Automated vision inspection of rail surface cracks: A double-layer data-driven framework |
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| Institution: | 1. P6600, 6/F, Yeung Kin Man Academic Building, Department of Systems Engineering and Engineering Management, City University of Hong Kong, Hong Kong;2. School of Computer and Communication Engineering, University of Science and Technology Beijing (USTB), Beijing 100083, China;3. Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing 100083, China;1. School of Civil Engineering, Southwest Jiaotong University, Chengdu, China;2. Key Laboratory of High-speed Railway Engineering, Ministry of Education, Chengdu, China;3. Department of Civil and Environmental Engineering, Rutgers, The State University of New Jersey, NJ, USA;1. State Key Lab of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China;2. Department of Computer Science, Rutgers University, Piscataway, NJ 08854, USA;3. China Railway Research Institute Group Co., Beijing 100844, China;1. Shanghai Research Institute of Materials, 99 Handan Road, 200437 Shanghai, China;2. Shanghai Engineering Research Center of Earthquake Energy Dissipation, 99 Road Handan, Shanghai 200437, China;3. Shanghai Polytechnic University, 2360 Jin Hai Road, Shanghai 201209, China;4. École des Ponts ParisTech, Laboratoire Navier, 6-8 avenue Blaise-Pascal, Paris 77455, France;5. Department of Civil Engineering, Shanghai Normal University, 201418 Shanghai, China;1. Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing 100048, China;2. Beijing Key Lab of Transportation Data Analysis and Mining, Beijing Jiaotong University, Beijing 100044, China;3. College of Information Engineering, Capital Normal University, Beijing 100048, China;4. Infrastructure Inspection Research Institute, China Academy of Railway Sciences, Beijing 100081, China |
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| Abstract: | A double-layer data-driven framework for the automated vision inspection of the rail surface cracks is proposed in this paper. Based on images of rails, the proposed framework is capable to detect the location of cracks firstly and next automatically obtain the boundary of cracks via a feature-based linear iterative crack aggregation (FLICA). Extended Haar-like features are applied to develop significant features for identifying cracks in images. Built on extended Haar-like features, a cascading classifier ensemble integrating three single cascading classifiers with a major voting scheme is proposed to decide the presence of cracks in the image. Each single cascading classifier is composed of a sequence of stage classifiers trained by the LogitBoost algorithm. A scalable sliding window carrying the cascading classifier ensemble is applied to scan images of rail tracks, which is identified by the Otsu’s method, and detect cracks. After completing the crack registration in the first layer, the FLICA is developed to discover boundaries of cracks. The effectiveness of the proposed data-driven framework for identifying rail surface cracks is validated with the rail images provided by the China Railway Corporation and Hong Kong Mass Transit Railway (MRT). Six benchmarking methods, the Otsu’s method, mean shift, the visual detection system, the geometrical approach, fully convolutional networks and the U-net, are utilized to prove advantages of the proposed framework. Results of the validation and comparative analyses demonstrate that the proposed framework is most effective in the rail surface crack detection. |
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| Keywords: | Crack detection Rail system Cascading classifier Clustering Data-driven approach |
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