Rail fastener automatic recognition method in complex background

Shengchun Wang; Peng Dai; Xinyu Du; Zichen Gu; Yufeng Ma

doi:10.1117/12.2503323

9 August 2018 Rail fastener automatic recognition method in complex background

Shengchun Wang, Peng Dai, Xinyu Du, Zichen Gu, Yufeng Ma

Proceedings Volume 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018); 1080625 (2018) https://doi.org/10.1117/12.2503323
Event: Tenth International Conference on Digital Image Processing (ICDIP 2018), 2018, Shanghai, China

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.

Citation Download Citation

Shengchun Wang, Peng Dai, Xinyu Du, Zichen Gu, and Yufeng Ma "Rail fastener automatic recognition method in complex background", Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 1080625 (9 August 2018); https://doi.org/10.1117/12.2503323

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