Deep Learning Based Facemask Detection

Priscilla Whitin, V. Jayasankar
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Abstract

The Covid-19 pandemic created a massive impact on various sectors across the globe. Nearly 400 million people have been affected by Covid-19 as of January 2022. Although vaccines have been developed, only 49.8% of world population have been vaccinated. The W.H.O has advised the public to maintain social distance in crowded places and wear well fitted mask to impede the spread of corona virus. It has been made mandatory by most countries to wear mask in public places, human monitoring continuously is impossible hence we deploy Deep learning model to implement the same. In this paper we have trained mobilenetV2 architecture for facemask detection using custom dataset. The accuracy of the model in real time is 99.99%
基于深度学习的面罩检测
新冠肺炎疫情对全球各行业产生了巨大影响。截至2022年1月,已有近4亿人受到Covid-19的影响。虽然已经研制出疫苗,但只有49.8%的世界人口接种了疫苗。世卫组织建议公众在人群密集的地方保持社交距离,并佩戴合适的口罩,以阻止冠状病毒的传播。大多数国家都强制要求在公共场所戴口罩,不可能持续进行人工监控,因此我们使用深度学习模型来实现相同的目标。在本文中,我们使用自定义数据集训练了用于面罩检测的mobilenetV2架构。该模型的实时精度为99.99%
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