Mask detection and classification with different convolutional neural networks

Qianru Li; Chenyu Song; Xinhong Xie

doi:10.1117/12.2640896

13 October 2022 Mask detection and classification with different convolutional neural networks

Qianru Li, Chenyu Song, Xinhong Xie

Proceedings Volume 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022); 1228703 (2022) https://doi.org/10.1117/12.2640896
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 2022, Wuhan, China

Abstract

Since 2019, the COVID-19 has been hanging over the whole world, causing uncountable financial loss. In this regard, wearing masks becomes a precaution for the public. However, some people are wearing masks in a wrong way, which may cause virus infection. To detect the wrong wearing of masks, we use 3 classic Convolutional Neural Networks, namely LeNet-5, AlexNet, and VGGNet-16, based on a unique dataset, to train the model and analyze the results. On the unique dataset, LeNet-5 achieved an accuracy of 80.3%, which was the lowest among the three networks, AlexNet attained an accuracy of 90.6%, which is near the precision of VGGNet-16, 92.83%. This work may help the advance of a digital city, making COVID-19 precaution under control.

Citation Download Citation

Qianru Li, Chenyu Song, and Xinhong Xie "Mask detection and classification with different convolutional neural networks", Proc. SPIE 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 1228703 (13 October 2022); https://doi.org/10.1117/12.2640896

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