Comparative Analysis of Object Detection Algorithms for Face Mask Detection
Rohan Kanotra1, Akash, Neelendu Wadhwa2, N. Jeyanthi3

1Rohan Kanotra*, Member of Technical Staff-1, VMWare, Bangalore, India. 2Akash, Analyst, BlackRock, Bangalore, India.
3Neelendu Wadhwa, Intern, Octonius, India
4Dr. N. Jeyanthi, Associate Professor Senior, School of Information Technology and Engineering, VIT, Vellore, India.

Manuscript received on February 22, 2021. | Revised Manuscript received on March 08, 2021. | Manuscript published on April 30, 2021. | PP: 148-151 | Volume-10 Issue-4, April 2021. | Retrieval Number: 100.1/ijeat.C22840210321 | DOI: 10.35940/ijeat.C2284.0410421
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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 has made mankind see unprecedented and unbelievable times with millions of people being affected due to it. Multiple countries have started vaccinating their populations in the hope that it will end the pandemic. Given the inequitable access to vaccines across the world and the highly mutating coronavirus it remains to be seen when will everyone get access to vaccines and how effective the vaccines might prove over the virus variants. Therefore, standard COVID behaviour is here to stay for some time. Wearing face masks is one such etiquette which greatly reduces risk of getting infected. Employing public face mask detection systems has helped multiple countries to bring the pandemic under control. In this paper we have done a quantitative analysis of different object detection algorithms namely ResNet,MobileNetV2 and CNN on face mask detection on accuracy and recall parameters using an unbiased, large and diverse dataset in order the algorithm which can be applied on a mass scale.
Keywords: COVID, Coronavirus, Pandemic, Object Detection, Res Net, MobileNetV2, CNN