Automatic extraction of data from infra-red thermometers with computer vision

Abstract

Contactless infrared thermometers (NCITs) have been widely used as a rapid screening tool for patients with fever, particularly during the peak of the Covid-19 pandemic in Africa and other developing countries. The screening information that can be gathered in this manner is difficult to leverage from a pandemic response perspective as common handheld thermometers have no means of automatically exporting measurement data. An algorithm for a mobile phone-based application is developed that is capable of automatically extracting the temperature information from common NCITs by making use of machine vision techniques and partitioning tasks between the screening operator and the mobile application. Temperature readings could accurately be captured from a range of devices with varying text characteristics and backlights in a range of lighting conditions. Additionally the methodology proposed is capable of running on low-end devices without a data connection and without extensive training requirements for different NCITs being required. The use of a low-cost smart-phone as the capturing device together with the involvement of the operator has been show to be a feasible alternative to fully automating processes. Results indicate that similar methods could be used in a range of applications where numerical data needs to be digitised from existing field instrumentation, particularly where such instrumentation feature segmented displays.