基于DCN-SERes-YOLOv3的人脸佩戴口罩检测算法

2020年新冠疫情爆发，佩戴口罩是有效抑制疫情反弹的重要措施之一，研究利用机器视觉技术检测人脸是否佩戴口罩有重要的现实意义。本文针对视频图像中人脸佩戴口罩时存在遮挡、检测目标较小、特征信息不明显、目标靠近群体不易识别等问题，提出一种基于DCN-SERes-YOLOv3的人脸佩戴口罩检测算法。首先，采用ResNet50与YOLOv3相结合的方式，将主干网络替换为ResNet50残差网络，为了平衡模型的精度与速度，对残差块中的卷积层改进并加入平均池化层，降低模型的损失与复杂度，提高检测速度；其次，将ResNet50残差网络中第4个残差块的常规卷积替换为DCN可变形卷积，提高模型适应人脸佩戴口罩时发生几何形变的能力；最后，引入SENet通道注意力机制，增强特征信息的表达能力。实验结果表明，本文算法的平均精度值高达95.36%，比传统YOLOv3算法提高了约4.1个百分点，且检测速度提高了11.7 fps，本文算法提高了检测人脸佩戴口罩任务的精度与速度，有较好的应用前景。

Face Mask Detection Algorithm Based on DCN-SERes-YOLOv3

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.