Convolutional neural network architecture for detecting facemask and social distancing: a preventive measure for COVID19
COVID-19 is a life-threatening virus which affected people at a global level in just a matter of few months and is highly contagious. In order to reduce its spread, SOPs must be followed, such as washing hands, wearing face masks, and maintaining social distance. Hence, to aid the strict follow up o...
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Springer Science and Business Media Deutschland GmbH
2022
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| _version_ | 1796866882559541248 |
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| author | Malik, Najeeb Ur Rehman Abu Bakar, Syed A. R. Sheikh, Usman Ullah Airij, Awais Gul |
| author_facet | Malik, Najeeb Ur Rehman Abu Bakar, Syed A. R. Sheikh, Usman Ullah Airij, Awais Gul |
| author_sort | Malik, Najeeb Ur Rehman |
| collection | ePrints |
| description | COVID-19 is a life-threatening virus which affected people at a global level in just a matter of few months and is highly contagious. In order to reduce its spread, SOPs must be followed, such as washing hands, wearing face masks, and maintaining social distance. Hence, to aid the strict follow up of SOPs, this paper proposes a system to detect whether the people are wearing face masks and maintaining social distance or not in order to break the chain of COVID 19. The proposed system uses Deep Learning (DL) model based on Convolutional Neural Network (CNN) architecture for training the facemask detector and OpenPose 2D skeleton extraction technique for detecting social distance. A DL model based on a 7-layered CNN architecture was proposed in this research to detect masked and unmasked faces. Based on the proposed technique, 99.98% validation and 99.98% testing accuracies were achieved. In addition to that, the maintenance of social distance which is the new normal nowadays was also detected using the images obtained from the internet as currently, there is no such database available for detecting social distancing. |
| first_indexed | 2024-03-05T21:18:50Z |
| format | Book Section |
| id | utm.eprints-100452 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T21:18:50Z |
| publishDate | 2022 |
| publisher | Springer Science and Business Media Deutschland GmbH |
| record_format | dspace |
| spelling | utm.eprints-1004522023-04-14T01:54:07Z http://eprints.utm.my/100452/ Convolutional neural network architecture for detecting facemask and social distancing: a preventive measure for COVID19 Malik, Najeeb Ur Rehman Abu Bakar, Syed A. R. Sheikh, Usman Ullah Airij, Awais Gul TK Electrical engineering. Electronics Nuclear engineering COVID-19 is a life-threatening virus which affected people at a global level in just a matter of few months and is highly contagious. In order to reduce its spread, SOPs must be followed, such as washing hands, wearing face masks, and maintaining social distance. Hence, to aid the strict follow up of SOPs, this paper proposes a system to detect whether the people are wearing face masks and maintaining social distance or not in order to break the chain of COVID 19. The proposed system uses Deep Learning (DL) model based on Convolutional Neural Network (CNN) architecture for training the facemask detector and OpenPose 2D skeleton extraction technique for detecting social distance. A DL model based on a 7-layered CNN architecture was proposed in this research to detect masked and unmasked faces. Based on the proposed technique, 99.98% validation and 99.98% testing accuracies were achieved. In addition to that, the maintenance of social distance which is the new normal nowadays was also detected using the images obtained from the internet as currently, there is no such database available for detecting social distancing. Springer Science and Business Media Deutschland GmbH 2022 Book Section PeerReviewed Malik, Najeeb Ur Rehman and Abu Bakar, Syed A. R. and Sheikh, Usman Ullah and Airij, Awais Gul (2022) Convolutional neural network architecture for detecting facemask and social distancing: a preventive measure for COVID19. In: Proceedings of the 11th International Conference on Robotics, Vision, Signal Processing and Power Applications Enhancing Research and Innovation through the Fourth Industrial Revolution. Lecture Notes in Electrical Engineering, 829 (NA). Springer Science and Business Media Deutschland GmbH, Singapore, pp. 942-947. ISBN 978-981168128-8 http://dx.doi.org/10.1007/978-981-16-8129-5_144 DOI:10.1007/978-981-16-8129-5_144 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Malik, Najeeb Ur Rehman Abu Bakar, Syed A. R. Sheikh, Usman Ullah Airij, Awais Gul Convolutional neural network architecture for detecting facemask and social distancing: a preventive measure for COVID19 |
| title | Convolutional neural network architecture for detecting facemask and social distancing: a preventive measure for COVID19 |
| title_full | Convolutional neural network architecture for detecting facemask and social distancing: a preventive measure for COVID19 |
| title_fullStr | Convolutional neural network architecture for detecting facemask and social distancing: a preventive measure for COVID19 |
| title_full_unstemmed | Convolutional neural network architecture for detecting facemask and social distancing: a preventive measure for COVID19 |
| title_short | Convolutional neural network architecture for detecting facemask and social distancing: a preventive measure for COVID19 |
| title_sort | convolutional neural network architecture for detecting facemask and social distancing a preventive measure for covid19 |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
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