Artificial intelligence-enabled Internet of Things-based system for COVID-19 screening using aerial thermal imaging
Autor: | Ahmed Barnawi, Bander A. Alzahrani, Rajkumar Tekchandani, Prateek Chhikara, Neeraj Kumar |
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Jazyk: | angličtina |
Rok vydání: | 2021 |
Předmět: |
Scheme (programming language)
Thermal imaging Computer Networks and Communications Computer science Object detection 02 engineering and technology Facial recognition system Unmanned aerial vehicles Article 0202 electrical engineering electronic engineering information engineering Face recognition Edge computing computer.programming_language Data processing business.industry Deep learning COVID-19 020206 networking & telecommunications Hardware and Architecture Analytics 020201 artificial intelligence & image processing Computer vision Artificial intelligence business Raw data computer Software |
Zdroj: | Future Generations Computer Systems |
ISSN: | 1872-7115 0167-739X |
Popis: | 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. |
Databáze: | OpenAIRE |
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