Face Mask Detection in Smart Cities Using Deep and Transfer Learning: Lessons Learned from the COVID-19 Pandemic
After different consecutive waves, the pandemic phase of Coronavirus disease 2019 does not look to be ending soon for most countries across the world. To slow the spread of the COVID-19 virus, several measures have been adopted since the start of the outbreak, including wearing face masks and mainta...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Systems |
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| Online Access: | https://www.mdpi.com/2079-8954/11/2/107 |
| _version_ | 1797617970589990912 |
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| author | Yassine Himeur Somaya Al-Maadeed Iraklis Varlamis Noor Al-Maadeed Khalid Abualsaud Amr Mohamed |
| author_facet | Yassine Himeur Somaya Al-Maadeed Iraklis Varlamis Noor Al-Maadeed Khalid Abualsaud Amr Mohamed |
| author_sort | Yassine Himeur |
| collection | DOAJ |
| description | After different consecutive waves, the pandemic phase of Coronavirus disease 2019 does not look to be ending soon for most countries across the world. To slow the spread of the COVID-19 virus, several measures have been adopted since the start of the outbreak, including wearing face masks and maintaining social distancing. Ensuring safety in public areas of smart cities requires modern technologies, such as deep learning and deep transfer learning, and computer vision for automatic face mask detection and accurate control of whether people wear masks correctly. This paper reviews the progress in face mask detection research, emphasizing deep learning and deep transfer learning techniques. Existing face mask detection datasets are first described and discussed before presenting recent advances to all the related processing stages using a well-defined taxonomy, the nature of object detectors and Convolutional Neural Network architectures employed and their complexity, and the different deep learning techniques that have been applied so far. Moving on, benchmarking results are summarized, and discussions regarding the limitations of datasets and methodologies are provided. Last but not least, future research directions are discussed in detail. |
| first_indexed | 2024-03-11T08:03:32Z |
| format | Article |
| id | doaj.art-1b969e7bdb914888848d3980c5a14ed1 |
| institution | Directory Open Access Journal |
| issn | 2079-8954 |
| language | English |
| last_indexed | 2024-03-11T08:03:32Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Systems |
| spelling | doaj.art-1b969e7bdb914888848d3980c5a14ed12023-11-16T23:35:48ZengMDPI AGSystems2079-89542023-02-0111210710.3390/systems11020107Face Mask Detection in Smart Cities Using Deep and Transfer Learning: Lessons Learned from the COVID-19 PandemicYassine Himeur0Somaya Al-Maadeed1Iraklis Varlamis2Noor Al-Maadeed3Khalid Abualsaud4Amr Mohamed5Department of Computer Science and Engineering, Qatar University, Doha P.O. Box 2713, QatarDepartment of Computer Science and Engineering, Qatar University, Doha P.O. Box 2713, QatarDepartment of Informatics and Telematics, Harokopio University of Athens, Omirou 9, Tavros, 17778 Athens, GreeceDepartment of Computer Science and Engineering, Qatar University, Doha P.O. Box 2713, QatarDepartment of Computer Science and Engineering, Qatar University, Doha P.O. Box 2713, QatarDepartment of Computer Science and Engineering, Qatar University, Doha P.O. Box 2713, QatarAfter different consecutive waves, the pandemic phase of Coronavirus disease 2019 does not look to be ending soon for most countries across the world. To slow the spread of the COVID-19 virus, several measures have been adopted since the start of the outbreak, including wearing face masks and maintaining social distancing. Ensuring safety in public areas of smart cities requires modern technologies, such as deep learning and deep transfer learning, and computer vision for automatic face mask detection and accurate control of whether people wear masks correctly. This paper reviews the progress in face mask detection research, emphasizing deep learning and deep transfer learning techniques. Existing face mask detection datasets are first described and discussed before presenting recent advances to all the related processing stages using a well-defined taxonomy, the nature of object detectors and Convolutional Neural Network architectures employed and their complexity, and the different deep learning techniques that have been applied so far. Moving on, benchmarking results are summarized, and discussions regarding the limitations of datasets and methodologies are provided. Last but not least, future research directions are discussed in detail.https://www.mdpi.com/2079-8954/11/2/107face mask detectiondeep learningdeep transfer learningdeep domain adaptationYOLOMobileNet |
| spellingShingle | Yassine Himeur Somaya Al-Maadeed Iraklis Varlamis Noor Al-Maadeed Khalid Abualsaud Amr Mohamed Face Mask Detection in Smart Cities Using Deep and Transfer Learning: Lessons Learned from the COVID-19 Pandemic Systems face mask detection deep learning deep transfer learning deep domain adaptation YOLO MobileNet |
| title | Face Mask Detection in Smart Cities Using Deep and Transfer Learning: Lessons Learned from the COVID-19 Pandemic |
| title_full | Face Mask Detection in Smart Cities Using Deep and Transfer Learning: Lessons Learned from the COVID-19 Pandemic |
| title_fullStr | Face Mask Detection in Smart Cities Using Deep and Transfer Learning: Lessons Learned from the COVID-19 Pandemic |
| title_full_unstemmed | Face Mask Detection in Smart Cities Using Deep and Transfer Learning: Lessons Learned from the COVID-19 Pandemic |
| title_short | Face Mask Detection in Smart Cities Using Deep and Transfer Learning: Lessons Learned from the COVID-19 Pandemic |
| title_sort | face mask detection in smart cities using deep and transfer learning lessons learned from the covid 19 pandemic |
| topic | face mask detection deep learning deep transfer learning deep domain adaptation YOLO MobileNet |
| url | https://www.mdpi.com/2079-8954/11/2/107 |
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