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.
@article{arxiv.2410.20699,
title = {CIB-SE-YOLOv8: Optimized YOLOv8 for Real-Time Safety Equipment Detection on Construction Sites},
author = {Xiaoyi Liu and Ruina Du and Lianghao Tan and Junran Xu and Chen Chen and Huangqi Jiang and Saleh Aldwais},
journal= {arXiv preprint arXiv:2410.20699},
year = {2024}
}