Face mask detection using YOLOv3 and faster R-CNN models: COVID-19 environment
There are many solutions to prevent the spread of the COVID-19 virus and one of the most effective solutions is wearing a face mask. Almost everyone is wearing face masks at all times in public places during the coronavirus pandemic. This encourages us to explore face mask detection technology to mo...
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| Published in | Multimedia tools and applications Vol. 80; no. 13; pp. 19753 - 19768 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.05.2021
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
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| Abstract | There are many solutions to prevent the spread of the COVID-19 virus and one of the most effective solutions is wearing a face mask. Almost everyone is wearing face masks at all times in public places during the coronavirus pandemic. This encourages us to explore face mask detection technology to monitor people wearing masks in public places. Most recent and advanced face mask detection approaches are designed using deep learning. In this article, two state-of-the-art object detection models, namely, YOLOv3 and faster R-CNN are used to achieve this task. The authors have trained both the models on a dataset that consists of images of people of two categories that are with and without face masks. This work proposes a technique that will draw bounding boxes (red or green) around the faces of people, based on whether a person is wearing a mask or not, and keeps the record of the ratio of people wearing face masks on the daily basis. The authors have also compared the performance of both the models i.e., their precision rate and inference time. |
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| AbstractList | There are many solutions to prevent the spread of the COVID-19 virus and one of the most effective solutions is wearing a face mask. Almost everyone is wearing face masks at all times in public places during the coronavirus pandemic. This encourages us to explore face mask detection technology to monitor people wearing masks in public places. Most recent and advanced face mask detection approaches are designed using deep learning. In this article, two state-of-the-art object detection models, namely, YOLOv3 and faster R-CNN are used to achieve this task. The authors have trained both the models on a dataset that consists of images of people of two categories that are with and without face masks. This work proposes a technique that will draw bounding boxes (red or green) around the faces of people, based on whether a person is wearing a mask or not, and keeps the record of the ratio of people wearing face masks on the daily basis. The authors have also compared the performance of both the models i.e., their precision rate and inference time. There are many solutions to prevent the spread of the COVID-19 virus and one of the most effective solutions is wearing a face mask. Almost everyone is wearing face masks at all times in public places during the coronavirus pandemic. This encourages us to explore face mask detection technology to monitor people wearing masks in public places. Most recent and advanced face mask detection approaches are designed using deep learning. In this article, two state-of-the-art object detection models, namely, YOLOv3 and faster R-CNN are used to achieve this task. The authors have trained both the models on a dataset that consists of images of people of two categories that are with and without face masks. This work proposes a technique that will draw bounding boxes (red or green) around the faces of people, based on whether a person is wearing a mask or not, and keeps the record of the ratio of people wearing face masks on the daily basis. The authors have also compared the performance of both the models i.e., their precision rate and inference time.There are many solutions to prevent the spread of the COVID-19 virus and one of the most effective solutions is wearing a face mask. Almost everyone is wearing face masks at all times in public places during the coronavirus pandemic. This encourages us to explore face mask detection technology to monitor people wearing masks in public places. Most recent and advanced face mask detection approaches are designed using deep learning. In this article, two state-of-the-art object detection models, namely, YOLOv3 and faster R-CNN are used to achieve this task. The authors have trained both the models on a dataset that consists of images of people of two categories that are with and without face masks. This work proposes a technique that will draw bounding boxes (red or green) around the faces of people, based on whether a person is wearing a mask or not, and keeps the record of the ratio of people wearing face masks on the daily basis. The authors have also compared the performance of both the models i.e., their precision rate and inference time. |
| Author | Kumar, Krishan Ahuja, Umang Sachdeva, Monika Kumar, Munish Singh, Sunil |
| Author_xml | – sequence: 1 givenname: Sunil surname: Singh fullname: Singh, Sunil organization: Department of Information Technology, University Institute of Engineering and Technology, Panjab University – sequence: 2 givenname: Umang surname: Ahuja fullname: Ahuja, Umang organization: Department of Information Technology, University Institute of Engineering and Technology, Panjab University – sequence: 3 givenname: Munish orcidid: 0000-0003-0115-1620 surname: Kumar fullname: Kumar, Munish email: munishcse@gmail.com organization: Department of Computational Sciences, Maharaja Ranjit Singh Punjab Technical University – sequence: 4 givenname: Krishan surname: Kumar fullname: Kumar, Krishan organization: Department of Information Technology, University Institute of Engineering and Technology, Panjab University – sequence: 5 givenname: Monika surname: Sachdeva fullname: Sachdeva, Monika organization: Department of Computer Science and Engineering, I. K. G. Punjab Technical University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33679209$$D View this record in MEDLINE/PubMed |
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| Keywords | COVID-19 Deep learning YOLO v3 Face mask detection Faster R-CNN |
| Language | English |
| License | The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
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| References_xml | – reference: Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L (2009) Imagenet: A large-scale hierarchical image database. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp 248–255 – reference: He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp 770–778 – reference: Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp 1–9 – reference: W H Organization (2020) WH corona-viruses (COVID-19),” https://www.who.int/emergencies/diseases/novel-corona-virus-2019 – reference: Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861, 2017 – reference: SunLZhaoCYanZLiuPDuckettTStolkinRA novel weakly-supervised approach for RGB-D-based nuclear waste object detectionIEEE Sensors J2019193487350010.1109/JSEN.2018.2888815 – reference: ShaoqingRKaimingHGirshickRJianSFaster R-CNN: towards real-time object detection with region proposal networksIEEE Trans Pattern Anal Mach Intell2015391137114910.1109/TPAMI.2016.2577031 – reference: Jason B A Gentle Introduction to Transfer Learning for Deep Learning. https://machinelearningmastery.com/transfer-learning-for-deep-learning/ – reference: Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection, vol 2016. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, pp 779–788. https://doi.org/10.1109/CVPR.2016.91 – reference: MatuschekCMollFFangerauHFischerJCZänkerKvan GriensvenMSchneiderMKindgen-MillesDKnoefelWTLichtenbergATamaskovicsBDjiepmo-NjanangFJBudachWCorradiniSHäussingerDFeldtTJensenBPelkaROrthKPeiperMGrebeOMaasKGerberPAPedotoABölkeEHaussmannJFace masks: benefits and risks during the COVID-19 crisisEur J Med Res2020253210.1186/s40001-020-00430-5(2020) – ident: 10711_CR1 doi: 10.1109/CVPR.2009.5206848 – volume: 25 start-page: 32 year: 2020 ident: 10711_CR5 publication-title: Eur J Med Res doi: 10.1186/s40001-020-00430-5 – ident: 10711_CR6 doi: 10.1109/CVPR.2016.91 – volume: 39 start-page: 1137 year: 2015 ident: 10711_CR7 publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2016.2577031 – volume: 19 start-page: 3487 year: 2019 ident: 10711_CR8 publication-title: IEEE Sensors J doi: 10.1109/JSEN.2018.2888815 – ident: 10711_CR9 doi: 10.1109/CVPR.2015.7298594 – ident: 10711_CR4 – ident: 10711_CR3 – ident: 10711_CR10 – ident: 10711_CR2 doi: 10.1109/CVPR.2016.90 |
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| SubjectTerms | Computer Communication Networks Computer Science Coronaviruses COVID-19 Data Structures and Information Theory Disease control Environment models Masks Multimedia Information Systems Object recognition Special Purpose and Application-Based Systems Viral diseases |
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| Title | Face mask detection using YOLOv3 and faster R-CNN models: COVID-19 environment |
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