Target Detection of Safety Protective Gear Using the Improved YOLOv5

• URL: http://arxiv.org/abs/2408.05964v3
• Date: Tue, 5 Nov 2024 11:30:40 GMT
• Title: Target Detection of Safety Protective Gear Using the Improved YOLOv5
• Authors: Hao Liu, Xue Qin,
• Abstract summary: We propose YOLO-EA, an innovative model that enhances safety measure detection by integrating ECA into its backbone's convolutional layers. YOLO-EA's effectiveness was empirically substantiated using a dataset derived from real-world railway construction site surveillance footage.
• Score: 6.517811916515857
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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