characteristic classification method and BP neural network classification method, results show that our method is better.
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    • References(23)
  • [1]
    杨衍舒.桥底检测爬壁机器人控制系统开发[D].南京: 南京理工大学, 2013.
    [2]
    戴启凡.桥梁检测爬壁机器人及其自适应控制技术研究[D]. 硕士学位论文,南京: 南京理工大学, 2014.
    [3]
    LIU Y, DAI Q F, LIU Q C. Adhesion-adaptive control of a novel bridge-climbing robot[C]// Proceedings of 3rd Annual International Conference on Cyber Technology in Automation, Control and Intelligent System. Nanjing, China: IEEE Press, 2013: 102-107.
    [4]
    LA H M,GUCUNSKI N, KEE S H, et al. Autonomous robotic system for bridge deck data collection and analysis[C]// International Conference on Intelligent Robots and Systems. Chicago, USA: IEEE Press, 2014: 1950-1955.
    [5]
    LIM R S,LA H M, SHENG W H. A robotic crack inspection and mapping system for bridge deck maintenance[J]. IEEE Transactions on Automation Science and Engineering, 2014, 11(2): 367-378.
    [6]
    JEONG HO LEE,JONG MIN LEE, HYUNG JIN KIM, AND YOUNG SHIK MOON. Machine vision System for automatic inspection of bridges[C]// Congress on Image and Signal Processing. Sanya, China: IEEE Press, 2008: 363-366.
    [7]
    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.
    [8]
    ITO A, AOKI Y, HASHIMOTO S. Accurate extraction and measurement of fine cracks from concrete block surface image[C]// 28th Annual Conference of the Industrial Electronics Society. Sevilla, Spain: IEEE Press, 2002, 3: 2202-2207.
    [9]
    KAWAMURA K, MIYAMOTO A, NAKAMURA H, et al. Proposal of a crack pattern extraction method from digital images using an interactive genetic algorithm[J]. Proceedings of Japan Society of Civil Engineers, 2003, 742: 115-131.
    [10]
    YAMAGUCHI T, HASHIMOTO S. Automated crack detection for concrete surface image using percolation model and edge information[C]// 32nd Annual Conference on Industrial Electronics. Pairs, France: IEEE Press, 2006: 3355-3360.
    [11]
    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.
    [12]
    YAMAGUCHI T, HASHIMOTO S. Fast crack detection method for large-size concrete surface images using percolation-based imageprocessing[J]. Machine Vision and Applications, 2010, 21(5): 797-809.
    [13]
    马常霞. 基于图像分析的路面裂缝检测的关键技术研究[D]. 南京理工大学, 2012.
    [14]
    徐树奎. 基于计算摄影的运动模糊图像清晰化技术研究[D]. 国防科技大学, 2011.
    [15]
    田宏阳. 动态场景的视频分割算法研究[D]. 山东大学, 2007.
    [16]
    任亮,徐志刚,赵祥模, 等. 基于Prim最小生成树的路面裂缝连接算法[J]. 计算机工程, 2015, 41(1): 31-36, 43.
    REN L, XU Z G, ZHAO X M, et al. Pavement crack connection algorithm based on Prim minimum spanning tree[J]. Computer Engineering, 2015, 41(1): 31-36, 43.
    [17]
    栗琳. 基于视觉的高反射球面缺陷快速检测关键技术研究[D]. 天津大学, 2013.
    [18]
    BLUMER A, EHRENFEUCHT A, HAUSSLER D, et al. Learnability and the Vapnik-Chervonenkis dimension[J]. Journal of the Association for Computing Machinery, 1989, 36(4): 929-965.
    [19]
    FEI B, LIU J B. BINARY TREE OF SVM: A new fast multiclass training and classification algorithm[J]. IEEE Transactions on Neural Networks, 2006, 17(3): 696-704.
    [20]
    TAKAHASHI F, ABE S. Decision-tree-based multiclass support vector machines[C]// Proceedings of the 9th International Conference on Neural Information Processing. Singapore: IEEE Press, 2002, 3(3): 1418-1422.
    [21]
    CHANG C C, LIN C J. LIBSVM: A library for support vector machines[J]. ACM Transactions on Intelligent Systems and Technology, 2011, 2(3): 389-396.
    [22]
    赵柯. 路面裂缝图像自动识别系统研究[D]. 长安大学, 2009.
    [23]
    唐磊, 赵春霞, 王鸿南, 等. 基于图像分析的路面裂缝检测和分类[J]. 工程图学学报, 2008, 29(3): 99-104.
