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Rail fastener automatic recognition method in complex background
Author(s): Shengchun Wang; Peng Dai; Xinyu Du; Zichen Gu; Yufeng Ma
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Paper Abstract

The Integrated patrolling inspection train has been used worldwide for railway safety monitoring. The camera mounted under the train can capture the track image for abnormal fastener detection. For solving the high false positive alarm of rail fastener recognition arising from ballasts occlusion and non-uniform illumination, we proposed a fastener defect recognition method using deep learning model, and constructed four network structures based on AlexNet and ResNet to learn the fastener feature in complex background. The experimental results show that the RestNet18 network model with unfreezing convolutional layers not only performs well at the trained line, but also has good generalization at the new line, which is a more appropriate model for fastener recognition by comparison with the traditional handcraft feature and existing deep learning models.

Paper Details

Date Published: 9 August 2018
PDF: 8 pages
Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 1080625 (9 August 2018); doi: 10.1117/12.2503323
Show Author Affiliations
Shengchun Wang, China Academy of Railway Sciences (China)
Peng Dai, China Academy of Railway Sciences (China)
Xinyu Du, China Academy of Railway Sciences (China)
Zichen Gu, Beijing IMAP Technology Co., Ltd. (China)
Yufeng Ma, Virginia Polytechnic Institute and State Univ. (United States)


Published in SPIE Proceedings Vol. 10806:
Tenth International Conference on Digital Image Processing (ICDIP 2018)
Xudong Jiang; Jenq-Neng Hwang, Editor(s)

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