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A railway fastener Inspection Method Based on Abnormal Sample Generation

Computer Modeling in Engineering & Sciences 2024年第4期139卷 565-592页

作者： Shubin Zheng Yue Wang Liming Li Xieqi Chen Lele Peng Zhanhao Shang Higher Vocational and Technical College Shanghai University of Engineering ScienceShanghai200437China School of Urban railway Transportation Shanghai University of Engineering ScienceShanghai201620China Shanghai Engineering Research Centre of Vibration and Noise Control Technologies for Rail Transit Shanghai University of Engineering ScienceShanghai201620China

Regular fastener detection is necessary to ensure the safety of ***,the number of abnormal fasteners is significantly lower than the number of normal fasteners in real *** supervised inspectionmethods have insufficient detection ability in cases of imbalanced *** 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 ***,a lightweight YOLOv5s is designed to achieve fast and precise localization of fastener ***,the foreground clip region of a fastener image is extracted by the designed fastener saliency detection network(F-SDNet),combined with data augmentation to generate a large number of abnormal fastener samples and balance the number of abnormal and normal ***,a fastener inspection model called fastener ResNet-8 is constructed by being trained with the augmented fastener *** show the effectiveness of our proposed method in solving the problem of sample imbalance in fastener *** and quantitative comparisons show that the proposed F-SDNet outperforms other state-of-the-art methods in clip region extraction,reaching MAE and max F-measure of 0.0215 and 0.9635,*** addition,the FPS of the fastener state inspection model reached 86.2,and the average accuracy reached 98.7%on 614 augmented fastener test sets and 99.9%on 7505 real fastener datasets.

关键词： railway fastener sample generation inspection model deep learning

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