Lightweight brake disc bolt missing detection algorithm based on TEDS

Xin Lu; Hang Zhou; Yehong Chen; Yifan Zhang; Tu Lv

doi:10.1117/12.3049979

8 November 2024 Lightweight brake disc bolt missing detection algorithm based on TEDS

Xin Lu, Hang Zhou, Yehong Chen, Yifan Zhang, Tu Lv

Proceedings Volume 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024); 1341602 (2024) https://doi.org/10.1117/12.3049979
Event: 2024 4th International Conference on Advanced Algorithms and Neural Networks, 2024, Qingdao, China

Abstract

Brake discs are the critical component of high-speed train braking systems. To address the issue of missing brake disc bolts in high-speed trainsets, this study introduces an enhanced and lightweight fault detection approach utilizing the YOLOv5 network. The network replaces the backbone of the YOLOv5s model with the FasterNet architecture to serve as the feature extraction network. Furthermore, the Pconv convolution is employed to replace the C3 module in the Neck layer, substantially diminishing the model's parameter count to fulfill lightweight objectives. In response to the scarcity of fault samples, this paper proposes the integration of a PSA mechanism and the Focal EIoU loss function. This approach is designed to counteract the imbalance between positive and negative samples within the dataset, thereby increasing accuracy. The experimental findings indicate that the model presented in this study attains a precision rate of 96.48% on the high-speed train brake disc bolt missing fault dataset, with mAP@0.5 of 92.06%. Relative to the YOLOv5s, enhancements of 2.77% and 1.4% were observed, respectively. The model contains 1,012,832 parameters and achieves a detection speed of 16.19 FPS.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xin Lu, Hang Zhou, Yehong Chen, Yifan Zhang, and Tu Lv "Lightweight brake disc bolt missing detection algorithm based on TEDS", Proc. SPIE 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), 1341602 (8 November 2024); https://doi.org/10.1117/12.3049979

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