Internet of Things (IoT) has recently brought an influential research and analysis platform in a broad diversity of academic and industrial disciplines, particularly in healthcare. The IoT revolution is reshaping current healthcare practices by consolidating technological, economic, and social views. Since December 2019, the spreading of COVID-19 across the world has impacted the world’s economy. IoT technology integrated with Artificial Intelligence (AI) can help to address COVID-19. UAVs equipped with IoT devices can collect raw data that demands computing and analysis to make intelligent decision without human intervention. To mitigate the effect of COVID-19, in this paper, we propose an IoT-UAV-based scheme to collect raw data using onboard thermal sensors. The thermal image captured from the thermal camera is used to determine the potential people in the image (of the massive crowd in a city), which may have COVID-19, based on the temperature recorded. An efficient hybrid approach for a face recognition system is proposed to detect the people in the image having high body temperature from infrared images captured in a real-time scenario. Also, a face mask detection scheme is introduced, which detects whether a person has a mask on the face or not. The schemes’ performance evaluation is done using various machine learning and deep learning classifiers. We use the edge computing infrastructure (onboard sensors and actuators) for data processing to reduce the response time for real-time analytics and prediction. The proposed scheme has an average accuracy of 99.5% using various performance evaluation metrics indicating its practical applicability in real-time scenarios.

物联网 (IoT) 最近为广泛的学术和工业学科带来了一个有影响力的研究和分析平台，尤其是在医疗保健领域。物联网革命正在通过整合技术、经济和社会观点来重塑当前的医疗保健实践。自 2019 年 12 月以来，COVID-19 在世界范围内的传播对世界经济产生了影响。与人工智能 (AI) 集成的物联网技术可以帮助解决 COVID-19 问题。配备物联网设备的无人机可以收集原始数据，需要计算和分析，从而在没有人为干预的情况下做出智能决策。为了减轻 COVID-19 的影响，在本文中，我们提出了一种基于物联网无人机的方案，使用机载热传感器收集原始数据。从热像仪捕获的热图像用于根据记录的温度确定图像中（城市中大量人群）可能患有 COVID-19 的潜在人物。提出了一种用于人脸识别系统的有效混合方法，以从实时场景中捕获的红外图像中检测图像中体温较高的人。此外，还引入了口罩检测方案，检测人的面部是否戴口罩。这些方案的性能评估是使用各种机器学习和深度学习分类器完成的。我们使用边缘计算基础设施（机载传感器和执行器）进行数据处理，以减少实时分析和预测的响应时间。所提出的方案的平均准确率为 99。