Abstract: With the outbreak of the COVID-19 epidemic in 2020, wearing mask is one of the important measures to effectively suppress the rebound of the epidemic. It is of great practical significance to study the use of machine vision technology to detect whether face masks are worn or not. This paper proposes a face mask detection algorithm based on DCN-SERes-YOLOv3 to solve the problems of occlusion, small detection targets, unobvious feature information, and difficult identification of the target group when wearing masks in video image. Firstly, the algorithm uses the combination of ResNet50 and YOLOv3 to replace the backbone network with the ResNet50 residual network. In order to balance the accuracy and speed of the model, the convolutional layer in the residual block is improved and the average pooling layer is added to reduce the model’s loss and complexity, improve the detection speed. Secondly, the conventional convolution of the fourth residual block in the ResNet50 residual network is replaced with DCN deformable convolution to improve the model’s ability to adapt to geometric deformation when wearing masks. Finally, the SENet channel attention mechanism is introduced to enhance the ability to express characteristic information. The experimental results show that the average accuracy of the algorithm proposed in this paper is as high as 95.36%, which is about 4.1 percent point higher than the traditional YOLOv3 algorithm, and the detection speed is increased by 11.7 fps. The proposed algorithm improves the precision and the speed of the task of detecting faces wearing masks and has high application prospect.

Key words: mask wearing, YOLOv3 algorithm, ResNet50 residual network, channel attention mechanism, deformable convolution, epidemic prevention and control