TY - JOUR
T1 - A novel drone-based system for accurate human temperature measurement and disease symptoms detection using thermography and AI
AU - Al Maashri, Ahmed
AU - Saleem, Ashraf
AU - Bourdoucen, Hadj
AU - Eldirdiry, Omer
AU - Al Ghadani, Ahmed
N1 - Funding Information:
The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Ahmed Al Maashri reports financial support was provided by the Ministry of Higher Education, Research, and Innovation in Oman .
Funding Information:
The authors would like to express their sincere gratitude to the Ministry of Higher Education, Research, and Innovation for funding this project (Research Grant RC/COVID-ENG/ECED/20/01 ). Also, the investigators would like to thank Sultan Qaboos University for providing a conducive academic environment to carry out the research. In addition, the research team highly appreciates the support that the Ministry of Health and the Telecommunications Regulatory Authority have provided in the early stages of the research. Furthermore, the authors thank Data2Cloud for providing Cloud services for data storage and algorithm processing. Finally, the investigators appreciate the assistance provided by AZIM TECHNOLOGY L.L.C. and its CEO, Mr. Yaqoob Al Hatali for conducting some of the field tests on and off-campus.
Publisher Copyright:
© 2022 Elsevier B.V.
PY - 2022/8/1
Y1 - 2022/8/1
N2 - The world continues to witness several waves of COVID-19 spread due to the emergence of new variants of the SARS-CoV-2 virus. Stopping the spread requires synergistic efforts that include the use of technologies such as unmanned aerial vehicles and machine learning. This paper presents a novel system for detecting disease symptoms from a distance using unmanned aerial vehicles equipped with thermal and visual image sensors. A hardware/software system that uses thermography to accurately calculate the skin temperature of targeted individuals using thermal cameras is developed. In addition, machine vision algorithms are developed to recognize human actions such as coughing and sneezing, which are paramount symptoms of respiratory infections. The proposed system is implemented and tested in outdoor environments. The results of experiments showed that the system can determine the skin temperature of multiple targeted individuals simultaneously with an error of less than 1 °C. The field experiments showed that the developed system is capable of simultaneously measuring the temperature of more than 10 individuals in less than 5 seconds. Just to give a perspective, it takes at least 3 seconds to measure one individual's temperature if this was done using traditional methods. Furthermore, the results showed that the system has accurately detected actions such as coughing and sneezing with almost 96% accuracy at a real-time performance of 28 frames/second.
AB - The world continues to witness several waves of COVID-19 spread due to the emergence of new variants of the SARS-CoV-2 virus. Stopping the spread requires synergistic efforts that include the use of technologies such as unmanned aerial vehicles and machine learning. This paper presents a novel system for detecting disease symptoms from a distance using unmanned aerial vehicles equipped with thermal and visual image sensors. A hardware/software system that uses thermography to accurately calculate the skin temperature of targeted individuals using thermal cameras is developed. In addition, machine vision algorithms are developed to recognize human actions such as coughing and sneezing, which are paramount symptoms of respiratory infections. The proposed system is implemented and tested in outdoor environments. The results of experiments showed that the system can determine the skin temperature of multiple targeted individuals simultaneously with an error of less than 1 °C. The field experiments showed that the developed system is capable of simultaneously measuring the temperature of more than 10 individuals in less than 5 seconds. Just to give a perspective, it takes at least 3 seconds to measure one individual's temperature if this was done using traditional methods. Furthermore, the results showed that the system has accurately detected actions such as coughing and sneezing with almost 96% accuracy at a real-time performance of 28 frames/second.
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U2 - 10.1016/j.rsase.2022.100787
DO - 10.1016/j.rsase.2022.100787
M3 - Article
C2 - 35702485
AN - SCOPUS:85132238263
SN - 2352-9385
VL - 27
SP - 100787
JO - Remote Sensing Applications: Society and Environment
JF - Remote Sensing Applications: Society and Environment
M1 - 100787
ER -