Multiple face mask wearer detection based on YOLOv3 approach
IAES International Journal of Artificial Intelligence
; 12(1):384-393, 2023.
Article
in English
| ProQuest Central | ID: covidwho-2228855
ABSTRACT
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.
Computers--Artificial Intelligence; DarkNet-53; Face mask detection; Object detection; ResNet-50; You only look once version 3; Algorithms; Viral diseases; Automation; Object recognition; Machine learning; Wear; Severe acute respiratory syndrome coronavirus 2; Coronaviruses; Image filters; Infectious diseases; Internet resources; Disease transmission; COVID-19
Full text:
Available
Collection:
Databases of international organizations
Database:
ProQuest Central
Type of study:
Experimental Studies
Language:
English
Journal:
IAES International Journal of Artificial Intelligence
Year:
2023
Document Type:
Article
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