基于计算机视觉的掩码检测

Yogiraj Bhale, Nikhil Agrawal, Sachin Kelwa
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引用次数: 0

摘要

Covid-19,由一种高贵的冠状病毒引起的大流行,在过去的两年里一直困扰着世界,发展的各个方面都受到了Covid-19的影响,医疗保健系统出现了几个问题,同时,已经采取了许多方法来减轻这种情况的精神,其中之一是在脸上使用口罩。通过识别未戴口罩或未适当佩戴口罩的人,我们提供了一种减少covid-19发展的策略。智能城市和公共场所使用闭路电视(CCTV)摄像头。当发现未戴口罩的人时,城市网络会通知当局。深度学习设计基于一组数据,该数据集比较了来自各种来源的戴口罩和不戴口罩的人的照片。准备好的设计辅助。戴口罩和不戴口罩的人的腹泻率为3%,预计我们的研究将被证明是保护我们的人民免受这些疾病的有益工具
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Mask Detection using Computer Vision
Covid-19, a pandemic caused by a noble coronavirus, has afflicted the world for the past two years Every aspect of development has been impacted by Covid-19 There were several problems with the healthcare system In the meanwhile, numerous methods have been made to lessen the spirit of this condition, one of which is the use of a mask on the face In this research, we offer a strategy for reducing covid-19 development by identifying persons who are not using masks or are not wearing them appropriately Closed-circuit television (CCTV) cameras are used in smart cities and public places When a person without a mask is identified, the city network informs the authorities The deep learning design is based on a data set that compares photos of individuals wearing and without wearing masks from a variety of sources For prior test information, the prepared design aided 97. 3 percent of existence on dysentery individuals with and without a facial mask It is anticipated that our research would prove to be a beneficial tool in protecting our people from diseases like these
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