An improved YOLO V5-based algorithm of safety helmet wearing detection

Detecting whether workers in surveillance images and videos are following regulations to wear safety helmets plays a vital role in reducing safety accidents on a construction site. Although some recent algorithms can fulfill this task with the help of emerging deep learning techniques. The detection accuracy is still hard to meet the needs of real applications. So, a new accurate and real-time method for safety helmet wearing detection is proposed. Firstly, the YOLO V5 network is improved by combining implicit and explicit information to enhance the context-aware ability. Then, based on the improved network, an accurate, fast, and stable safety helmet wearing detection algorithm is proposed. Finally, a series of experiments are performed to validate the proposed algorithm. It shows that the proposed algorithm outperforms the state-of-the-art methods.