Adaptive Road Crack Detection System by Pavement Classification

This paper presents a road distress detection system involving the phases needed to properly deal with fully automatic road distress assessment. A vehicle equipped with line scan cameras, laser illumination and acquisition HW-SW is used to storage the digital images that will be further processed to identify road cracks. Pre-processing is firstly carried out to both smooth the texture and enhance the linear features. Non-crack features detection is then applied to mask areas of the images with joints, sealed cracks and white painting, that usually generate false positive cracking. A seed-based approach is proposed to deal with road crack detection, combining Multiple Directional Non-Minimum Suppression (MDNMS) with a symmetry check. Seeds are linked by computing the paths with the lowest cost that meet the symmetry restrictions. The whole detection process involves the use of several parameters. A correct setting becomes essential to get optimal results without manual intervention. A fully automatic approach by means of a linear SVM-based classifier ensemble able to distinguish between up to 10 different types of pavement that appear in the Spanish roads is proposed. The optimal feature vector includes different texture-based features. The parameters are then tuned depending on the output provided by the classifier. Regarding non-crack features detection, results show that the introduction of such module reduces the impact of false positives due to non-crack features up to a factor of 2. In addition, the observed performance of the crack detection system is significantly boosted by adapting the parameters to the type of pavement.

[1]  Hungwen Li,et al.  Fast Hough transform: A hierarchical approach , 1986, Comput. Vis. Graph. Image Process..

[2]  Guoqiang Wang,et al.  A Novel Image Segmentation Method of Pavement Surface Cracks Based on Fractal Theory , 2008, 2008 International Conference on Computational Intelligence and Security.

[3]  Suebskul Phiphobmongkol,et al.  Crack Detection on Asphalt Surface Image Using Enhanced Grid Cell Analysis , 2008, 4th IEEE International Symposium on Electronic Design, Test and Applications (delta 2008).

[4]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[5]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[6]  E. Salari,et al.  Pavement distress detection and classification using feature mapping , 2010, 2010 IEEE International Conference on Electro/Information Technology.

[7]  Peisen S. Huang,et al.  Wavelet-Based Pavement Distress Classification , 2005 .

[8]  S Mcrobbie,et al.  TTS Research - Crack detection on local roads. Phase 1 , 2006 .

[9]  M. Avila,et al.  Detection of defects in road surface by a vision system , 2008, MELECON 2008 - The 14th IEEE Mediterranean Electrotechnical Conference.

[10]  Bo Shaobo,et al.  Pavement Crack Detection and Analysis for High-grade Highway , 2007, 2007 8th International Conference on Electronic Measurement and Instruments.

[11]  Tao Wang,et al.  Beamlet Transform Based Pavement Image Crack Detection , 2010, 2010 International Conference on Intelligent Computation Technology and Automation.

[12]  K. Shadan,et al.  Available online: , 2012 .

[13]  Wei Wei,et al.  Automatic Road Crack Image Preprocessing for Detection and Identification , 2009, 2009 Second International Conference on Intelligent Networks and Intelligent Systems.

[14]  Shivprakash Iyer,et al.  A robust approach for automatic detection and segmentation of cracks in underground pipeline images , 2005, Image Vis. Comput..

[15]  Xinyu Yang,et al.  Automation Recognition of Pavement Surface Distress Based on Support Vector Machine , 2009, 2009 Second International Conference on Intelligent Networks and Intelligent Systems.

[16]  Lou Jing,et al.  Pavement Crack Distress Detection Based on Image Analysis , 2010, 2010 International Conference on Machine Vision and Human-machine Interface.

[17]  Xinxin Niu,et al.  Automatic Recognition of Pavement Surface Crack Based on BP Neural Network , 2008, 2008 International Conference on Computer and Electrical Engineering.

[18]  Manuel Avila,et al.  Automatic detection and classification of defect on road pavement using anisotropy measure , 2009, 2009 17th European Signal Processing Conference.

[19]  Djelloul Ziadi,et al.  Supervised learning and automatic recognition of asphalt pavement deteriorations , 2009, AICCSA.

