An Automated Covid-19 Face Mask Detection and Warning System with Deep Learning

Authors

  • Mrs. P. Bhuvaneshwari  Assistant Professor, Department of Information Technology, Muthayammal Engineering College (Autonomous), Tamilnadu, India
  • Dr. E. Punarselvam  Professor and Head of the Department, Department of Information Technology, Muthayammal Engineering College (Autonomous), Tamilnadu, India
  • Ms. S. Janani  Department of Information Technology, Muthayammal Engineering College (Autonomous), Tamilnadu, India
  • Ms. R. Kaviya  Department of Information Technology, Muthayammal Engineering College (Autonomous), Tamilnadu, India
  • Ms. C. SriRanjani  Department of Information Technology, Muthayammal Engineering College (Autonomous), Tamilnadu, India

DOI:

https://doi.org/10.32628/IJSRST218258

Keywords:

Facial Mask Detection, COVID-19, Deep Learning, Convolutional Neural Network(CNN), Regional based Convolutional Neural Network(RCNN), Smart City

Abstract

The corona virus COVID-19 pandemic is causing a global health crisis so the effective protection methods are wearing a face mask in public areas according to the World Health Organization (WHO). The COVID-19 pandemic forced governments across the world to impose lockdowns to prevent virus transmissions. Reports indicate that wearing face masks while at work clearly reduces the risk of transmission. As the result, to create an efficient and economic approach of using Artificial Intelligence (AI)for safe environment in a manufacturing setup. A hybrid model using deep and classical machine learning for face mask detection will be presented. A face mask detection dataset consists of with mask and without mask images, by using OpenCV to do real-time face detection from a live stream via our webcam. The use of dataset is to build a COVID-19 face mask detector with computer vision using Python, OpenCV, and Tensor Flow and Keras. The goal is to identify whether the person on video stream is wearing a face mask or not with the help of computer vision and (RCNN) deep learning.

References

  1. Mohammad Marufur Rahman, Hossen Manik, Milon Islam. “An Automated System to Limit COVID-19 Using Facial Mask Detection in Smart City Network.” 978-1-7281-9615-2/20 Oct.2020
  2. Amith verma, Toshanal Meenpal, Asthosh Balakrishnan, “Facial Mask detection Using Semantic /segmentation”, ICCCs,2019.
  3. L.Calavia, C. Baladrón, J. M. Aguiar, B. Carro, and A. Sánchez- Esguevillas, “A Semantic Autonomous Video Surveillance System for Dense Camera Networks in Smart Cities,” Sensors, vol. 12, no. 8, pp. 10407–10429, Aug. 2012.
  4. M. Z. Islam, M. M. Islam, and A. Asraf, “A Combined Deep CNN- LSTM Network for the Detection of Novel Coronavirus (COVID-19) Using X-ray Images,” Informatics in Medicine Unlocked, vol. 20, pp. 100412, Aug. 2020.
  5. S. Feng, C. Shen, N. Xia, W. Song, M. Fan, and B. J. Cowling, “Rational use of face masks in the covid 19 pandemic,” The Lancet Respiratory Medicine, 2020
  6. Jinsu Lee, Sang-Kwang Lee, seong- Yang.” An Ensemble Method of CNN Models for Object Detection”,IEEE,2018.
  7. L. Li et al., “COVID-19 patients’ clinical characteristics, discharge rate, and fatality rate of meta-analysis,” J. Med. Virol., vol. 92, no. 6, pp. 577–583, Jun.2020.
  8. L.Liuetal., “Deep Learning for Generic Object Detection: A Survey,” Int. J. Comput. Vis., vol. 128, no. 2, pp. 261–318, Sep. 2018.
  9. R. P. Singh, M. Javaid, A. Haleem, and R. Suman, “Internet of things (IoT) applications to fight against COVID-19 pandemic,” DiabetesMetab. Syndr. Clin. Res. Rev., vol. 14, no. 4, pp. 521–524, Jul.2020.
  10. X. Wang, X. Le, and Q. Lu, “Analysis of China’s Smart City Upgrade and Smart Logistics Development under the COVID-19 Epidemic,” J. Phys. Conf. Ser., vol. 1570, p. 012066,2020.
  11. J.WonSonn and J K.Lee, “The smart city as time-space cartographer in COVID-19 control: the South Korean strategy and democratic control of surveillance technology,” Eurasian Geogr. Econ., pp. 1– 11, May.2020.
  12. Y. Fang, Y. Nie, and M. Penny, “Transmission dynamics of the covid-19 outbreak and effectiveness of governmentinterventions: A datadriven analysis,”Journal of medical virology, vol. 92, no. 6, pp. 645–659, 2020.
  13. Y. Liu, A. A. Gayle, A. Wilder-Smith, and J. Rocklöv, “The reproductive number of covid-19 is higher compared to sars coronavirus,”Journal of travel medicine, 2020.
  14. S. Feng, C. Shen, N. Xia, W. Song, M. Fan, and B. J. Cowling, “Rational use of face masks in the covid19pandemic,”The Lancet Respiratory Medicine, 2020.
  15. Sebastian Handrich,” Face Attribute Detection with MobileNetV2 and NasNet- Mobile”, IEEE,2018.
  16. G. Halegoua, “Smart City Technologies,” Smart Cities,doi: 10.7551/mitpress/11426.003.0005, 2020

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Published

2021-04-30

Issue

Section

Research Articles

How to Cite

[1]
Mrs. P. Bhuvaneshwari, Dr. E. Punarselvam, Ms. S. Janani, Ms. R. Kaviya, Ms. C. SriRanjani "An Automated Covid-19 Face Mask Detection and Warning System with Deep Learning " International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 8, Issue 2, pp.382-386, March-April-2021. Available at doi : https://doi.org/10.32628/IJSRST218258