%0 Journal Article %A DING Pei %A ALIFU·Kuerban %A GENG Liting %A HAN Wenxuan %T Real-Time Face Mask Detection and Standard Wearing Recognition Method in Natural Environment %D 2021 %R 10.3778/j.issn.1002-8331.2106-0363 %J Computer Engineering and Applications %P 268-275 %V 57 %N 24 %X

The novel coronavirus has a strong infectious effect. It can prevent the spread of airborne droplets and aerosols by wearing masks standard. However, some people do not wear masks or wear masks irregularly in public. It is not conducive to the development of epidemic prevention and control work. In order to solve this problem, the article improves YOLOv4 and proposes a real-time face mask detection and standard wearing recognition method in natural environment. For the large number of model parameters and the difficult of deploy, a lightweight backbone network L-CSPDarkNet (Light CSPDarkNet) is proposed to improve the detection speed of the model, and a lightweight feature enhancement module Light-FEB(Light Feature Enhancement Black) and multi-scale attention are proposed. The Multi-Scale-Sam (Multi Scale Sam) module enhances the feature extraction capabilities of the lightweight backbone network. Experimental results show that the accuracy of the proposed method reaches 91.94%, which is 3.55 percentage points higher than the original YOLOv4, and the detection speed reaches 75 frames per second, which is higher than the 35 frames per second of the original YOLOv4, and the false detection has been improved.

%U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2106-0363