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http://dx.doi.org/10.6109/jkiice.2021.25.1.44

Mask Wearing Detection System using Deep Learning  

Nam, Chung-hyeon (Department of Computer Engineering, Korea University of Technology and Education)
Nam, Eun-jeong (Department of Computer Engineering, Korea University of Technology and Education)
Jang, Kyung-Sik (Department of Computer Engineering, Korea University of Technology and Education)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.25, no.1, 2021 , pp. 44-49 More about this Journal
Abstract
Recently, due to COVID-19, studies have been popularly worked to apply neural network to mask wearing automatic detection system. For applying neural networks, the 1-stage detection or 2-stage detection methods are used, and if data are not sufficiently collected, the pretrained neural network models are studied by applying fine-tuning techniques. In this paper, the system is consisted of 2-stage detection method that contain MTCNN model for face recognition and ResNet model for mask detection. The mask detector was experimented by applying five ResNet models to improve accuracy and fps in various environments. Training data used 17,217 images that collected using web crawler, and for inference, we used 1,913 images and two one-minute videos respectively. The experiment showed a high accuracy of 96.39% for images and 92.98% for video, and the speed of inference for video was 10.78fps.
Keywords
Deep learning; Image processing; Object detection; Image classification;
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