Mask Detection System with Computer Vision-Based on CNN and YOLO Method Using Nvidia Jetson Nano

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Ade Anggian Hakim
Elmo Juanara
Rispandi Rispandi


Health is an essential aspect of life. The World Health Organization (WHO) has officially declared the Corona Virus (Covid-19) a global pandemic that has spread to Indonesia. For preventive measures against Covid-19, the Indonesian government is trying to deal with the Covid-19 pandemic with 3M health protocol aimed at community activities, such as Memakai Masker (wearing masks), Mencuci Tangan (washing hands), and Menjaga Jarak (maintaining distance). In this study, software and hardware design was carried out to detect mask users and immediately warn violators who do not use masks automatically and can function automatically offline by utilizing digital image processing using NVIDIA Jetson Nano using the YOLO (You Only Look Once) method. The CNN YOLOv4-tiny model is chosen to obtain measurement results for mask user detection accuracy because it has a relatively minor computational value and is faster. The best camera detection angle is obtained at a vulnerable angle of 45O-90O or in the range of 90O-135O with value confidence that the average is 99.94% and the best accuracy is at a lux value greater than 70, and a minimum camera height of 1 meter and a maximum of 3 meters. Under conditions of lux 96 (bright), the maximum distance for detecting a face object is 12 meters, and the ability of the system to output a warning sound has been successfully integrated with a relay to run the mp3 module separately from the system, so as not to interfere with the Jetson Nano computation process and the model is successfully run on the Jetson Nano with an average computation of 13 frames per second.

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Hakim, A. A., Juanara, E., & Rispandi, R. (2023). Mask Detection System with Computer Vision-Based on CNN and YOLO Method Using Nvidia Jetson Nano. Journal of Information System Exploration and Research, 1(2).


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