An Intelligent Multi-Emissivity Infrared Temperature Correction Method for Substation Equipment Based on Semantic Segmentation

Emissivity is a critical parameter in infrared temperature measurement and varies significantly among different materials. Infrared thermography has been widely used for the inspection of substation equipment. However, substations contain a large number of devices with complex structures, making it impractical to assign a separate emissivity value to each device or component. This limitation can significantly affect temperature measurement accuracy. To address this issue, this paper proposes an intelligent multi-emissivity temperature correction method for infrared images of substation equipment. First, a temperature–emissivity correction function is established. Then, a total of 2189 infrared images of substation equipment are collected, and the main equipment components are annotated at the pixel level. Subsequently, an equipment component segmentation model based on DeepLabv3+ is trained. Finally, different emissivity values are assigned to different component regions for temperature correction, and corrected infrared pseudo-color images are regenerated. In the experiment, the temperature values before and after correction are compared with thermocouple measurements. In the present validation experiment, the average deviation between the corrected infrared temperature and the thermocouple measurement was reduced by 79.2% compared with that before correction.