Face mask detection using AI: An approach to reduce risk of Corona virus spread

Face mask detection using AI: An approach to reduce risk of Corona virus spread

Registration ID: IJNRD_217515

Published ID: IJNRD2404148

DOI: Click Here to Get

SATHAMBAKAM KISHORE , RAYALA BABA SAI , SYED AKRAM HUSSAIN , PULLALAREVU SATHYANARAYANA REDDY

Face mask detection , Transfer learning , COVID-19 , Object deletion ,One-stage detector ,Two-stage detector

Effective strategies to restrain COVID-19 pandemic need high attention to mitigate negatively impacted communal health and global economy, with the brim-full horizon yet to unfold. In the absence of effective antiviral and limited medical resources, many measures are recommended by WHO to control the infection rate and avoid exhausting the limited medical resources. Wearing a mask is among the non-pharmaceutical intervention measures that can be used to cut the primary source of SARS-CoV2 droplets expelled by an infected individual. Regardless of discourse on medical resources and diversities in masks, all countries are mandating coverings over the nose and mouth in public. To contribute towards communal health, this paper aims to devise a highly accurate and real-time technique that can efficiently detect non-mask faces in public and thus, enforcing to wear mask. The proposed technique is ensemble of one-stage and two-stage detectors to achieve low inference time and high accuracy. We start with ResNet50 as a baseline and applied the concept of transfer learning to fuse high-level semantic information in multiple feature maps. In addition, we also propose a bounding box transformation to improve localization performance during mask detection. The experiment is conducted with three popular baseline models viz. ResNet50, AlexNet and MobileNet. We explored the possibility of these models to plug-in with the proposed model so that highly accurate results can be achieved in less inference time. It is observed that the proposed technique achieves high accuracy (98.2%) when implemented with ResNet50. Besides, the proposed model generates 11.07% and 6.44% higher precision and recall in mask detection when compared to the recent public baseline model published as RetinaFaceMask detector. The outstanding performance of the proposed model is highly suitable for video surveillance devices.

Published Paper   E-Certificate

"Face mask detection using AI: An approach to reduce risk of Corona virus spread", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.9, Issue 4, page no.b380-b393, April-2024, Available :https://ijnrd.org/papers/IJNRD2404148.pdf

Volume 9 Issue 4, April-2024

Pages : b380-b393

Paper Reg. ID: IJNRD_217515

Published Paper Id: IJNRD2404148

Downloads: 000121167

Research Area: Computer Engineering 

Country: Anantapur, ANDHRA PRADESH, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2404148.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2404148

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJNRD (IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition

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