Multiscale Object Detection Method for Track Construction Safety Based on Improved YOLOv5

In track construction, it is an important and necessary guarantee for production safety to check the number of workers and tools before and after the track maintenance. In view of time-consuming, laborious, and low detection efficiency of traditional manual inspection way, we propose an improved YOLOv5 multiscale object detection algorithm for track construction safety in this paper. We improve, from Generalized Intersection over Union (GIoU) to Distance Intersection over Union (DIoU), the loss function for Bounding Box (BBox) regression to speed up the convergence of the model. And we also improve, from the Non-Maximum Suppression (NMS) to DIoU-NMS, the post-processing method to enhance the model’s detection ability for occluded objects and small objects. The experiment results on the Track Maintenance dataset (our self-prepared dataset) and MS COCO dataset show that the mAP value of our improved YOLOv5 algorithm is 94.8% and 38.7%, respectively. Compared with the original YOLOv5 algorithm, the mAP values on above datasets are increased by 5.1% and 5.4%, respectively. The validation experimental results on MS COCO dataset and Track Maintenance dataset indicate that the detection ability of our improved YOLOv5 algorithm for occluded objects and small objects is enhanced. The proposed algorithm can provide technical support for the real-time accurate detection of track construction workers and tools.