Yue Xuebin, Li Hengyi, Meng Lin
Department of Electronic and Computer Engineering, Ritsumeikan University, 1-1-1 Nojihigashi, Kusatsu, Shiga, Japan.
Procedia Comput Sci. 2022;202:152-157. doi: 10.1016/j.procs.2022.04.021. Epub 2022 May 10.
Since the prevalence of COVID-19, the virus has spread all over the world. A large number of people have been infected and died, and countries all over the world have experienced the most severe crisis. Vaccination can effectively resist the virus. However, it does not mean that vaccination can suppress virus spread completely. Hence, wearing a mask correctly and keeping the social distance become emergency methods for reducing the risk of infection. This paper proposes an AI-based prevention embedded system against COVID-19 in daily life by keeping the function of the emergency method. The system consists of two functions, mask-wearing-status detection, and social-distance measurement. Mask-wearing-status detection employs YOLO and realizes the detection and classification of three mask-wearing-status, corrected-wearing, non-corrected-wearing, and without-wearing. Social-distance measurement equips a depth camera for measuring the distance between humans. The system gives an alert when people do not wear a mask correctly or do not keep their social distance. The system has been implemented on Jetson-nano, a compact embedded board, and achieves 6 The experimental results also show that the mask-wearing-status detection accuracy archives at 93.21% and the error of social-distance measurement are within 3 which have proved the effectiveness of the system.
自新冠疫情流行以来，该病毒已蔓延至全球。大量人员被感染并死亡，世界各国都经历了最严峻的危机。接种疫苗可以有效抵抗病毒。然而，这并不意味着接种疫苗就能完全抑制病毒传播。因此，正确佩戴口罩和保持社交距离成为降低感染风险的应急方法。本文通过保留应急方法的功能，提出了一种基于人工智能的日常生活中预防新冠病毒的嵌入式系统。该系统由两个功能组成，即口罩佩戴状态检测和社交距离测量。口罩佩戴状态检测采用YOLO实现对三种口罩佩戴状态（正确佩戴、未正确佩戴、未佩戴）的检测和分类。社交距离测量配备深度相机用于测量人与人之间的距离。当人们未正确佩戴口罩或未保持社交距离时，系统会发出警报。该系统已在紧凑型嵌入式开发板Jetson-nano上实现，并达到了6……实验结果还表明，口罩佩戴状态检测准确率达到93.21%，社交距离测量误差在3……以内，证明了该系统的有效性。