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    [1]
    杨衍舒.桥底检测爬壁机器人控制系统开发[D].南京: 南京理工大学, 2013.
    [2]
    戴启凡.桥梁检测爬壁机器人及其自适应控制技术研究[D]. 硕士学位论文,南京: 南京理工大学, 2014.
    [3]
    LIU Y, DAI Q F, LIU Q C. Adhesion-adaptive control of a novel bridge-climbing robot[C]// Proceedings of 3rd Annual International Conference on Cyber Technology in Automation, Control and Intelligent System. Nanjing, China: IEEE Press, 2013: 102-107.
    [4]
    LA H M,GUCUNSKI N, KEE S H, et al. Autonomous robotic system for bridge deck data collection and analysis[C]// International Conference on Intelligent Robots and Systems. Chicago, USA: IEEE Press, 2014: 1950-1955.
    [5]
    LIM R S,LA H M, SHENG W H. A robotic crack inspection and mapping system for bridge deck maintenance[J]. IEEE Transactions on Automation Science and Engineering, 2014, 11(2): 367-378.
    [6]
    JEONG HO LEE,JONG MIN LEE, HYUNG JIN KIM, AND YOUNG SHIK MOON. Machine vision System for automatic inspection of bridges[C]// Congress on Image and Signal Processing. Sanya, China: IEEE Press, 2008: 363-366.
    [7]
    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.
    [8]
    ITO A, AOKI Y, HASHIMOTO S. Accurate extraction and measurement of fine cracks from concrete block surface image[C]// 28th Annual Conference of the Industrial Electronics Society. Sevilla, Spain: IEEE Press, 2002, 3: 2202-2207.
    [9]
    KAWAMURA K, MIYAMOTO A, NAKAMURA H, et al. Proposal of a crack pattern extraction method from digital images using an interactive genetic algorithm[J]. Proceedings of Japan Society of Civil Engineers, 2003, 742: 115-131.
    [10]
    YAMAGUCHI T, HASHIMOTO S. Automated crack detection for concrete surface image using percolation model and edge information[C]// 32nd Annual Conference on Industrial Electronics. Pairs, France: IEEE Press, 2006: 3355-3360.
    [11]
    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.
    [12]
    YAMAGUCHI T, HASHIMOTO S. Fast crack detection method for large-size concrete surface images using percolation-based imageprocessing[J]. Machine Vision and Applications, 2010, 21(5): 797-809.
    [13]
    马常霞. 基于图像分析的路面裂缝检测的关键技术研究[D]. 南京理工大学, 2012.
    [14]
    徐树奎. 基于计算摄影的运动模糊图像清晰化技术研究[D]. 国防科技大学, 2011.
    [15]
    田宏阳. 动态场景的视频分割算法研究[D]. 山东大学, 2007.
    [16]
    任亮,徐志刚,赵祥模, 等. 基于Prim最小生成树的路面裂缝连接算法[J]. 计算机工程, 2015, 41(1): 31-36, 43.
    REN L, XU Z G, ZHAO X M, et al. Pavement crack connection algorithm based on Prim minimum spanning tree[J]. Computer Engineering, 2015, 41(1): 31-36, 43.
    [17]
    栗琳. 基于视觉的高反射球面缺陷快速检测关键技术研究[D]. 天津大学, 2013.
    [18]
    BLUMER A, EHRENFEUCHT A, HAUSSLER D, et al. Learnability and the Vapnik-Chervonenkis dimension[J]. Journal of the Association for Computing Machinery, 1989, 36(4): 929-965.
    [19]
    FEI B, LIU J B. BINARY TREE OF SVM: A new fast multiclass training and classification algorithm[J]. IEEE Transactions on Neural Networks, 2006, 17(3): 696-704.
    [20]
    TAKAHASHI F, ABE S. Decision-tree-based multiclass support vector machines[C]// Proceedings of the 9th International Conference on Neural Information Processing. Singapore: IEEE Press, 2002, 3(3): 1418-1422.
    [21]
    CHANG C C, LIN C J. LIBSVM: A library for support vector machines[J]. ACM Transactions on Intelligent Systems and Technology, 2011, 2(3): 389-396.
    [22]
    赵柯. 路面裂缝图像自动识别系统研究[D]. 长安大学, 2009.
    [23]
    唐磊, 赵春霞, 王鸿南, 等. 基于图像分析的路面裂缝检测和分类[J]. 工程图学学报, 2008, 29(3): 99-104.
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