Automated Screening System for Covid Safety
Yarram Sai Subhash Reddy1, Sri Krishna Borra2, Koye Sai Vishnu Vamsi3, Nandipati Jaswanth Sai4, Koye Jayanth5

1Y Sai Subhash Reddy, Department of Computer Science Engineering, Vellore Institute of Technology, Vellore. (Tamil Nadu), India.
2Sri Krishna Borra, Department of Electronics and Communication Engineering, National Institute of Technology, Jamshedpur (Jharkhand), India.
3Koye Sai Vishnu Vamsi, Department of Computer Science Engineering, Vellore Institute of Technology, Vellore (Tamil Nadu), India.
4Nandipati Jaswanth Sai, Department of Computer Science Engineering, Vellore Institute of Technology, Vellore (Tamil Nadu), India.
5Koye Jayanth, Department of Computer Science Engineering, Vellore Institute of Technology, Vellore (Tamil Nadu), India.
Manuscript received on November 15, 2021. | Revised Manuscript received on November 17, 2021. | Manuscript published on December 30, 2021. | PP: 39-42 | Volume-11 Issue-2, December 2021. | Retrieval Number: 100.1/ijeat.B32661211221 | DOI: 10.35940/ijeat.B3266.1211221
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: COVID-19 is a life-threatening virus taking the lives of thousands of people every day throughout the world. Even though many organizations and companies worked hard and developed vaccines, production of vaccines at large scale to meet today’s demand is not an easy job as there is a shortage of raw materials and cases are rising steeply. Inoculation of every individual cannot be achieved in the foreseeable future. Even the government is vaccinating people in a phased manner prioritizing older people and people who are more vulnerable to the virus. The main objective of this work is to provide an optimum solution for COVID-19 indoor safety for industries, offices, and commercial places where footfall is high. This work focus on automation of temperature sensing and mask detection which is usually carried out by a person. Elimination of human intervention reduces the risk of contraction and spreading and avoids mistakes due to human negligence. Continuous monitoring of a person is not possible and there is no guarantee that a person who is entering a place wearing a mask puts it on until he leaves it. This research intends to implement mask detection along with surveillance which is cost effective as it does not require additional hardware setup.
Keywords: Arduino Uno, Computer Vision, COVID-19, Deep Learning, Convolutional Neural Networks