Thermal Saliency-Based Spatial Weighting With Nonlinear Enhancement for Principal Component Thermography in ECPT

作者：Tengyan Xi, Ping Wang, Yan Shi, Yuan Zhang, Weihu Zhou · 发表于：IEEE Transactions on Industrial Informatics · 年份：2026 · DOI：10.1109/TII.2026.3658229 · 被引用次数：1 · 研究领域：Computer Science

The detection of weak defects in eddy current pulsed thermography is hampered by nonuniform heating and thermal diffusion, which cause low signal-to-noise ratio (SNR). While conventional principal component thermography (PCT) is widely used for data reduction, its nature as a “blind” statistical tool often fails to separate these subtle defect signatures from the dominant background noise. To address this, we adapt conventional PCT by introducing a saliency-based spatial weighting strategy. The core innovation is a weighting scheme where a spatial saliency map, derived from time-integrated thermal energy, is nonlinearly enhanced via a gamma transformation. Integrating this high-contrast weight map into the PCT algorithm transforms it from a blind statistical tool into a targeted, guided analysis. Validated on six diverse metallic specimens, the proposed spatially weighted PCT (SW-PCT) significantly outperforms traditional PCT and other representative methods in SNR, especially for enhancing weak defects. An optimized vectorized implementation ensures high computational efficiency, establishing SW-PCT as a robust solution that balances detection accuracy and processing speed for industrial nondestructive testing applications.