Image-based Method for Concrete Bridge Crack Detection ?

This paper presents a modifled image processing method for detecting cracks in surface images of concrete bridges. Clip, fllling and rotation transformation are applied on concrete images for precise extracting cracks. Modifled C-V model, adaptive threshold, morphology, C-V model and Canny are compared on segmenting crack images which are gathered in difierent illumination. Analysis results indicated that the misclassiflcation rate of the modifled C-V model is 3.56% and the operation time is 96 ms. Therefore, the proposed method can be used for the accurate detection of cracks in surface images recorded under various conditions. Moreover, the estimated crack widths are in good agreement with those measured manually.

[1]  Ikhlas Abdel-Qader,et al.  ANALYSIS OF EDGE-DETECTION TECHNIQUES FOR CRACK IDENTIFICATION IN BRIDGES , 2003 .

[2]  Paul Fieguth,et al.  Automated detection of cracks in buried concrete pipe images , 2006 .

[3]  Chunming Li,et al.  Minimization of Region-Scalable Fitting Energy for Image Segmentation , 2008, IEEE Transactions on Image Processing.

[4]  Chang-Soo Han,et al.  Auto inspection system using a mobile robot for detecting concrete cracks in a tunnel , 2007 .

[5]  Jeong Ho Lee,et al.  Bridge inspection robot system with machine vision , 2009 .

[6]  Ioannis Brilakis,et al.  Visual retrieval of concrete crack properties for automated post-earthquake structural safety evaluation , 2011 .

[7]  Tara C. Hutchinson,et al.  Improved image analysis for evaluating concrete damage , 2006 .

[8]  Xiao Chun.  Texture Image Segmentation Using Level Set Function Evolved by Gaussian Mixture Model , 2010 .

[9]  S. S. Law,et al.  Wavelet-based crack identification of bridge beam from operational deflection time history , 2006 .

[10]  Amir Averbuch,et al.  Color image segmentation based on adaptive local thresholds , 2005, Image Vis. Comput..

[11]  Shuji Hashimoto,et al.  Fast crack detection method for large-size concrete surface images using percolation-based image processing , 2010, Machine Vision and Applications.

[12]  Shivprakash Iyer,et al.  Segmentation of Pipe Images for Crack Detection in Buried Sewers , 2006, Comput. Aided Civ. Infrastructure Eng..

[13]  Michele Dilena,et al.  Dynamic testing of a damaged bridge , 2011 .

[14]  Lei Zhang,et al.  Active contours with selective local or global segmentation: A new formulation and level set method , 2010, Image Vis. Comput..

[15]  Qingquan Li,et al.  CrackTree: Automatic crack detection from pavement images , 2012, Pattern Recognit. Lett..