Multiple face mask wearer detection based on YOLOv3 approach
The coronavirus disease 2019 (COVID-19) is a highly infectious disease caused by the SARS-CoV-2 coronavirus. In breaking the transmission chain of SARS-CoV-2, the government has made it compulsory for the people to wear a mask in public places to prevent COVID-19 transmission. Hence, an automated fa...
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Institute of Advanced Engineering and Science
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Online Access: | http://eprints.utm.my/107580/1/MuhammadAmirAsAri2023_MultipleFaceMaskWearerDetection.pdf http://eprints.utm.my/107580/ http://dx.doi.org/10.11591/ijai.v12.i1.pp384-393 |
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my.utm.1075802024-09-25T06:19:08Z http://eprints.utm.my/107580/ Multiple face mask wearer detection based on YOLOv3 approach Cheng, Xiao Ge As’ari, Muhammad Amir Sufri, Nur Anis Jasmin TK Electrical engineering. Electronics Nuclear engineering The coronavirus disease 2019 (COVID-19) is a highly infectious disease caused by the SARS-CoV-2 coronavirus. In breaking the transmission chain of SARS-CoV-2, the government has made it compulsory for the people to wear a mask in public places to prevent COVID-19 transmission. Hence, an automated face mask detection is crucial to facilitate the monitoring process in ensuring people to wear a face mask in public. This project aims to develop an automated face and face mask detection for multiple people by applying deep learning-based object detection algorithm you only look once version 3 (YOLOv3). YOLOv3 object detection algorithm was concatenated with different backbones including ResNet-50 and Darknet-53 to develop the face and face mask detection model. Datasets were collected from online resources including Kaggle and Github and the images were filtered and labelled accordingly. The models were trained on 4393 images and evaluated based on precision, recall, mean average precision and the detection time. In conclusion, DarkNet53_YOLOv3 was chosen as the better model compared to ResNet50_YOLOv3 model with its good performance on accuracy with a mAP of 95.94% and a fast detection speed with a detection time of 50 seconds on 776 images. Institute of Advanced Engineering and Science 2023 Article PeerReviewed application/pdf en http://eprints.utm.my/107580/1/MuhammadAmirAsAri2023_MultipleFaceMaskWearerDetection.pdf Cheng, Xiao Ge and As’ari, Muhammad Amir and Sufri, Nur Anis Jasmin (2023) Multiple face mask wearer detection based on YOLOv3 approach. IAES International Journal of Artificial Intelligence, 12 (1). pp. 384-393. ISSN 2089-4872 http://dx.doi.org/10.11591/ijai.v12.i1.pp384-393 DOI : 10.11591/ijai.v12.i1.pp384-393 |
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TK Electrical engineering. Electronics Nuclear engineering Cheng, Xiao Ge As’ari, Muhammad Amir Sufri, Nur Anis Jasmin Multiple face mask wearer detection based on YOLOv3 approach |
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The coronavirus disease 2019 (COVID-19) is a highly infectious disease caused by the SARS-CoV-2 coronavirus. In breaking the transmission chain of SARS-CoV-2, the government has made it compulsory for the people to wear a mask in public places to prevent COVID-19 transmission. Hence, an automated face mask detection is crucial to facilitate the monitoring process in ensuring people to wear a face mask in public. This project aims to develop an automated face and face mask detection for multiple people by applying deep learning-based object detection algorithm you only look once version 3 (YOLOv3). YOLOv3 object detection algorithm was concatenated with different backbones including ResNet-50 and Darknet-53 to develop the face and face mask detection model. Datasets were collected from online resources including Kaggle and Github and the images were filtered and labelled accordingly. The models were trained on 4393 images and evaluated based on precision, recall, mean average precision and the detection time. In conclusion, DarkNet53_YOLOv3 was chosen as the better model compared to ResNet50_YOLOv3 model with its good performance on accuracy with a mAP of 95.94% and a fast detection speed with a detection time of 50 seconds on 776 images. |
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Article |
author |
Cheng, Xiao Ge As’ari, Muhammad Amir Sufri, Nur Anis Jasmin |
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Cheng, Xiao Ge As’ari, Muhammad Amir Sufri, Nur Anis Jasmin |
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Cheng, Xiao Ge |
title |
Multiple face mask wearer detection based on YOLOv3 approach |
title_short |
Multiple face mask wearer detection based on YOLOv3 approach |
title_full |
Multiple face mask wearer detection based on YOLOv3 approach |
title_fullStr |
Multiple face mask wearer detection based on YOLOv3 approach |
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Multiple face mask wearer detection based on YOLOv3 approach |
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multiple face mask wearer detection based on yolov3 approach |
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Institute of Advanced Engineering and Science |
publishDate |
2023 |
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http://eprints.utm.my/107580/1/MuhammadAmirAsAri2023_MultipleFaceMaskWearerDetection.pdf http://eprints.utm.my/107580/ http://dx.doi.org/10.11591/ijai.v12.i1.pp384-393 |
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