Deep learning-based occlusion-aware face mask detection for airborne disease control
Abstract Airborne infectious diseases are a significant threat to human beings. Nowadays, one of the deadliest airborne diseases, coronavirus (COVID-19), is resulting in a massive health crisis due to its rapid transmission. The World Health Organization for protection against the spread of airborne...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-07-01
|
| Series: | Discover Computing |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s10791-025-09684-1 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849342038810558464 |
|---|---|
| author | Teshome Ayechiluhem Yalew Sosina M. Gashaw Aleka Melese Ayalew Mourad Oussalah |
| author_facet | Teshome Ayechiluhem Yalew Sosina M. Gashaw Aleka Melese Ayalew Mourad Oussalah |
| author_sort | Teshome Ayechiluhem Yalew |
| collection | DOAJ |
| description | Abstract Airborne infectious diseases are a significant threat to human beings. Nowadays, one of the deadliest airborne diseases, coronavirus (COVID-19), is resulting in a massive health crisis due to its rapid transmission. The World Health Organization for protection against the spread of airborne diseases has set several guidelines. The most effective preventive measure against airborne diseases, according to the World Health Organization, is wearing masks in public places and crowded areas. It is challenging to monitor people manually in these areas. In this study, we collect data from public and local sources to develop an occlusion-aware face mask detection model. This study presents a deep learning-based occlusion-aware face mask detection model designed to identify both proper and improper mask usage, even under partial facial occlusions. A dataset of 4,820 images, including occlusions from hands, objects, and mask misuse, was used to train and evaluate three convolutional neural network models: InceptionV3, MobileNetV2, and DenseNet121. Among them, DenseNet121 achieved the highest accuracy of 96.3% on test data. Therefore, our proposed study is used to investigate occlusion aware face mask classification using deep learning. |
| format | Article |
| id | doaj-art-1f14f675cd4d48fc98e1c391db4386e7 |
| institution | Kabale University |
| issn | 2948-2992 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Computing |
| spelling | doaj-art-1f14f675cd4d48fc98e1c391db4386e72025-08-20T03:43:30ZengSpringerDiscover Computing2948-29922025-07-0128112510.1007/s10791-025-09684-1Deep learning-based occlusion-aware face mask detection for airborne disease controlTeshome Ayechiluhem Yalew0Sosina M. Gashaw1Aleka Melese Ayalew2Mourad Oussalah3Department of Information Systems, University of GondarDepartment of Electrical and Computer Engineering, Addis Ababa Institute of TechnologyCenter for Machine Vision and Signal Analysis (CMVS), University of OuluCenter for Machine Vision and Signal Analysis (CMVS), University of OuluAbstract Airborne infectious diseases are a significant threat to human beings. Nowadays, one of the deadliest airborne diseases, coronavirus (COVID-19), is resulting in a massive health crisis due to its rapid transmission. The World Health Organization for protection against the spread of airborne diseases has set several guidelines. The most effective preventive measure against airborne diseases, according to the World Health Organization, is wearing masks in public places and crowded areas. It is challenging to monitor people manually in these areas. In this study, we collect data from public and local sources to develop an occlusion-aware face mask detection model. This study presents a deep learning-based occlusion-aware face mask detection model designed to identify both proper and improper mask usage, even under partial facial occlusions. A dataset of 4,820 images, including occlusions from hands, objects, and mask misuse, was used to train and evaluate three convolutional neural network models: InceptionV3, MobileNetV2, and DenseNet121. Among them, DenseNet121 achieved the highest accuracy of 96.3% on test data. Therefore, our proposed study is used to investigate occlusion aware face mask classification using deep learning.https://doi.org/10.1007/s10791-025-09684-1COVID-19OcclusionFace maskInceptionV3MobileNetV2Airborne infectious |
| spellingShingle | Teshome Ayechiluhem Yalew Sosina M. Gashaw Aleka Melese Ayalew Mourad Oussalah Deep learning-based occlusion-aware face mask detection for airborne disease control Discover Computing COVID-19 Occlusion Face mask InceptionV3 MobileNetV2 Airborne infectious |
| title | Deep learning-based occlusion-aware face mask detection for airborne disease control |
| title_full | Deep learning-based occlusion-aware face mask detection for airborne disease control |
| title_fullStr | Deep learning-based occlusion-aware face mask detection for airborne disease control |
| title_full_unstemmed | Deep learning-based occlusion-aware face mask detection for airborne disease control |
| title_short | Deep learning-based occlusion-aware face mask detection for airborne disease control |
| title_sort | deep learning based occlusion aware face mask detection for airborne disease control |
| topic | COVID-19 Occlusion Face mask InceptionV3 MobileNetV2 Airborne infectious |
| url | https://doi.org/10.1007/s10791-025-09684-1 |
| work_keys_str_mv | AT teshomeayechiluhemyalew deeplearningbasedocclusionawarefacemaskdetectionforairbornediseasecontrol AT sosinamgashaw deeplearningbasedocclusionawarefacemaskdetectionforairbornediseasecontrol AT alekameleseayalew deeplearningbasedocclusionawarefacemaskdetectionforairbornediseasecontrol AT mouradoussalah deeplearningbasedocclusionawarefacemaskdetectionforairbornediseasecontrol |