用于医疗保健和安全应用的实用混合物联网架构与深度学习技术

Inf. Comput. Pub Date : 2023-07-03 DOI:10.3390/info14070379
Viet Q. Vu, Minh-Quang Tran, Mohammed Amer, Mahesh Khatiwada, S. Ghoneim, M. Elsisi
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引用次数: 0

摘要

即使在COVID-19大流行的背景下,口罩检测技术也变得越来越重要。随着人脸识别技术的进步,人脸检测已成为各种应用的关键功能。本文介绍了一种基于深度学习算法You Only Look Once (YOLO)的物联网(IoT)架构,以保持社会的健康和安全,并为未来的研究收集数据。所提出的范式是建立在经济考虑的基础上的,并且易于实现。然而,使用的YOLOv4-tiny是现有最快的目标检测模型之一。利用NVIDIA Jetson Nano边缘纳米设备计算能力的面具检测摄像头(MaskCam)与智能相机应用程序并排构建,用于检测个人脸上的面具。MaskCam区分口罩佩戴者,未佩戴口罩的人,以及根据MQTT协议未正确佩戴口罩的人。此外,MaskCam系统自带一个自行开发的网页浏览应用程序,用于收集和可视化统计数据,用于定性和定量分析。实际结果证明了所提出的智能掩模检测系统的优越性和有效性。一方面,即使在较小的分辨率下,YOLOv4-full也获得了最好的结果,尽管帧率太小,无法实时使用。另一方面,无论检测质量如何,它的速度都是其他检测模型的两倍。因此,推理可以在整个视频序列上更频繁地运行,从而产生更准确的输出。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Practical Hybrid IoT Architecture with Deep Learning Technique for Healthcare and Security Applications
Facial mask detection technology has become increasingly important even beyond the context of the COVID-19 pandemic. Along with the advancement in facial recognition technology, face mask detection has become a crucial feature for various applications. This paper introduces an Internet of Things (IoT) architecture based on a developed deep learning algorithm named You Only Look Once (YOLO) to keep society healthy, and secured, and collect data for future research. The proposed paradigm is built on the basis of economic consideration and is easy to implement. Yet, the used YOLOv4-tiny is one of the fastest object detection models to exist. A mask detection camera (MaskCam) that leverages the computing power of NVIDIA’s Jetson Nano edge nanodevices was built side by side with a smart camera application to detect a mask on the face of an individual. MaskCam distinguishes between mask wearers, those who are not wearing masks, and those who are not wearing masks properly according to MQTT protocol. Furthermore, a self-developed web browsing application comes with the MaskCam system to collect and visualize statistics for qualitative and quantitative analysis. The practical results demonstrate the superiority and effectiveness of the proposed smart mask detection system. On the one hand, YOLOv4-full obtained the best results even at smaller resolutions, although the frame rate is too small for real-time use. On the other hand, it is twice as fast as the other detection models, regardless of the quality of detection. Consequently, inferences may be run more frequently over the entire video sequence, resulting in more accurate output.
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