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.

翻译：近期国内新冠肺炎疫情形势严峻，但在公共场所仍存在部分人员未佩戴口罩或佩戴口罩不规范的现象，需要相关工作人员即时提醒并监督其正确佩戴。面对如此重要且繁杂的工作，在公共场所开展自动化口罩佩戴检测显得尤为必要。本文提出一种基于改进YOLOv4的口罩佩戴检测新方法。具体而言：首先，我们在主干网络中引入坐标注意力模块以协调特征融合与表征；其次，我们实施了一系列网络结构改进以增强模型性能与鲁棒性；最后，我们自适应部署K-means聚类算法，使九个锚框更适配我们的NPMD数据集。实验表明，改进后的YOLOv4模型性能显著提升，在保持64.37 FPS相近速度的同时，平均精度（AP）较基线模型提高4.06%。