基于机器学习和边缘计算的人脸检测

Ivan Jovović, Dejan Babic, Stevan Cakic, Tomo Popović, S. Krco, Petar Knezevic
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引用次数: 3

摘要

本文描述了旨在将机器学习、物联网和边缘计算用于健康用例的研究工作,主要是预防传染病的传播。研究的主要动机是Covid-19大流行和需要改善对预防措施实施的控制。在这项研究中,实验的重点是使用机器学习来创建和利用口罩检测的预测模型。然后在各种平台上评估预测模型,重点是在配备摄像机传感器的各种边缘设备上的使用。对标准笔记本电脑、Raspberry Pi3、Jetson Nano AI edge等不同平台进行了测试和评估。最后,本文讨论了一种可能的方法来实现一个解决方案,将利用口罩检测功能,并为未来的研究步骤奠定了路径。
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Face Mask Detection Based on Machine Learning and Edge Computing
This paper describes research effort aimed at the use of machine learning, Internet of Things, and edge computing for a use case in health, mainly the prevention of the spread of infectious diseases. The main motivation for the research was the Covid-19 pandemic and the need to improve control of the prevention measures implementation. In the study, the experimentation was focused on the use of machine learning to create and utilize prediction models for face mask detection. The prediction model is then evaluated on the various platforms with a focus on the use on various edge devices equipped with a video camera sensor. Different platforms have been tested and evaluated such as standard laptop PC, Raspberry Pi3, and Jetson Nano AI edge platform. Finally, the paper discusses a possible approach to implement a solution that would utilize the face mask detection function and lays out the path for the future research steps.
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