Article
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Target Detection of Safety Protective Gear Using the Improved YOLOv5
Version 1
: Received: 11 May 2024 / Approved: 12 May 2024 / Online: 13 May 2024 (12:15:09 CEST)
How to cite: Liu, H.; Qin, X. Target Detection of Safety Protective Gear Using the Improved YOLOv5. Preprints 2024, 2024050758. https://doi.org/10.20944/preprints202405.0758.v1 Liu, H.; Qin, X. Target Detection of Safety Protective Gear Using the Improved YOLOv5. Preprints 2024, 2024050758. https://doi.org/10.20944/preprints202405.0758.v1
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.
Keywords
Object detection; Construction industry; Safety devices; Computer vision; Deep learning
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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