YOLO-H: a lightweight object detection framework for helmet wearing detection
Jian Pan, Zhe Li, Yi Wei, Cong Huang, Dong Sheng Liang, Tong Lü, Zhibin Chen, Yingxiong Nong, Bin Zhou, Weiwei Liu · 2023
In construction, coal mining, tobacco manufacturing and other industries, wearing helmets is crucial safety measure for workers, and the monitoring of helmet wearing plays a significant role in maintaining production safety. However, manual monitoring demands substantial human, material and financial resources, and will suffer from low efficiency and are error prone. Therefore, we proposed a lightweight real-time deep learning-based detection framework called YOLO-H, for automatic helmet wearing detection. Our YOLO-H model was developed on the foundation of YOLOv5-n by introducing the state-of-the-art techniques such as re-parameterization, decoupled head, label assignment strategy and loss function. Our proposed YOLO-H performed more efficiently and effectively. On a private dataset, our proposed framework achieved 94.5% [email protected] and 65.2% [email protected]:0.95 with 82 FPS (Frames Per Second), which surpassed YOLOv5 by a large margin. Compared to other methods, our framework also showed overwhelming performance in terms of speed and accuracy. More importantly, the developed framework can be applied to other object detection scenarios.