Citation

Fahd Al-Selwi, Hatem and Hassan, Nawaid and Ab. Ghani, Hadhrami and Amir Hamzah, Nur Asyiqin and Abd. Aziz, Azlan (2023) Face mask detection and counting using you only look once algorithm with Jetson Nano and NVDIA giga texel shader extreme. IAES International Journal of Artificial Intelligence (IJ-AI), 12 (3). p. 1169. ISSN 2089-4872

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Abstract

Deep learning and machine learning are becoming more extensively adopted artificial intelligence techniques for machine vision problems in everyday life, giving rise to new capabilities in every sector of technology. It has a wide range of applications, ranging from autonomous driving to medical and health monitoring. For image detection, the best reported approach is the you only look once (YOLO) algorithm, which is the faster and more accurate version of the convolutional neural network (CNN) algorithm. In the healthcare domain, YOLO can be applied for checking the face mask wearing of the people, especially in a public area or before entering any closed space such as a building to avoid the spread of the air-borne disease such as COVID-19. The main challenges are the image datasets, which are unstructured and may grow large, affecting the accuracy and speed of the detection. Secondly is the portability of the detection devices, which are generally dependent on the more portable like NVDIA Jetson Nano or from the existing computer/laptop. Using the low-power NVDIA Jetson Nano system as well as NVDIA giga texel shader extreme (GTX), this paper aims to design and implement real-time face mask wearing detection using the pre-trained dataset as well as the real-time data.

Item Type:	Article
Uncontrolled Keywords:	Deep learning; Internet of things; Jetson Nano; Nvidia; You only look once
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75-76.95 Calculating machines
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	02 May 2023 04:08
Last Modified:	02 May 2023 04:08
URII:	http://shdl.mmu.edu.my/id/eprint/11381

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