Characterization and Quantitative Evaluation of Surface Cracks on Steel Plates Based on Eddy Current Pulsed Thermography

Eddy Current Pulsed Thermography (ECPT) is an emerging non-destructive testing technique widely applied for surface defect detection in metallic materials. This paper proposes a multi-stage synergistic optimization method to address key technical challenges, including background noise, low defect contrast, and complex thermal gradients. The proposed method comprises three stages: pre-processing, feature extraction, and post-processing. In the pre-processing stage, the excitation peak frame is dynamically selected using image entropy difference, static background noise is suppressed through differential operations, and a geometric prior mask is generated via edge detection. The feature extraction stage uses the Augmented Lagrangian Multiplier (ALM) method to solve a Differential Image-based Robust Principal Component Analysis (DI-RPCA) model. This process reconstructs a sparse matrix that preserves the complete defect morphology while simultaneously eliminating high-frequency noise from the coil region. In the post-processing stage, adaptive histogram equalization is applied to the sparse matrix to enhance indication edge details, and a Local Spatial Consistency-based Dual Thresholding Segmentation (LSC-DTS) method is designed for efficient anomaly-background separation. It combines adaptive thresholding based on image grayscale distribution with an area-based filtering mechanism for defect candidate regions. Experimental results on steel plates with natural fatigue cracks demonstrate that the proposed method offers significant advantages in quantitative detection performance over conventional methods.