| Title |
An Automated System to Limit COVID-19 Using Facial Mask Detection in Smart City Network |
| ID_Doc |
37225 |
| Authors |
Rahman, MM; Manik, MMH; Islam, MM; Mahmud, S; Kim, JH |
| Title |
An Automated System to Limit COVID-19 Using Facial Mask Detection in Smart City Network |
| Year |
2020 |
| Published |
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| 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. |
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