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.
In order to solve the problem of lack of temporal and multi-scale information in gait recognition tasks based on gait energy images. A gait recognition method based on two-branch network is proposed. In the convolutional network branch, the attention mechanism and residual connection are combined to design a multi-scale feature extraction module to obtain effective multi-scale features. A simple temporal image is constructed and input into the long short-term memory network to extract temporal features. More accurate gait classification can be achieved by combining the two types of features. Experimental results on open gait dataset CASIA-B show that the proposed method has good classification effect.
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