Abstract: Under the situation of normalized epidemic prevention and control, there are dense crowds in railway stations, subway stations and other public places, which are prone to the spread of virus. Aiming at the problems of small mask targets, large amount of model parameters and difficult to deploy in crowded places, an improved lightweight structure and re-parameterized network is proposed. On the Retinaface algorithm, the dual cascade pyramid network is used to replace the original feature fusion network to enhance the feature information and to improve the detection effect of small-scale targets. At the same time, the structure re-parameterized network RepVGG is used to replace the original MobileNet0.25 backbone network. During model training, the residual structure is used to improve the feature extraction ability of the model. During model reasoning, the model parameters are reducedand the reasoning speed is improved by re-parameterization of the model structure. The experimental results show that the frame rate is 92.59 fps and the average accuracy rate (mAP) of the proposed algorithm on three different levels of verification sets of self-building data sets is 94.17%, 93.30%, 86.88%, which is 1.17 percentage points, 2.89 percentage points and 5.35 percentage points higher than the original Retinaface algorithm respectively. Mask wearing detection can be better carried out in natural scenes.

Key words: mask wearing detection, Retinaface algorithm, structural re-parameterization, feature fusion, lightweight