Traditional infrared (IR) thermography faces fundamental limitations in remotely detecting zero-value porcelain insulators on live transmission lines, primarily due to inadequate spatial resolution and high susceptibility to environmental interference. These constraints severely hinder the identification of subtle thermal anomalies indicative of advanced-stage degradation. This study introduces an innovative non-contact temperature measurement approach utilizing passive radio frequency identification (RFID) technology. To overcome the inherent nonlinearity and device-to-device variability of commercial RFID temperature sensors—the primary sources of measurement inaccuracy—we developed a novel two-stage calibration model integrating adaptive median filtering for noise suppression and high-order polynomial regression for systematic error correction. Extensive experimental validation demonstrates that our model achieves a remarkable 71.4% reduction in mean absolute error (MAE), decreasing it from 0.63 ℃ to 0.18 ℃ compared to uncorrected measurements. Rigorous comparative analyses confirm the calibrated RFID system significantly outperforms standard IR thermography in detection accuracy and robustness, consistently identifying critical temperature differentials as small as 0.5 K that are typically undetectable by IR methods. As a pure software solution requiring no hardware modifications, the proposed calibration framework can be seamlessly integrated into unmanned aerial vehicle (UAV)-based inspection systems. This advancement provides power utilities with a robust and practical tool for condition monitoring of transmission line insulators, significantly enhancing the reliability of online diagnosis for deteriorated insulators while maintaining cost-effectiveness and operational simplicity.