13th Asia Pacific Transportation Development Conference
Subway Rail Fastener Locating Based on Visual Attention Model
Publication: Resilience and Sustainable Transportation Systems
ABSTRACT
Fastener defects detection based on computer vision is an important task in subway rail inspection systems, in which the first step is fastener location. In this paper, a fastener locating method based on visual attention model for subway track is presented, which can implement fastener location in the subway track mixed with ballast-less track in the main line and ballast track on turnout region. Firstly, rail location is obtained by the vertical projection algorithm. Secondly, the image is classified into ballast or ballast-less based on visual attention model with the fractal dimension. Thirdly, ballast image and ballast-less image are treated separately. On one hand, in the ballast image, the sleeper is located with the line segment detector algorithm, and then fastener can be located by the sleeper location combined with the rail location. On the other hand, in the ballast-less image, the fastener is located with horizontal projection algorithm combined with the rail location. Experimental results demonstrate that the proposed method can locate the fastener accurately.
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ACKNOWLEDGEMENTS
The authors are grateful to all the anonymous reviewers for their useful comments and suggestions to the original version of this paper. This work is supported by National Natural Science Foundation of China (Grant No. 51975347), Key Technology R&D Project of Shanghai Committee of Science and Technology (Grant No. 18030501300), and the Starting Program of Shanghai University of Engineering Science (Grant No. 0240-E3-0507-19-05138).
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Information & Authors
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Published In
Resilience and Sustainable Transportation Systems
Pages: 460 - 465
Editors: Fengxiang Qiao, Ph.D., Texas Southern University, Yong Bai, Ph.D., Marquette University, Pei-Sung Lin, Ph.D., University of South Florida, Steven I Jy Chien, Ph.D., New Jersey Institute of Technology, Yongping Zhang, Ph.D., California State Polytechnic University, and Lin Zhu, Ph.D., Shanghai University of Engineering Science
ISBN (Online): 978-0-7844-8290-2
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© 2020 American Society of Civil Engineers.
History
Published online: Jun 29, 2020
Published in print: Jun 29, 2020
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