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

Xiaoyi Liu, Ruina Du, Lianghao Tan, Junran Xu, Chen Chen +2 more
10/28/2024
cs.CV

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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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Cite this paper

@article{liu2024cibseyolov,
  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},
  year   = {2024},
  eprint = {2410.20699},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2410.20699v1}
}

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