An Efficient Temperature Calibration Method Based on the Improved Infrared Forward Model and Bayesian Inference

Infrared thermography is widely used to detect abnormal body temperature because of its noncontact and large scale. Attenuation during infrared propagation causes the measured temperature to always be less than the actual value. This article proposes a novel method of temperature calibration based on Bayesian inference. In the first step, we propose an improved infrared radiation model (IIRM), which accounts for emissivity and measures the distance between the radiation source and the infrared imager. This study leverages naive Bayesian inference (NBI) to derive surface emissivity. Then, using the improved model, the parameters of the model and the temperature distribution are reconstructed by joint maximum a posterior (JMAP). The IIRM and JMAP method (IIRM-JMAP) improved the accuracy of temperature measurement. The improved infrared thermal radiation model is suitable for measuring scenarios with different measuring distances, different humidity factors, and different emissivities. The proposed method has been validated to have small errors through various experiments on a blackbody and high-speed direct-drive blower.