Target Detection of Safety Protective Gear Using the Improved YOLOv5
<|reference_start|>Target Detection of Safety Protective Gear Using the Improved YOLOv5: 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.<|reference_end|>
@article{liu2024target,
    title={Target Detection of Safety Protective Gear Using the Improved YOLOv5},
    author={Hao Liu and Xue Qin},
    journal={arXiv preprint arXiv:2408.05964},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.05964},
    primaryClass={cs.CV cs.LG}
}