Abstract
The world has faced a massive health emergency due to the prompt transmission of corona-virus (COVID-19) over the last two years. Since there is no specific treatment for COVID-19, infections have to be limited through prevention methods. Wearing a face mask is an effective preventive method in public areas. However, it is impractical to manually implement such regulations on big locations and trace any infractions. Automatic face mask detection facilitated by deep learning techniques provides a better alternative to this. This research introduced an automatic face detection system using ASUS Tinker single-board computer and MobileNetV2 model. As most of the publicly available face mask detection dataset was artificially generated, in this work, a real face mask detection dataset was first created consisting of a total of 300 images. The ASUS Tinker board’s model training and testing performance and training time have been assessed for this dataset and a publicly accessible dataset of 1376 images. The recommended system reached 99% of test accuracy, precision, recall, and f1-score for the newly collected dataset and 100% of test accuracy, precision, recall, and f1-score for the publicly available dataset.
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Ferdib-Al-Islam, Jahan, N., Rupa, F.Y., Sarkar, S., Hossain, S., Kabir, S.S. (2023). Performance Analysis of ASUS Tinker and MobileNetV2 in Face Mask Detection on Different Datasets. In: Satu, M.S., Moni, M.A., Kaiser, M.S., Arefin, M.S. (eds) Machine Intelligence and Emerging Technologies. MIET 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 490. Springer, Cham. https://doi.org/10.1007/978-3-031-34619-4_23
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