Dual-Modal Fusion PRI-SWT Model for Eddy Current Detection of Cracks, Delamination, and Impact Damage in Carbon Fiber-Reinforced Plastic Materials  
[PERSON], [PERSON] and [PERSON]  
ripples [14; 15; 16]. The accurate identification and classification of these defects were crucial for ensuring product safety, reliability, and economic efficiency. Research on CFRP defect classification algorithms has attracted considerable scholarly interest. For example, [PERSON] et al. [17] proposed a defect classification method using Support Vector Machines (SVMs) based on the energy characteristics of CFRP ultrasonic inspection results, demonstrating its effectiveness through experimental validation. [PERSON] et al. [18] conducted the ultrasonic detection of three types of CFRP defects--impact damage, foreign body inclusions, and porosity--and employed an artificial neural network (ANN) to classify these defects based on time domain ultrasonic echo signals, achieving a high classification accuracy that met project requirements. Similarly, [PERSON] et al. [19] introduced various intelligent classification methods into the CFRP automated fiber layup process, enhancing detection and production efficiency. The authors noted that increasing the depth of the network could improve the applicability of visual classification methods. Recent advances in hardware, particularly the iterative development of GPUs, have driven the rapid evolution of deep neural network algorithms, which require substantial computational power. Deep convolutional neural networks (DCNNs), Inception (GoogLeNet), Vision Transformer (ViT), and Swin Transformer models display outstanding performance in machine learning tasks such as natural language processing, image classification, object detection, and image segmentation [20; 21]. The use of deep network models in image classification tasks is becoming increasingly prevalent. For instance, [PERSON] et al. [22] utilized a dense convolutional neural network (DCNN) to classify infrared thermography detection results, identifying debonding, delamination, and cracks in CFRPs. Their experiments showed that DenseNet-121 outperformed other CNN architectures, such as AlexNet, VGG-16, and ResNet-50, achieving a classification accuracy of 98.48%, thus highlighting the potential of deep neural networks for use in CFRP defect classification. [PERSON] et al. [23] employed a Swin Transformer-based U-Net structure for crack detection and classification tasks, demonstrating a superior performance compared to other algorithms when used on a customized dataset. Additionally, the Transformer model has exhibited outstanding performance in terms of identifying and classifying defects in various contexts, including rail surfaces [24], insulators in transmission lines [25], and metal surfaces [26]. [PERSON] et al. [27] compared the classification performance of CNNs and ViT, concluding that ViT methods hold great promise in terms of predictive maintenance and the safety of railroads. ViT image classification methods have also been successfully applied in medical disease detection [28], chemical pipeline inspections [29], crop disease detection [30], product manufacturing damage detection [31], and industrial printed circuit board (PCB) classification [32]. To the best of the authors' knowledge, existing CFRP eddy current NDT defect classification methods predominantly rely on manual analysis and basic machine learning techniques. These approaches suffer from limitations such as subjectivity, high labor costs, and low resolution. Advanced computer vision algorithms, including Vision Transformers (ViTs) and Swin Transformer models, present promising solutions to these challenges. Compared to traditional image classification methods like Support Vector Machines (SVMs) and convolutional neural networks (CNNs), the ViT model offers superior accuracy and generalization for CFRP defect classification. This is attributed to its enhanced global sensing capabilities.