A model of face mask wearing normality detection based on deep learning

Jin Dai; Yuemeng Lu; Xingxing Zhou

doi:10.1117/12.2636925

29 April 2022 A model of face mask wearing normality detection based on deep learning

Jin Dai, Yuemeng Lu, Xingxing Zhou

Proceedings Volume 12247, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2022); 122471L (2022) https://doi.org/10.1117/12.2636925
Event: 2022 International Conference on Image, Signal Processing, and Pattern Recognition, 2022, Guilin, China

Abstract

Regulating the wearing of masks is an important means to effectively prevent respiratory infectious diseases, and safety risks can be reduced by using machines instead of humans for mask wear testing. In this paper, we propose a deep learningbased mask wearing normality detector. This method uses featu-re pyramids to fuse multi-level features and employs a multiscale detection strategy to improve the detection accuracy of face land-marks and masks. In addition, a context sensitive predict module for facial landmarks and masks detection is also proposed. We compared the proposed model with Retina Net, YOLO4 and Faster R-CNN. The results show that the proposed model is superior to existing model for the normative detection of mask wearing.

Citation Download Citation

Jin Dai, Yuemeng Lu, and Xingxing Zhou "A model of face mask wearing normality detection based on deep learning", Proc. SPIE 12247, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2022), 122471L (29 April 2022); https://doi.org/10.1117/12.2636925

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