## 4 Performance Evaluation and Analysis
### Results and Discussion
#### 4.3.2 Model-Specific Performance Insights  
The performance comparison of Post-ResNet-50, Post-DenseNet-121, and Post-ViT, trained using the synthetic thermographic image dataset with the transformation module applied, revealed that Post-ViT demonstrated the best performance. Post-ViT achieved an accuracy of 51.62%, approximately 14% higher than the other models. It recorded a specificity of 98.91%, indicating its ability to detect nearly all non-defective data without omission. Precision was 94.57%, reflecting a high probability that predictions of defects correspond to actual defects. Furthermore, Post-ViT achieved the highest recall value of 14.81%, outperforming the other models. These results highlight the overall superiority of ViT in defect detection performance.  
Conventional CNN-based models such as ResNet-50 and DenseNet-121 primarily focus on local features, making it challenging to capture spatially distant relationships between temperature variations. The superior performance of ViT compared to ResNet-50 and DenseNet-121 can be attributed to its ability to effectively learn the interactions between spatially distributed temperatures in thermographic data. In the thermographic data from wind turbine blades, heat transfer characteristics caused by defects often exhibit complexity, where the temperature at a specific location can influence distant, non-adjacent areas. ViT excels in capturing such global relationships.  
The self-attention mechanism in ViT effectively models the interactions between spatially distant temperature changes, enabling it to reflect the complex heat transfer characteristics of wind turbine blades. This is evidenced by its high performance in specificity, precision, and recall, positioning ViT as the most effective model for defect detection among the evaluated architectures.  
The performance of deep learning models trained on the real thermographic image dataset was experimentally compared with that of models trained exclusively on the synthetic thermographic image dataset. RID-ResNet-50, RID-DenseNet-121, and RID-ViT are deep learning models trained using 80% of the real thermographic image dataset as the training set, with the remaining 20% of the data, which was not used during training, serving as the evaluation set.  
When compared to Post-ResNet-50, Post-DenseNet-121, and Post-ViT, the differences in accuracy were relatively small: 13.7% for ResNet-50, 2.97% for DenseNet-121, and 5.06% for ViT. This can be attributed to the lack of contrast between defective and non-defective areas in the early and late heating stages when the specimen approaches thermal equilibrium, which results in the disappearance of the characteristic features of the defect class. Figure 11(a) shows the thermographic image of the RD78-50 specimen obtained at a heating time of 4 s, while Figure 11(b) shows the image obtained at a heating time of 150 s. At 150 s, a clear contrast between defective and non-defective areas is observed. These findings suggest that the physical characteristics of thermal equilibrium observed in thermographic datasets can negatively impact the model's performance.  
Figure 12: Changes in Thermographic Images of the RD78-50 Specim Over Time.