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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Vydané v:IAES International Journal of Artificial Intelligence Ročník 12; číslo 1; s. 384
Hlavní autori: Ge, Cheng Xiao, As’ari, Muhammad Amir, Jasmin Sufri, Nur Anis
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Yogyakarta IAES Institute of Advanced Engineering and Science 01.03.2023
Predmet:
Algorithms
Automation
Coronaviruses
COVID-19
Disease transmission
Image filters
Infectious diseases
Internet resources
Machine learning
Object recognition
Severe acute respiratory syndrome coronavirus 2
Viral diseases
Wear
ISSN:2089-4872, 2252-8938, 2089-4872
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Shrnutí: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. 
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:2089-4872
2252-8938
2089-4872
DOI:10.11591/ijai.v12.i1.pp384-393