Development of Health Mask Identification Using YOLOv5 Architecture

(1) * Ahmad Fauzi Mail (Universitas Buana Perjuangan Karawang, Indonesia)
(2) Prasetyo Ajie Mail (Universitas Buana Perjuangan Karawang, Indonesia)
(3) Anis Fitri Nur Masruriyah Mail (Universitas Buana Perjuangan Karawang, Indonesia)
(4) Deden Wahiddin Mail (Universitas Buana Perjuangan Karawang, Indonesia)
(5) Hanny Hikmayanti Mail (Universitas Buana Perjuangan Karawang, Indonesia)
(6) April Lia Hananto Mail (Universitas Buana Perjuangan Karawang, Indonesia)
*corresponding author


Coronavirus Disease 2019 (COVID-19) causes the state to suffer losses, especially in the health sector. WHO calls for controlling COVID-19 with health protocols that must be obeyed, one of which is wearing a mask. The use of masks can reduce the transmission of COVID-19. But there are still many people who ignore the protocol to use masks properly. So a system was created to detect the use of masks properly using the YOLOv5 architecture. Aiming to help regulate the use of masks in public areas or open places. The process of this research begins with data collection in the form of images. The collected image data will later be used as a dataset and model training will be carried out using the YOLOv5s model. The accuracy results obtained from this study reached 90.37%


Computer Vision COVID-19 Deep Learning Identification Image Processing



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