Prevention of the spread of viral disease using artificial intelligence from data obtained by UAVs

Abstract

Research background: One of the major globalization consequences is a threat of rapidly spreading communicable diseases. In recent months, COVID-19 has spread worldwide. It is a highly infectious disease, which is manifested mainly by fever, respiratory problems, muscle pain and fatigue. Therefore, there is a need to reliable monitor people's body temperature. If the monitoring process takes places in enclosed spaces, the procedure may be performed at the entrance to the building. However, the problem occurs in public spaces. Therefore, to solve this problem, we propose the use of a drone with a thermal camera for scanning people in public spaces and subsequent evaluation using classification methods. Purpose of the article: The aim of this work is to create a model for sensing and measuring the body temperature of people in public spaces, so that the global impacts of COVID-19 on the economy and society are reduced. Methods: To monitor large areas, it is necessary to have suitable methods for obtaining quality data. One of the methods for obtaining data with high spatial resolution is the use of UAVs with a planned flight. Artificial intelligence methods will be used for the classification of persons, their representatives are e.g. convolutional neural networks. Findings & Value added: The proposed model of sensing and subsequent classification of people into groups (normal body temperature, elevated body temperature). The output of the model will help to monitor the spread of infectious diseases (the condition is a symptom - increased body temperature) in today's globalized world.