## 3 PRI-SWT Dual-Modal Fusion Model for CFRP Defect Detection
### Parallel Real-Imaginary/Swin Transformer Multi-Modal Fusion Model  
In CFRP eddy current detection, the data consist of two images representing the amplitude and phase information of the sinusoidal signal. To effectively extract defective features, it is crucial to merge these two intrinsically linked images. However, the high resolution and large volume of images pose significant challenges, as the self-attention mechanism in traditional Vision Transformer (ViT) models requires substantial memory and processing power, making it less efficient for such tasks.  
To address these issues, the innovative PRI-SWT model was designed, as illustrated in Figure 8. This model introduces an innovative approach by first concatenating the magnitude phase (or real-imaginary) images along the channel dimension, enabling parallel processing within the PRI-SWT framework. The complex input tensor, which combines the real and imaginary components of the CFRP detection signal, is processed through the first split layer and decoupled into two separate signals. These signals are then directed into two parallel SWT feature extractors--one above the other--to extract feature maps that are rich in low-level semantic information. Subsequently, various fusion layers, such as linear mapping layers or convolutional layers, are utilized to perform feature fusion and the concatenation of the two feature maps, which have different characteristics. This process enables complementary information exchange. The resulting fused feature map is then remapped and decoupled into two distinct feature maps, allowing for further high-level semantic information fusion. The complex input tensor, which integrates both the real and imaginary components of the CFRP detection signal, is processed through the first split layer, effectively decoupling it into two signals. These signals are subsequently routed into two parallel SWT feature extractors--one positioned above the other--to extract feature maps rich in low-level semantic information. Following this, different types of fusion layers, such as linear mapping layers or convolutional layers, are employed to perform feature fusion between the two distinct feature maps. This process facilitates complementary information exchange. The resulting fused feature map is then mapped and decoupled again into two separate feature maps, allowing for the continuation of high-level semantic information fusion. The PRI-SWT model not only addresses the computational challenges posed by high-resolution image data but also maximizes the utilization of complementary information from the amplitude and phase signals, thereby improving the accuracy and robustness of CFRP defect detection. Its ability to efficiently process large-scale data while preserving critical defect features demonstrates its superiority in tackling complex defect detection tasks.  
To enhance the exchange of information between different windows, the windows are artificially shifted, allowing for new self-attention feature maps to be calculated within these shifted windows, as depicted in Figure 9. This method not only optimizes the attention mechanism but also ensures that crucial defect features are effectively captured. Finally, the relevant features are extracted through real-imaginary (R-I) convolutional layer fusion, producing classification results after several stages of processing.  
The design of the dual-modal fusion approach used in the PRI-SWT model is both innovative and essential in addressing the limitations of existing methods. It significantly improves classification performance, as will be demonstrated in subsequent validation experiments, where the PRI-SWT model is shown to outperform traditional classifiers such as SVM and ViT.