基于DLN口罩检测和AR距离的COVID-19社会意识与安全援助

A. Tenriawaru, A. Basori, A. B. F. Mansur, Qusai Al-Qurashi, Abdullah Al-Muhaimeed, Majid Al-Hazmi
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引用次数: 0

摘要

新型冠状病毒感染症(COVID-19)的爆发,迫使主要国家对社会采取严格的政策。人们必须戴上口罩,保持距离,避免病毒感染。政府雇用官员监视公民,并警告他们如果没有戴口罩。这条警告信息还通过短信和社交媒体传播,以确保人们的安全意识。本文旨在利用深度学习网络(DLN)和预警系统,通过从CCTV或图像输入视频流,然后进行分析,提供面罩检测。如果发现有人没有戴口罩,他们会通过扬声器提醒他们,并提醒他们罚款。AR距离非常有用,可以根据特定区域内被检测到的人员给出违规者的位置。该系统设计为无需人工干预的智能自动工作。该系统的识别率达到99%,有望帮助政府提高人们对自己和周围人的安全意识。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Social Awareness and Safety Assistance of COVID-19 based on DLN face mask detection and AR Distancing
The outbreak of coronavirus disease (COVID-19) has forced major countries to apply strict policy toward society. People must wear a facemask and always keep their distance from each other's to avoid virus contamination. Government employ officers to monitor citizen and warn them if not wearing a face mask. The warning message also spread through SMS and social media to ensure people about safety and awareness. This paper aims to provide face mask detection using the Deep Learning Network(DLN) and warning system through video stream input from CCTV or images then analyzed. If people not wearing a mask are detected, they will alert them through the speaker and remind them about a penalty. AR distancing very useful to give position toward violator location based on the detected person in a certain area. The system is designed to work intelligently and automatically without human intervention. With the accuracy of 99% recognition, it's expected that the system can help the government to increase people awareness toward the safety of themselves and people around them.
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