基于卷积神经网络和预训练CNN模型的Covid-19大流行时代口罩检测

Ivana Lucia Kharisma, R. Handayanto, D. A. Dewi
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引用次数: 0

摘要

自2020年初以来,冠状病毒或Covid-19已在全球广泛传播。世卫组织提供了社区可以完成的预防病毒传播的基本指导。其中之一是在户外活动时戴口罩。缺乏口罩使用意识成为防止新冠肺炎传播过程中的障碍。本研究的目的是利用卷积神经网络和预训练的CNN算法开发一个人脸检测模型。本文提出的模型在训练过程中的准确率,CNN、VGG16和VGG19的准确率分别为97.79%、99.87%和100%。利用给出的测试数据集使用混淆矩阵对所提出的模型进行评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Face Mask Detection In The Covid-19 Pandemic Era by Implementing Convolutional Neural Network and Pre-Trained CNN Models
The Coronavirus or Covid-19 has spread widely throughout the world since the beginning of 2020. WHO provides basic guidance in preventing the spread of the virus that can be done by the community. One of them is the use of masks when doing activities outside the home. Lack of awareness in mask usage become the obstacle in the process of efforts to prevent the spread of covid 19. The aim of this research is to develop a face mask detection model by implementing the convolutional neural network and pre trained CNN algorithm. The accuracy of the proposed models in training process, the accuracy of CNN, VGG16, and VGG19 are 97.79%, 99.87% and 100%, respectively. The proposed models evaluated using confusion matrix using testing datasets given.
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