Mask wearing detection algorithm based on improved Yolov7

Abstract

Manual inspection of the mask is too time-consuming and laborious. In order to detect whether a mask is worn in a crowded public place, a mask-wearing detection method based on improved YOLOV7 is proposed, which uses Depth wise separable convolution instead of conventional convolution, in order to integrate the local feature information and the whole image information deeply, Dilated Convolution was used to improve the Pyramid Pooling Module (DC-PPM) , at last, the loss function of target location is optimized, which makes it not only have the ability of feature extraction to fuse the whole and local information, but also have the ability of not losing the detail information. The experimental results show that the detection accuracy and speed of the algorithm are 95.07% and 79 frames/s respectively, which are 3.4% and 14 frames/s higher than the original YOLOV7 algorithm, very good to meet the actual application needs.