基于嵌入式学习模型的物联网实时掩码检测比较研究

International Journal of Advanced Science and Computer Applications Pub Date : 2023-12-26 DOI:10.47679/ijasca.v3i2.49

Mohamed Amine Meddaoui, M. Erritali, Françoise Sailhan

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引用次数: 0

摘要

冠状病毒爆发后，为减缓病毒传播，采取了许多预防措施。其中，口罩检测是一项关键的创新技术，它可以识别佩戴口罩的人数。为此，本文对支持口罩检测的几种机器学习和深度学习算法（如 SVM、RNN、Mask-RCNN、LSTM、CNN、Auto-Encoder、GAN、U-Net GAN）进行了比较研究。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A Comparative Study of Embedded Learning Models IoT-based for real time Mask Detection

Following the outbreak of the coronavirus, many preventive measures are implemented to slow down the transmission of the virus. Amongst others, facemask detection is a key innovative technology that allows the identificationof the number of individuals wearing face masks. In this regard, this paperprovides a comparative study of several machine learning and deep learningalgorithms (e.g., SVM, RNN, Mask-RCNN, LSTM, CNN, Auto-Encoder,GAN, U-Net GAN) that support mask detection.

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International Journal of Advanced Science and Computer Applications

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