  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.
