Open AccessMASK DETECTION SYSTEM FOR COVID-19 SCENARIO USING COMPUTER VISION
Author(s) -
P. Bhanu Prasad,
Mukul Shende,
Mayur Karemore,
Lucky Khobragade,
Amit Dravyakar,
Davesh Bondre
Publication year - 2021
Publication title -
international journal of engineering applied science and technology
Language(s) - English
Resource type - Journals
ISSN - 2455-2143
DOI - 10.33564/ijeast.2021.v05i12.015
Subject(s) - covid-19 , artificial intelligence , computer science , face (sociological concept) , computer vision , pandemic , deep learning , closed circuit , detector , pixel , face masks , telecommunications , medicine , sociology , disease , infectious disease (medical specialty) , social science , pathology , virology , outbreak
The new pandemic of(Coronavirus Disease-2019) COVID-19continues to spread worldwide. Everypotential sector is experiencing a decline ingrowth. (World Health Organization) WHOsuggests that Wearing Face Mask can reducethe impact of COVID-19. So, This PaperProposed a system that controls the growth ofCOVID-19 by finding individuals who don'twear masks in populated areas like malls,markets where all public places are undersurveillance with closed-circuit televisioncameras (CCTV). When a person without amask is found, the corresponding authority isinformed by the CCTV network. And it cancalculate the number of people that do notwear the mask and emit an audible signal toinform the authority. A deep learning moduleis trained on a dataset composed of images ofpeople wearing different types of masks andpeople without masks collected from varioussources. It also contains some confusingimages that help the model to achieve greaterprecision than other models. This model willuse the dataset to build a COVID-19 facemask detector with computer vision usingComputer Vision. This approach allowedextracting even the details from the pixels