Application of deep learning and machine learning models to detect COVID-19 face masks - A review
The continuous COVID-19 upsurge and emerging variants present unprecedented challenges in many health systems. Many regulatory authorities have instituted the mandatory use of face masks especially in public places where massive contact of people is frequent and inevitable, particularly inside publi...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co. Ltd.
2021-01-01
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| Series: | Sustainable Operations and Computers |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666412721000325 |
| _version_ | 1797976483927425024 |
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| author | Elliot Mbunge Sakhile Simelane Stephen G Fashoto Boluwaji Akinnuwesi Andile S Metfula |
| author_facet | Elliot Mbunge Sakhile Simelane Stephen G Fashoto Boluwaji Akinnuwesi Andile S Metfula |
| author_sort | Elliot Mbunge |
| collection | DOAJ |
| description | The continuous COVID-19 upsurge and emerging variants present unprecedented challenges in many health systems. Many regulatory authorities have instituted the mandatory use of face masks especially in public places where massive contact of people is frequent and inevitable, particularly inside public transport facilities, sports arenas, shopping malls and workplaces. However, compliance and adherence to proper wearing of face masks have been difficult due to various reasons including diversified mask types, different degrees of obstructions, various variations, balancing various model detection accuracy or errors and deployment requirements, angle of view, deployment of detection model on computers with limited processing power, low-resolution images, facial expression, and lack of real-world dataset. Therefore, this study aimed at providing a comprehensive review of artificial intelligence models that have been used to detect face masks. The study revealed that deep learning models such as the Inceptionv3 convolutional neural network achieved 99.9% accuracy in detecting COVID-19 face masks. We deducted that most of the datasets used to detect face masks are created artificially, do not represent the real-world environments which ultimately affect the precision accuracy of the model when deployed in the real world. Hence there is a need for sharing real-world COVID-19 face mask images for modelling deep learning techniques. The study also revealed that deeper and wider deep learning architectures with increased training parameters, such as inception-v4, Mask R-CNN, Faster R-CNN, YOLOv3, Xception, and DenseNet are not yet implemented to detect face masks. |
| first_indexed | 2024-04-11T04:51:38Z |
| format | Article |
| id | doaj.art-f241237c11fd443c9d8043b833664a82 |
| institution | Directory Open Access Journal |
| issn | 2666-4127 |
| language | English |
| last_indexed | 2024-04-11T04:51:38Z |
| publishDate | 2021-01-01 |
| publisher | KeAi Communications Co. Ltd. |
| record_format | Article |
| series | Sustainable Operations and Computers |
| spelling | doaj.art-f241237c11fd443c9d8043b833664a822022-12-27T04:37:36ZengKeAi Communications Co. Ltd.Sustainable Operations and Computers2666-41272021-01-012235245Application of deep learning and machine learning models to detect COVID-19 face masks - A reviewElliot Mbunge0Sakhile Simelane1Stephen G Fashoto2Boluwaji Akinnuwesi3Andile S Metfula4Department of Computer Science, Faculty of Science and Engineering, University of Eswatini, P Bag 4 Kwaluseni, Eswatini; Department of Information Technology, Faculty of Accounting and Informatics, Durban University of Technology, P O Box 1334, Durban 4000, South Africa; Corresponding author.Department of Computer Science, Faculty of Science and Engineering, University of Eswatini, P Bag 4 Kwaluseni, EswatiniDepartment of Computer Science, Faculty of Science and Engineering, University of Eswatini, P Bag 4 Kwaluseni, EswatiniDepartment of Computer Science, Faculty of Science and Engineering, University of Eswatini, P Bag 4 Kwaluseni, EswatiniDepartment of Computer Science, Faculty of Science and Engineering, University of Eswatini, P Bag 4 Kwaluseni, EswatiniThe continuous COVID-19 upsurge and emerging variants present unprecedented challenges in many health systems. Many regulatory authorities have instituted the mandatory use of face masks especially in public places where massive contact of people is frequent and inevitable, particularly inside public transport facilities, sports arenas, shopping malls and workplaces. However, compliance and adherence to proper wearing of face masks have been difficult due to various reasons including diversified mask types, different degrees of obstructions, various variations, balancing various model detection accuracy or errors and deployment requirements, angle of view, deployment of detection model on computers with limited processing power, low-resolution images, facial expression, and lack of real-world dataset. Therefore, this study aimed at providing a comprehensive review of artificial intelligence models that have been used to detect face masks. The study revealed that deep learning models such as the Inceptionv3 convolutional neural network achieved 99.9% accuracy in detecting COVID-19 face masks. We deducted that most of the datasets used to detect face masks are created artificially, do not represent the real-world environments which ultimately affect the precision accuracy of the model when deployed in the real world. Hence there is a need for sharing real-world COVID-19 face mask images for modelling deep learning techniques. The study also revealed that deeper and wider deep learning architectures with increased training parameters, such as inception-v4, Mask R-CNN, Faster R-CNN, YOLOv3, Xception, and DenseNet are not yet implemented to detect face masks.http://www.sciencedirect.com/science/article/pii/S2666412721000325COVID-19Face maskArtificial intelligenceDeep learning modelsMachine learning |
| spellingShingle | Elliot Mbunge Sakhile Simelane Stephen G Fashoto Boluwaji Akinnuwesi Andile S Metfula Application of deep learning and machine learning models to detect COVID-19 face masks - A review Sustainable Operations and Computers COVID-19 Face mask Artificial intelligence Deep learning models Machine learning |
| title | Application of deep learning and machine learning models to detect COVID-19 face masks - A review |
| title_full | Application of deep learning and machine learning models to detect COVID-19 face masks - A review |
| title_fullStr | Application of deep learning and machine learning models to detect COVID-19 face masks - A review |
| title_full_unstemmed | Application of deep learning and machine learning models to detect COVID-19 face masks - A review |
| title_short | Application of deep learning and machine learning models to detect COVID-19 face masks - A review |
| title_sort | application of deep learning and machine learning models to detect covid 19 face masks a review |
| topic | COVID-19 Face mask Artificial intelligence Deep learning models Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2666412721000325 |
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