An Automated Face-mask Detection System using YOLOv5 for Preventing Spread of COVID-19

Autor:	Md Asifuzzaman Jishan, Md Shahabub Alam, Imran Rashid Mazumder, Khan Raqib Mahmud, Abul Kalam Al Azad
Rok vydání:	2023
Předmět:	Computer Networks and Communications Hardware and Architecture Computer Science (miscellaneous) Software Information Systems
Zdroj:	International Journal of Computing. :58-68
ISSN:	2312-5381 1727-6209
DOI:	10.47839/ijc.22.1.2880
Popis:	Object detection systems based on deep learning have been immensely successful incomplex object detection tasks images and have shown potential in a wide range of real-life applicationsincluding the COVID-19 pandemic. One of the key challenges in containing and mitigating the infectionamong the population is to ensure and enforce the proper use of face masks. The objective of this paperis to detect the proper use of facial masks among the urban population in a megacity. In this study, wetrained and validated a new dataset to detect images such as ‘with mask’, ‘without mask’, and ‘masknot in position’ using YOLOv5. The dataset is comprised of 6550 images with the three classes. Thedataset attained a commendable performance accuracy of 95% on mAP. This study can be implementedfor automated scanning for monitoring the proper use of face masks in different settings of public spaces.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::1f7121a53ad259084cf27fb9f9136b5b https://doi.org/10.47839/ijc.22.1.2880 Zobrazit plný text záznamu