Automatic Inspection Method for YOLOv5s Railway Freight Vehicles Based on Channel Pruning

Railway freight safety inspection is an important component of railway transportation safety production. Traditional manual outdoor inspection methods are associated with issues such as high labor intensity, low efficiency, and the potential for oversight. To address these problems, this paper proposes a real-time automated railway freight vehicle inspection method based on channel pruning, aiming to improve the detection efficiency of freight trains and alleviate the pressure of manual inspection. First, a YOLOv5s model is constructed, consisting of functional modules such as Focus, BottleneckCSP, and SPP. Subsequently, the model is compressed using a channel pruning technique, reducing its size and enabling deployment on resource-constrained devices like small-scale machines. Finally, the model is adjusted to achieve quick and precise rail freight truck detection. The experimental findings indicate that the pruned model reduces the number of model parameters by 91.9%, decreases the model size by 11.23 MB, and achieves a mAP of 98.14%, which is only 0.183% less than the model without pruning. To demonstrate that the suggested approach is preferable, a comparison is conducted with the YOLOv5x, YOLOv5n, and YOLOv5m algorithms. The comparison results demonstrate that the proposed method has significantly faster forward inference times than YOLOv5x, YOLOv5m, and YOLOv5n, with reductions of 95.6 ms, 22 ms, and 3.4 ms, respectively. The model size is also smaller than YOLOv5x, YOLOv5m, and YOLOv5n by 168.43 MB, 39.15 MB, and 2.34 MB, respectively. Moreover, the mAP is only 0.15% and 0.07% lower than YOLOv5x and YOLOv5m, respectively. These findings show that the suggested method, which can be implemented on small devices, achieves automated inspection of railway freight cars while taking detection speed and model size into account.