Research on track personnel intrusion detection algorithm based on YOLOv5

To address the issues of image blurriness in railway scenarios and the failure to detect small and occluded targets, an improved person detection algorithm based on YOLOv5 is proposed. The feature extraction capability of YOLOv5 is insufficient, and it lacks sensitivity to certain information. To improve these shortcomings, the Convolutional Block Attention Module (CBAM) is introduced into the original YOLOv5 algorithm to enhance feature extraction capabilities and mitigate the problem of missed detections for occluded and small targets. Additionally, the SIoU loss function is used in place of the CIoU loss function as the bounding box regression loss function to accelerate model convergence and improve bounding box localization accuracy. Experimental results show that the average detection accuracy of the model reaches 99.4%, and the detection speed is 41 frames per second, enabling rapid and accurate detection of foreign objects on the railway, meeting the requirements for real-time target detection.