Hierarchical Thermography Temperature Calibration Using Semantic Segmentation With Quantified Uncertainty for Emissivity Model Prior

Infrared thermography is widely used in intelligent industrial anomaly detection and automated monitoring systems for its noncontact temperature measurement capabilities, while visible images often serve as complementary information sources. However, infrared propagation and external environmental disturbances introduce measurement uncertainties—among which surface emissivity remains particularly difficult to quantify. To address this challenge, we present an efficient temperature calibration method that integrates Bayesian inference-based semantic segmentation with uncertainty quantification, enabling accurate calibration across equipment with diverse material surfaces. Specifically, a variational Bayesian approach (VBA) is developed for semantic segmentation of infrared thermography, incorporating aligned visible images as a helper for better segmentation, thanks to their richer textures and structural geometry information. Using the segmentation results along with their quantified uncertainties, we introduce a prior emissivity model and construct a hierarchical temperature calibration framework that accounts for physical fluctuations and segmentation-induced errors. Final temperature estimates are derived using a maximum a posteriori (MAP) inference algorithm. Experimental results on blackbody and blower datasets validate the effectiveness of the proposed method in calibrating temperatures across materials with varying emissivities.