Improved mask wearing detection algorithm YOLOv7-DSC based on YOLOv7-tiny
This paper proposes an improved mask wearing detection algorithm YOLOv7 DSC based on YOLOv7 tiny to address the problems of poor real-time performance and difficulty in deploying masks to mobile devices in dense crowds.This algorithm combines the SE attention mechanism improved by deep separable convolution to design a lightweight feature extraction module,and combines BiFPN to design a weighted feature fusion module.Through ex-perimental verification,the mAP of YOLOv7-DSC algorithm on the mask data set is 96.9％,which is only 0.5％lower than that of YOLOv7-tiny algorithm.Compared with the YOLOv3-tiny,YOLOv4-tiny,YOLOv5s,Mobile-NetV3,ShuffleNetV2,GhostNet and Swin-Transformer algorithms,the mAP is 13.4％,11.2％,4.5％,5.7％,5.8％,4.2％,and 5.1％higher,respectively;When the detection accuracy is comparable to that of YOLOv7-tiny al-gorithm,the number of parameters and computation are reduced by 60％and 55％,respectively,to only 2.4M and 6.OG,which greatly reduces the hardware costs.
NETL
NSTL
万方数据
维普
