Shubin Zheng1,3, Yue Wang2, Liming Li2,3,*, Xieqi Chen2,3, Lele Peng2,3, Zhanhao Shang2
CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 565-592, 2024, DOI:10.32604/cmes.2023.043832
Abstract Regular fastener detection is necessary to ensure the safety of railways. However, the number of abnormal fasteners is significantly lower than the number of normal fasteners in real railways. Existing supervised inspection methods have insufficient detection ability in cases of imbalanced samples. To solve this problem, we propose an approach based on deep convolutional neural networks (DCNNs), which consists of three stages: fastener localization, abnormal fastener sample generation based on saliency detection, and fastener state inspection. First, a lightweight YOLOv5s is designed to achieve fast and precise localization of fastener regions. Then, the foreground clip… More >
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