基于多类深度学习的面罩检测高效两阶段识别

R. Dhaya
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引用次数: 14

摘要

世界卫生组织(WHO)认为COVID-19冠状病毒是一种全球大流行。在公共场所戴口罩是最有效的防护措施。此外,COVID-19大流行促使所有国家都建立了封锁,以防止病毒传播。根据一项调查研究,在工作中使用口罩可以减少快速传播的机会。如果不使用口罩或佩戴不当,就会导致冠状病毒在世界各地传播的第三波和第四波。这促使我们对口罩识别系统进行有效的调查,并监测在公共场所使用合适口罩的人。深度学习是在拥挤的地方检测一个人是否戴口罩的最有效方法。本研究利用多类深度学习技术,提出了一种有效的两阶段识别(ETSI)人脸检测方法。然而,二值分类不能提供关于口罩检测和错误的信息。该方法采用CNN的“ReLU”激活函数对口罩进行检测。此外,在当前的大流行危机中,本研究文章提供了一种非常有效和精确的方法来识别COVID-19。由于在最终输出中使用了多类缩写,精度得到了提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Efficient Two Stage Identification for Face mask detection using Multiclass Deep Learning Approach
The World Health Organization (WHO) considers the COVID-19 Coronavirus to be a global pandemic. The most effective form of protection is to wear a face mask in public places. Moreover, the COVID-19 pandemic prompted all the countries to set up a lockdown to prevent viral transmission. According to a survey study, the use of facemasks at work decreases the chances of fast transmission. If the facemasks are not used or are worn incorrectly, it contributes to the third and fourth waves of the corona virus spreading throughout the world. This motivates us to conduct an efficient investigation of the face mask identification system and monitor people, who use suitable face mask in public places. Deep learning is the most effective approach for detecting whether or not a person is wearing a face mask in a crowded area. Using a multiclass deep learning technique, this research study proposes an efficient two stage identification (ETSI) for face mask detection. Whereas, the binary classification does not offer information about face mask detection and error. The proposed approach employs CNN's "ReLU" activation function to detect the face mask. Furthermore, in the current pandemic crisis, this research article offers a very efficient and precise approach for identifying COVID-19. Precision has increased as a result of the employment of a multi-class abbreviation in the final output.
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