Target Detection of Safety Protective Gear Using the Improved YOLOv5
This work addresses safety compliance monitoring for railway construction workers, but it is incremental as it builds on YOLOv5 with specific enhancements.
The paper tackled the problem of detecting small and obstructed personal protective equipment in high-risk railway construction by proposing YOLO-EA, which improved precision to 98.9% and recall to 94.7%, up by 2.5% and 0.5% respectively, while maintaining real-time performance at 70.774 fps.
In high-risk railway construction, personal protective equipment monitoring is critical but challenging due to small and frequently obstructed targets. We propose YOLO-EA, an innovative model that enhances safety measure detection by integrating ECA into its backbone's convolutional layers, improving discernment of minuscule objects like hardhats. YOLO-EA further refines target recognition under occlusion by replacing GIoU with EIoU loss. YOLO-EA's effectiveness was empirically substantiated using a dataset derived from real-world railway construction site surveillance footage. It outperforms YOLOv5, achieving 98.9% precision and 94.7% recall, up 2.5% and 0.5% respectively, while maintaining real-time performance at 70.774 fps. This highly efficient and precise YOLO-EA holds great promise for practical application in intricate construction scenarios, enforcing stringent safety compliance during complex railway construction projects.