Railway fastener defect detection based on deep convolutional networks

Ying Zhou; Xiaoqing Li; Hu Chen

doi:10.1117/12.2557231

3 January 2020 Railway fastener defect detection based on deep convolutional networks

Ying Zhou, Xiaoqing Li, Hu Chen

Author Affiliations +

Proceedings Volume 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019); 113732D (2020) https://doi.org/10.1117/12.2557231
Event: Eleventh International Conference on Graphics and Image Processing, 2019, Hangzhou, China

Abstract

Fasteners are the important components of railway system, which can be used to fix the tracks to the sleepers and reduce the likelihood of derailment. Nowadays, the extensively used approaches for the automatic detection of defective fasteners are vision-based approaches. However, they are not robust and efficient enough to be applied in reality. To solve this problem, this paper applies deep convolutional networks for the automatic detection of fastener defect and proposes a two-stage fastener defect detection framework. The framework is composed of a CenterNet-based fastener localization module and a VGG-based defect classification module. Besides,we innovatively introduce an attention mechanism named CBAM into localization network and an adaptive weighted softmax loss in classification network training procedure to elevate the accuracy of both modules. The experiment result shows that both methods have obviously improved the performance of the fastener defect detection system. The proposed localization network has a better accuracy-speed trade-off with 99.94% AP at 63 FPS on the test set. In addition, the proposed defect classification network has the best accuracy (up to 98.10%) on the test set and can be used to classify up to 5 categories of defects.

Citation Download Citation

Ying Zhou, Xiaoqing Li, and Hu Chen "Railway fastener defect detection based on deep convolutional networks", Proc. SPIE 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019), 113732D (3 January 2020); https://doi.org/10.1117/12.2557231

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