A method of mask wearing state detection based on YOLOv5

Abstract

The COVID-19 (Corona Virus Disease 2019) outbroke in 2019, and in order to stop the epidemic, wearing masks is a very critical part. The use of deep learning technology for mask wearing detection can improve the detection accuracy and reduce human and material resources. In this paper, the YOLOv5(You Only Look Once version 5) model is used for mask wearing detection. In the experimental validation phase, the performance of YOLOv5 is tested by using different methods, respectively. Finally, it is found that the detection performance is optimal with the training method of labelsmoothing, and the Mean Average Precision (mAP) can reach 0.9252.