## 4 Results Analysis
### Model Comparisons  
Following the model's configuration, the consistent and fair comparison of classification performance was ensured by maintaining uniform dataset segmentation and parameters across the PRI-SWT, SVM, and ViT models. Both SVM and ViT were trained using either magnitude or phase images. The confusion matrices are shown in Figure 10, where 1 corresponds to crack defects, 2 to delamination, and 3 to impact damage. The detailed performance metrics for each model are presented in Table 2.  
Building on the previous performance comparison, the PRI-SWT model demonstrates significant improvements over traditional methods. The ViT model, when using phase images, achieved macro-P, macro-R, macro-F1, and Kappa scores of 88.1%, 85.8%, 86.9%, and 80.9%. In comparison, the PRI-SWT model improved these metrics by 7.1%, 8%, 7.6%, and 11.5%, respectively, due to its ability to fuse magnitude and phase information from CFRP eddy current detection. This fusion ensures the comprehensive extraction of defect characteristics and reduces the risk of missing key information when relying on a single image.  
CFRP defects vary widely in terms of size, with cracks ranging from small to medium scales, and impact damage covering larger areas, as shown in Figure 11.  
CNN models, while generally more sensitive to smaller features, tend to struggle with larger defects. In contrast, the PRI-SWT model, utilizing Transformer capabilities, captures both small- and large-scale features, resulting in significantly improved classification performance.  
The validation set accuracy versus epoch training curves for both the ViT and PRI-SWT models are illustrated in Figure 12. The PRI-SWT model not only demonstrates superior convergence during training, achieving up to 97% accuracy on the validation set,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **macro-P** & **macro-R** & **macro-F1** & **Kappa Coefficient** \\ \hline SVM\_RealImage & 0.783 & 0.765 & 0.774 & 0.655 \\ SVM\_ImagImage & 0.666 & 0.637 & 0.651 & 0.456 \\ Vi-T\_RealImage & 0.737 & 0.651 & 0.691 & 0.535 \\ Vi-T\_ImagImage & 0.881 & 0.858 & 0.869 & 0.809 \\ RPI-SWT & 0.952 & 0.938 & 0.945 & 0.924 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of evaluation indicators.  
Figure 11: Eddy current testing results for three types of CFRP defect: (**a**) crack of small size; (**b**) crack of medium size; (**c**) low-velocity impact of large size. The three types of CFRP defects vary in shape and scale.  
Figure 10: The figure provides detailed confusion matrices for several classification models. It also includes the ROC curves for the typical ViT and PRI-SWT models, with the AUC values for the classification of the three CFRP defect types clearly indicated in the ROC curve images. In the figures, labels 0, 1, and 2 correspond to crack, delamination, and impact damage defects, respectively. The results demonstrate the relative superiority of the PRI-SWT model.  
but also outperforms the ViT model in terms of both speed and stability. As shown in Figure 12, the training curves indicate that PRI-SWT converges more rapidly and maintains higher accuracy throughout the process. This enhanced performance is attributed to its innovative architecture, which effectively merges magnitude and phase information for the more comprehensive extraction of defect characteristics. The PRI-SWT model stabilizes around epoch 20, showcasing its robustness in handling the complexities of CFRP defect classification.