Artificial Intelligence and Deep Learning based Face Mask Detection System using Generic Camera

2022 IEEE Pune Section International Conference (PuneCon) Pub Date : 2022-12-15 DOI:10.1109/PuneCon55413.2022.10014873

Chetan H. Patil, H. Patil, S. Mali

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Abstract

In the past two years 2020 and 2021, the COVID-19 outburst has had a serious effect on human life. The effects and side effects of COVID-19 are being stroked in almost every discipline relevant to survival and development. The healthcare system was in a problematic situation during this tough time in pandemic situation all over the world. One of the many precautions and protections used to break the chain of spreading of this virus is wearing a mask and keeping safe distance. In a network of smart cities where entirely public spaces are monitored by Closed-Circuit Television (CCTV) cameras, we are offering a strategy in this study that restricts the spread of COVID-19 by identifying people not wearing mask. The network alerts the proper authority whenever a person without a mask is discovered. It is believed that our research will help many countries throughout the world stop the spread of this infectious disease. We've examined this on 200 real people in the present, with a 100% success rate. It is also observed that when more than one person in front of CCTV success rate reduced exponentially

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基于人工智能和深度学习的人脸检测系统

在2020年和2021年这两年，新冠肺炎疫情对人类生活造成了严重影响。COVID-19的影响和副作用正在几乎所有与生存和发展有关的学科中受到关注。在全球大流行的艰难时期，医疗保健系统陷入了困境。打破这种病毒传播链的许多预防和保护措施之一是戴口罩并保持安全距离。在一个完全由闭路电视(CCTV)摄像机监控的公共空间的智能城市网络中，我们在本研究中提出了一种通过识别未戴口罩的人来限制COVID-19传播的策略。一旦发现没有面具的人，网络就会向有关当局发出警报。相信我们的研究将帮助世界上许多国家阻止这种传染病的传播。目前，我们已经对200个人进行了测试，成功率为100%。我们还观察到，当一个以上的人在CCTV前成功率呈指数下降

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2022 IEEE Pune Section International Conference (PuneCon)

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