## 5 Conclusions  
The PRI-SWT model was developed to address the challenges of multi-defect classification in CFRP materials using complex voltage signals. In the experiment, eddy current testing was performed on CFRP specimens with cracks, delamination, and impact damage using a nine-element probe. Amplitude and phase images, reflecting the characteristics of these defects, were collected. Through a series of data acquisition and processing steps, a total of 2612 crack defect images, 1750 impact damage images, and 1372 delamination defect images were generated, providing comprehensive data on the electromagnetic response characteristics of various defect types. To ensure the diversity of the model's training data, the dataset was carefully split into training and validation sets. Specifically, for crack defect images, there were 1828 training images and 784 validation images; for impact damage, there were 1225 training images and 525 validation images; and for delamination defects, there were 960 training images and 412 validation images. The dataset was divided along a 7:3 ratio, allowing the model to be trained on a large dataset while its performance on unseen data was evaluated using the validation set. This approach enhances the model's generalization ability and mitigates issues such as overfitting and underfitting. The results demonstrate that the macro-F1 score improved by 29.4%, reaching 0.945, while the Kappa coefficient improved by 46.8%, achieving 0.924, compared to the SVM and Vision Transformer (ViT) algorithms. Ablation experiments confirmed that the linear projection fusion layer outperforms the 2D convolutional fusion layer, with a macro-F1 score of 0.945 versus one of 0.928 for the convolutional layer, highlighting its superior effectiveness in information integration. Additionally, reducing window displacement improved classification performance, achieving a macro-P score of 0.963, while increasing the window size led to a macro-F1 score of 0.961. Deepening the network further enhanced the model's performance, as reflected in a Kappa coefficient of 0.946. Smaller window shifts allowed for improved capture of localized defect features, whereas larger windows provided valuable contextual information. These advancements demonstrate the significant potential of the PRI-SWT model regarding accurate CFRP defect classification, promoting more reliable nondestructive testing in engineering applications. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Depth of PRI-SWT** & **macro-P** & **macro-R** & **macro-F1** & **Kappa Coefficient** \\ \hline \([2,2,4,6]\) & 0.952 & 0.938 & 0.945 & 0.924 \\ \([2,4,4,6]\) & 0.963 & 0.957 & 0.96 & 0.939 \\ \([4,4,4,6]\) & 0.963 & 0.96 & 0.961 & 0.946 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Evaluation indicators of models of different depths. Although the PRI-SWT model has made significant improvements in recognition and classification performance for CFRP defect eddy current testing, it is important to note the increased training and inference time costs associated with the complexity of the network. The introduction of strategies, such as dual parallel information flow and fusion reasoning layers, further contributes to the increased time overhead. For the ViT model, the typical inference time is 11.2 s, while the Swin Transformer model offers some improvement, reducing the inference time to 10.56 s. However, the PRI-SWT model requires approximately 30.59 s. When classification accuracy is prioritized over the time cost, PRI-SWT is an excellent choice. However, this comes with challenges in terms of industrial real-time detection applications due to its increased computational demands. In future research, the scalability of the PRI-SWT model will be tested on more complex defect types, such as delamination defects and fiber wrinkles.