Face occlusion detection algorithm based on yolov5

The current face-mask recognition detection algorithm during the epidemic only distinguishes between wearing or not wearing a mask. Such detection often has certain loopholes, such as using other objects to cover their mouths and noses instead of masks to cheat the detection. To address such problems, this paper proposes a YOLOv5 based face occlusion detection algorithm, which is modified based on the YOLOv5 algorithm by improving the loss function as DIoU and increasing the experimental samples by introducing multiple data sets to improve the object detection effect. The experimental results show that the improved YOLOv5 algorithm has improved the object detection effect for different kinds of face occlusions, which verifies the method’s effectiveness.