A New Method on Mask-Wearing Detection for Natural Population Based on Improved YOLOv4
This work addresses mask-wearing compliance for public health during the COVID-19 pandemic, but it is incremental as it builds on existing YOLOv4 with specific enhancements.
The paper tackles automated mask-wearing detection in public places to address COVID-19 safety by proposing an improved YOLOv4 method, achieving a 4.06% AP increase over the baseline with a speed of 64.37 FPS.
Recently, the domestic COVID-19 epidemic situation is serious, but in 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 an important and complicated work, it is very necessary to carry out automated mask-wearing detection in public places. This paper proposes a new mask-wearing detection method based on 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 adaptively deploy the K-means clustering algorithm to make the nine anchor boxes more suitable for our NPMD dataset. The experiments show that the improved YOLOv4 performs better, exceeding the baseline by 4.06\% AP with a comparable speed of 64.37 FPS.