Evaluating deep transfer learning models for face mask detection
Due to the fast transmission of coronavirus and the severe sequela of COVID-19, which has no specific cure, the world is facing a massive health crisis. According to the World Health Organization (WHO), wearing a mask in public locations and crowded locations is the most effective prevention of COVI...
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my-utar-eprints.50122022-12-26T14:13:43Z Evaluating deep transfer learning models for face mask detection Goh, Pei Jin QA76 Computer software Due to the fast transmission of coronavirus and the severe sequela of COVID-19, which has no specific cure, the world is facing a massive health crisis. According to the World Health Organization (WHO), wearing a mask in public locations and crowded locations is the most effective prevention of COVID-19. In Malaysia, wearing a face mask is mandatory in public areas. However, it is impossible to detect all passers-by manually as it requires much manpower. This research proposes an automation approach to maskwearing detection by identifying people who are (i) not wearing a mask, (ii) wearing a mask, (ii) incorrect mask-wearing, and (ii) wearing double masks. Transfer learning methods were adopted by using five pre-trained models: (i) VGG, (ii) MobileNet, (iii) ResNet, (iv) Inception and (v) Xception models. These models were trained based on 2000 real-life data sets collected from various sources with a data augmentation technique. The research results show that the pre-trained ResNet152 model outperformed the other models by achieving 0.8667 accuracy on the testing data set (120 images from the other distribution) and 0.8447 accuracy on the videos captured using a smartphone. 2022 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/5012/1/1802410_GOH_PEI_JIN.pdf Goh, Pei Jin (2022) Evaluating deep transfer learning models for face mask detection. Final Year Project, UTAR. http://eprints.utar.edu.my/5012/ |
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QA76 Computer software Goh, Pei Jin Evaluating deep transfer learning models for face mask detection |
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Due to the fast transmission of coronavirus and the severe sequela of COVID-19, which has no specific cure, the world is facing a massive health crisis. According to the World Health Organization (WHO), wearing a mask in public locations and crowded locations is the most effective prevention of COVID-19. In Malaysia, wearing a face mask is mandatory in public areas. However, it is impossible to detect all passers-by manually as it requires much manpower. This research proposes an automation approach to maskwearing detection by identifying people who are (i) not wearing a mask, (ii) wearing a mask, (ii) incorrect mask-wearing, and (ii) wearing double masks. Transfer learning methods were adopted by using five pre-trained models: (i) VGG, (ii) MobileNet, (iii) ResNet, (iv) Inception and (v) Xception models. These models were trained based on 2000 real-life data sets collected from various sources with a data augmentation technique. The research results show that the pre-trained ResNet152 model outperformed the other models by achieving 0.8667 accuracy on the testing data set (120 images from the other distribution) and 0.8447 accuracy on the videos captured using a smartphone. |
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Final Year Project / Dissertation / Thesis |
author |
Goh, Pei Jin |
author_facet |
Goh, Pei Jin |
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Goh, Pei Jin |
title |
Evaluating deep transfer learning models for face mask detection |
title_short |
Evaluating deep transfer learning models for face mask detection |
title_full |
Evaluating deep transfer learning models for face mask detection |
title_fullStr |
Evaluating deep transfer learning models for face mask detection |
title_full_unstemmed |
Evaluating deep transfer learning models for face mask detection |
title_sort |
evaluating deep transfer learning models for face mask detection |
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2022 |
url |
http://eprints.utar.edu.my/5012/1/1802410_GOH_PEI_JIN.pdf http://eprints.utar.edu.my/5012/ |
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1753793017718767616 |
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13.16051 |