Lightweight brake disc bolt missing detection algorithm based on TEDS

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 [email protected] 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.