A model for face mask detection through deep learning

Abstract

Since the outbreak of the new coronavirus in China in 2019, wearing masks has gained widespread attention to prevent virus transmission. However, traditional face detection models have struggled to accurately detect faces covered by masks, posing a challenge for public health and security applications. In this study, we propose a novel lightweight model for face-mask detection, called YOLO-ARGhost, which is based on YOLOv4 and incorporates an attention mechanism to enhance accuracy. Our model is designed for fast face-mask detection, overcoming the limitations of previous models. Experimental evaluations on the AIZOO dataset demonstrate that our approach achieves an impressive mean average precision (mAP) of 92.8%.