Detection of COVID-19 Pandemic Face Mask Using ConvNet in Busy Environments
- Authors: Veluchamy S.1, Rajeesh Kumar N.V.2, Srinivasan P.3, Nandhakumar A.4, K. G. Parthiban5
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View Affiliations Hide Affiliations1 Department of Electronics and Communication Engineering, Sri Venkadeswara College of Engineering and Technology, Coimbatore, India 2 Computer Science and Engineering, Amrita College of Engineering and Technology, Coimbatore, India 3 Department of Electronics and Communication Engineering, Amrita College of Engineering and Technology, Coimbatore, India 4 Department of Electronics and Communication Engineering, Dhaanish Ahmed Institute of Technology, Coimbatore, India 5 Department of Electronics and Communication Engineering, Dhaanish Ahmed Institute of Technology, Coimbatore, India
- Source: Intelligent Technologies for Research and Engineering , pp 50-66
- Publication Date: July 2024
- Language: English
Detection of COVID-19 Pandemic Face Mask Using ConvNet in Busy Environments, Page 1 of 1
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The number of people using face masks has increased on public transportation, retail outlets, and at the workplace. All municipal entrances, workplaces, malls, schools, and hospital gates must have temperature and mask checks in order for people to enter. The paper's goal is to find someone who isn't wearing a face mask in order to control COVID-19. ConvNets may be used to recognize and classify images. The model depends on ConvNot to assess whether or not someone is wearing a mask. It is possible to identify an image's face by utilizing a face identification algorithm. These faces are then processed using Conv Net face mask detection. If the model is able to extract patterns and characteristics from photographs, it will be categorized as either "Mask" or "No Mask". With an accuracy rate of 99.85 percent, Mobile Net V2 is the most accurate in regard to training data. MobilenetV2 correctly identifies the mask in "Mask" or "No Mask" video transmissions.
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