[20]  Guo Yanqing,et al.  Algorithm on Contourlet Domain in Detection of Road Cracks for Pavement Images , 2010, 2010 Ninth International Symposium on Distributed Computing and Applications to Business, Engineering and Science.

[21]  Peggy Subirats,et al.  A combined wavelet-based image processing method for emergent crack detection on pavement surface images , 2004, 2004 12th European Signal Processing Conference.

[22]  Alondra Chamorro,et al.  Data collection technologies for road management , 2006 .

[23]  Y. Y. Kim,et al.  A Technique Based on Image Processing for Measuring Cracks in the Surface of Concrete Structures , 2007 .

[24]  Xue Li,et al.  A Neural Network based Technique for Automatic Classification of Road Cracks , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[25]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[26]  Chun-Xia Zhao,et al.  Pavement Distress Detection Based on Nonsubsampled Contourlet Transform , 2008, 2008 International Conference on Computer Science and Software Engineering.

[27]  Xiangzhi Bai,et al.  Multi structuring element top-hat transform to detect linear features , 2010, IEEE 10th INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS.

[28]  Peggy Subirats,et al.  Automation of Pavement Surface Crack Detection using the Continuous Wavelet Transform , 2006, 2006 International Conference on Image Processing.

[29]  Changming Sun,et al.  Fast Linear Feature Detection Using Multiple Directional Non-Maximum Suppression , 2009, 18th International Conference on Pattern Recognition (ICPR'06).

[30]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[31]  Jiri Matas,et al.  Robust wide-baseline stereo from maximally stable extremal regions , 2004, Image Vis. Comput..

[32]  Changming Sun,et al.  Priority-Based Path Growing for Linear Feature Detection , 2007, 9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications (DICTA 2007).

[33]  R P Elliott,et al.  Investigation of image archiving for pavement surface distress survey , 1999 .

[34]  T. Saar,et al.  Automatic Asphalt pavement crack detection and classification using Neural Networks , 2010, 2010 12th Biennial Baltic Electronics Conference.

[35]  S. Hashimoto,et al.  Automated Crack Detection for Concrete Surface Image Using Percolation Model and Edge Information , 2006, IECON 2006 - 32nd Annual Conference on IEEE Industrial Electronics.

[36]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[37]  Chunxia Zhao,et al.  Pavement Cracks Detection Based on FDWT , 2009, 2009 International Conference on Computational Intelligence and Software Engineering.

[38]  Ezzatollah Salari,et al.  Beamlet transform based technique for pavement image processing and classification , 2009, 2009 IEEE International Conference on Electro/Information Technology.

[39]  Bugao Xu,et al.  Automatic inspection of pavement cracking distress , 2006, J. Electronic Imaging.

[40]  K H McGhee,et al.  AUTOMATED PAVEMENT DISTRESS COLLECTION TECHNIQUES , 2004 .

[41]  Jun-Hai Yong,et al.  Texture Analysis and Classification With Linear Regression Model Based on Wavelet Transform , 2008, IEEE Transactions on Image Processing.

[42]  Peggy Subirats,et al.  Introduction of a wavelet transform based on 2D matched filter in a Markov random field for fine structure extraction: application on road crack detection , 2009, Electronic Imaging.

[43]  H. S. Wolff,et al.  iRun: Horizontal and Vertical Shape of a Region-Based Graph Compression , 2022, Sensors.

[44]  Mohamed S Kaseko,et al.  COMPARISON OF TRADITIONAL AND NEURAL CLASSIFIERS FOR PAVEMENT-CRACK DETECTION. , 1994 .

[45]  Erkki Oja,et al.  A new curve detection method: Randomized Hough transform (RHT) , 1990, Pattern Recognit. Lett..

[46]  Anil K. Jain,et al.  Unsupervised texture segmentation using Gabor filters , 1990, 1990 IEEE International Conference on Systems, Man, and Cybernetics Conference Proceedings.

[47]  Sylvie Chambon,et al.  Road Crack Extraction with Adapted Filtering and Markov Model-based Segmentation - Introduction and Validation , 2010, VISAPP.