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Real-time face mask detection using YOLOv4

Coronavirus disease (COVID-19) has been widespread in the world. Starting from December 2019, COVID-19 is a major public health and economic problem because the virus has impacted detrimentally the life quality of people. Therefore, the government has applied a new restriction to force people to wea...

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Main Author:	Tey, Chen Hup
Format:	Final Year Project / Dissertation / Thesis
Published:	2022
Subjects:	Q Science (General) T Technology (General)
Online Access:	http://eprints.utar.edu.my/4674/1/fyp_CS_2022_TCH.pdf http://eprints.utar.edu.my/4674/
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id	my-utar-eprints.4674
record_format	eprints
spelling	my-utar-eprints.46742022-10-13T07:24:58Z Real-time face mask detection using YOLOv4 Tey, Chen Hup Q Science (General) T Technology (General) Coronavirus disease (COVID-19) has been widespread in the world. Starting from December 2019, COVID-19 is a major public health and economic problem because the virus has impacted detrimentally the life quality of people. Therefore, the government has applied a new restriction to force people to wear a face mask to control the spread of coronavirus. However, it is very difficult to manually check if everyone in the public areas is wearing a mask correctly or not. Furthermore, normally the detection results will not be saved or recorded if manually monitoring the people whether they wear masks properly or not. Even though there are many existing face mask detection applications in the market, but most of them do not connect to the database to store the detection results. However, it is important to use those data to analyze the face mask detection result so that important information such as the face mask compliance rate for a specific date can be known. Therefore, this project, which is an automated and powerful real-time face mask detection application that used the YOLOv4 model, has been developed. In this application, the system will apply the multithreading approach to perform real-time detection on two cameras simultaneously. This can save time and human effort in monitoring whether people are wearing masks correctly, as it is inefficient to check whether people wear masks correctly or not manually using pure human effort, especially in public areas. This system will also alert the person by displaying an alarm sound when the person is not wearing a face mask, or the face mask is wearing in an incorrect manner. At the same time, the system will also automatically record the detection results as a video for the user to check or watch the detection results later. Besides that, this application also allows the user to record the detection results manually by taking a screenshot and saving the screenshot into the MySQL database and local drive. Moreover, this application is also connected to the MySQL database to store the detection results. This application will apply an object tracking algorithm when performing face mask detection in real-time to prevent the detections on the same object from being stored into the database repeatedly in every single frame. Furthermore, this application also allows the user to create a face mask detection dashboard for a specific date by using the data stored in the MySQL database for analysis purposes. There is also a function that allows the user to export the detection result from the MySQL database to a CSV file. 2022-04-22 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/4674/1/fyp_CS_2022_TCH.pdf Tey, Chen Hup (2022) Real-time face mask detection using YOLOv4. Final Year Project, UTAR. http://eprints.utar.edu.my/4674/
institution	Universiti Tunku Abdul Rahman
building	UTAR Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tunku Abdul Rahman
content_source	UTAR Institutional Repository
url_provider	http://eprints.utar.edu.my
topic	Q Science (General) T Technology (General)
spellingShingle	Q Science (General) T Technology (General) Tey, Chen Hup Real-time face mask detection using YOLOv4
description	Coronavirus disease (COVID-19) has been widespread in the world. Starting from December 2019, COVID-19 is a major public health and economic problem because the virus has impacted detrimentally the life quality of people. Therefore, the government has applied a new restriction to force people to wear a face mask to control the spread of coronavirus. However, it is very difficult to manually check if everyone in the public areas is wearing a mask correctly or not. Furthermore, normally the detection results will not be saved or recorded if manually monitoring the people whether they wear masks properly or not. Even though there are many existing face mask detection applications in the market, but most of them do not connect to the database to store the detection results. However, it is important to use those data to analyze the face mask detection result so that important information such as the face mask compliance rate for a specific date can be known. Therefore, this project, which is an automated and powerful real-time face mask detection application that used the YOLOv4 model, has been developed. In this application, the system will apply the multithreading approach to perform real-time detection on two cameras simultaneously. This can save time and human effort in monitoring whether people are wearing masks correctly, as it is inefficient to check whether people wear masks correctly or not manually using pure human effort, especially in public areas. This system will also alert the person by displaying an alarm sound when the person is not wearing a face mask, or the face mask is wearing in an incorrect manner. At the same time, the system will also automatically record the detection results as a video for the user to check or watch the detection results later. Besides that, this application also allows the user to record the detection results manually by taking a screenshot and saving the screenshot into the MySQL database and local drive. Moreover, this application is also connected to the MySQL database to store the detection results. This application will apply an object tracking algorithm when performing face mask detection in real-time to prevent the detections on the same object from being stored into the database repeatedly in every single frame. Furthermore, this application also allows the user to create a face mask detection dashboard for a specific date by using the data stored in the MySQL database for analysis purposes. There is also a function that allows the user to export the detection result from the MySQL database to a CSV file.
format	Final Year Project / Dissertation / Thesis
author	Tey, Chen Hup
author_facet	Tey, Chen Hup
author_sort	Tey, Chen Hup
title	Real-time face mask detection using YOLOv4
title_short	Real-time face mask detection using YOLOv4
title_full	Real-time face mask detection using YOLOv4
title_fullStr	Real-time face mask detection using YOLOv4
title_full_unstemmed	Real-time face mask detection using YOLOv4
title_sort	real-time face mask detection using yolov4
publishDate	2022
url	http://eprints.utar.edu.my/4674/1/fyp_CS_2022_TCH.pdf http://eprints.utar.edu.my/4674/
_version_	1748184794720632832
score	13.211869

Real-time face mask detection using YOLOv4

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