Research on Mask Wearing Detection of Natural Population Based on Improved YOLOv4

Recently, the domestic COVID-19 epidemic situation has been serious, but in some public places, some people do not wear masks or wear masks incorrectly, which requires the relevant staff to instantly remind and supervise them to wear masks correctly. However, in the face of such important and complicated work, it is necessary to carry out automated mask wearing detection in public places. This paper proposes a new mask wearing detection method based on the improved YOLOv4. Specifically, firstly, we add the Coordinate Attention Module to the backbone to coordinate feature fusion and representation. Secondly, we conduct a series of network structural improvements to enhance the model performance and robustness. Thirdly, we deploy the K-means clustering algorithm to make the nine anchor boxes more suitable for our NPMD dataset. The experimental results show that the improved YOLOv4 performs better, exceeding the baseline by 4.06% AP with a comparable speed of 64.37 FPS.