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Title	An Automated System To Limit Covid-19 Using Facial Mask Detection In Smart City Network
ID_Doc	7694
Authors	Rahman M.M.; Manik M.M.H.; Islam M.M.; Mahmud S.; Kim J.-H.
Year	2020
Published	IEMTRONICS 2020 - International IOT, Electronics and Mechatronics Conference, Proceedings
DOI	http://dx.doi.org/10.1109/IEMTRONICS51293.2020.9216386
Abstract	COVID-19 pandemic caused by novel coronavirus is continuously spreading until now all over the world. The impact of COVID-19 has been fallen on almost all sectors of development. The healthcare system is going through a crisis. Many precautionary measures have been taken to reduce the spread of this disease where wearing a mask is one of them. In this paper, we propose a system that restrict the growth of COVID-19 by finding out people who are not wearing any facial mask in a smart city network where all the public places are monitored with Closed-Circuit Television (CCTV) cameras. While a person without a mask is detected, the corresponding authority is informed through the city network. A deep learning architecture is trained on a dataset that consists of images of people with and without masks collected from various sources. The trained architecture achieved 98.7% accuracy on distinguishing people with and without a facial mask for previously unseen test data. It is hoped that our study would be a useful tool to reduce the spread of this communicable disease for many countries in the world. © 2020 IEEE.
Author Keywords	Convolutional Neural Network; COVID-19; Deep Learning; Facial Mask Detection; Smart City

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