论文标题
基于改进的Yolov4
A New Method on Mask-Wearing Detection for Natural Population Based on Improved YOLOv4
论文作者
论文摘要
最近，国内Covid-19的流行状况很严重，但是在公共场所，有些人不会戴口罩或戴口罩不正确，这要求相关人员立即提醒和监督他们正确戴口罩。但是，面对如此重要且复杂的工作，非常有必要在公共场所进行自动面罩检测。本文提出了一种基于改进的Yolov4的新面具检测方法。具体而言，首先，我们将坐标注意模块添加到主链中以坐标特征融合和表示。其次，我们进行了一系列网络结构改进，以增强模型性能和鲁棒性。第三，我们自适应地部署K-均值聚类算法，以使九个锚点更适合我们的NPMD数据集。实验表明，改进的Yolov4的性能更好，超过4.06 \％AP，可比速度为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.
