However, purely vision-based methods typically extract features from single images, focusing on aspects such as the magnitude and phase components of the CFRP eddy current system's output voltage signals, as shown in Figure 1.  
These methods often overlook the interdependence between the complex signal components. In CFRP defect eddy current testing, the output sinusoidal voltage signal from the detection coil is passed through a lock-in amplifier to the data acquisition card, where it is assessed. The data are then processed to generate magnitude and phase detection results of the complex voltage signal. These signals are distinct yet deeply interrelated; they both reflect the same defect type but contain different semantic information with different meanings. Extracting their complementary information is a pressing challenge that needs to be addressed in the field of CFRP defect detection. Additionally, the dual-modal fusion strategy is widely employed in fields such as medicine, scientific research, and industrial applications. For instance, in healthcare, dual-modal fusion is utilized to combine imaging data, such as MRI or CT scans, with electronic health records (EHRs). This integration enhances diagnostic accuracy by correlating visual anomalies with patient history and clinical data, which facilitates more informed decision-making. Similarly, in robotics and human-computer interactions, dual-modal fusion combines visual and auditory information to improve object recognition and navigation. Figure 1: The figure presents the eddy current testing (ECT) results for the detection of cracks, delamination, and low-velocity impact damage in carbon fiber-reinforced polymer (CFRP) materials. Results were obtained utilizing a nine-array ECT probe. The sinusoidal voltage signal generated by the output coil of the probe is processed through a lock-in amplifier, yielding two output signals. The subsequent data processing of these signals results in amplitude and phase images of the scanning process. (a) Crack defect; (b) impact defect; (c) delamination defect. Robots can analyze camera feeds to identify objects while processing audio cues, enabling them to interact more effectively with their environment and with humans. In the realm of sentiment analysis, dual-modal approaches that integrate text and audio have demonstrated improved accuracy in terms of understanding emotions. By considering both the content of spoken language and the vocal tone, these systems capture nuances in sentiment that single-modal analyses might miss. Additionally, dual-modal fusion plays a critical role in security and surveillance, where video data are often combined with biometric information, such as facial recognition, to enhance identification accuracy and improve real-time threat detection. Therefore, this paper proposes a dual-modal fusion Parallel Real-Imaginary/Swin Transformer (PRI-SWT) network for use detecting CFRP defects from complex signal images using eddy current testing. The proposed model employs two parallel Swin Transformers (SWTs) to independently compute self-attention and generate feature maps for the real and imaginary components of the signal. A fusion layer--either a convolutional layer or a linear projection layer--is incorporated into the model to capture the relationships between the real and imaginary parts of the complex signal. By dividing the input image into several shiftable windows and calculating key metrics such as self-attention scores and relative positional encodings within each window, the PRI-SWT model significantly enhances both computational efficiency and classification accuracy. The model outperforms traditional algorithms, such as Support Vector Machine (SVM) and Vision Transformer (ViT), in CFRP defect classification, underscoring the substantial research effort invested in improving detection accuracy.