Thermal drift suppression algorithm for infrared thermal imaging gas detection systems

Owing to their advantages of low cost, compact structure, and no requirement for cryogenic cooling, uncooled infrared thermal imagers have been extensively employed in fields such as infrared gas detection, industrial monitoring, and environmental remote sensing. However, their output is highly prone to thermal drift, leading to a significant degradation in stability under complex outdoor operating conditions. To address this issue, a thermal drift correction method for temperature measurement based on a grayscale response model is proposed in this paper. Derived from the physical response characteristics of infrared detector pixel units, the method establishes a three-stage coupled model linking grayscale, radiance, and temperature. Experimental results demonstrate that the proposed correction model can effectively suppress thermal drift-induced errors, with an average coefficient of determination R2 reaching 0.99996 and a mean square error (MSE) of approximately 5.85. These findings provide a robust technical foundation for the deployment of uncooled infrared thermal imagers in high signal-to-noise ratio (SNR) application scenarios, such as hazardous gas monitoring and industrial safety inspection.