CIB-SE-YOLOv8: Optimized YOLOv8 for Real-Time Safety Equipment Detection on Construction Sites

• URL: http://arxiv.org/abs/2410.20699v1
• Date: Mon, 28 Oct 2024 03:07:03 GMT
• Title: CIB-SE-YOLOv8: Optimized YOLOv8 for Real-Time Safety Equipment Detection on Construction Sites
• Authors: Xiaoyi Liu, Ruina Du, Lianghao Tan, Junran Xu, Chen Chen, Huangqi Jiang, Saleh Aldwais,
• Abstract summary: This study presents a computer vision-based solution using YOLO for real-time helmet detection. Our proposed CIB-SE-YOLOv8 model incorporates SE attention mechanisms and modified C2f blocks, enhancing detection accuracy and efficiency.
• Score: 4.028949797830281
• License:
• Abstract: Ensuring safety on construction sites is critical, with helmets playing a key role in reducing injuries. Traditional safety checks are labor-intensive and often insufficient. This study presents a computer vision-based solution using YOLO for real-time helmet detection, leveraging the SHEL5K dataset. Our proposed CIB-SE-YOLOv8 model incorporates SE attention mechanisms and modified C2f blocks, enhancing detection accuracy and efficiency. This model offers a more effective solution for promoting safety compliance on construction sites.

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