## 4 Performance Evaluation and Analysis
### Results and Discussion
#### 4.3.3 Analysis of Model Performance Based on Defect Depth and Size  
Table 7 presents the accuracy results based on defect depth. An analysis of Table 7 indicates that the models incorporating the transformation module tend to achieve higher accuracy for specimens with defects located farther from the embedded heating panel. For the RD60 real specimen, accuracy improved by 0.51% with the ResNet-50 model, 12.05% with the DenseNet-121 model, and 22.76% with the ViT model. Similarly, for the RD78 real specimen, accuracy increased by 3.52% with the ResNet-50 model, 6.8% with the DenseNet-121 model, and 20.28% with the ViT model. However, for specimens with defects located closer to the embedded heating panel, a decrease in accuracy was observed. Specifically, the accuracy for the RD20 real specimen decreased by 1.06% with the ResNet-50 model and by 1.49% for the RD42 real specimen. Furthermore, the accuracy for the RD29 real specimen decreased by 7.68% with the ViT model. When the embedded heating panel is in close proximity to the defect, the thermal signals generated through heat transfer weaken due to the material's absorption and dissipation of heat. As a result, although the time required for heat to reach the defect is shorter, the thermal signal is attenuated by the material in front of the defect, making the temperature difference between defective and non-defective regions less distinguishable. This likely indicates that the transformation module failed to generate synthetic data capable of adequately capturing these subtle temperature differences, thereby limiting the model's ability to learn from such variations. Thus, the model appears to have insufficiently learned the subtle temperature variations present in the RD29 real specimen and instead prioritized learning from stronger thermal signals. Consequently, while accuracy improved for deeper defects in the RD42, RD60, and RD78 real specimens, accuracy declined for the shallower defect in the RD29 real specimen. Table 8 presents the accuracy results based on defect size. An analysis of Table 8 reveals that, with a few exceptions in the ResNet-50 model, accuracy generally improves as defect size increases. Smaller defects often exhibit minimal temperature differences, making them more challenging for the models to detect. However, the transformation module effectively amplified these temperature differences, leading to enhanced detection performance in the DenseNet-121 and ViT models. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Defect Depth** & **Pre-ResNet-50** & **Post-ResNet-50** & **Pre-DenseNet-121** & **Post-DenseNet-121** & **Pre-ViT** & **Post-ViT** \\ \hline RN-1\(-\)RN-6 & 76.32\% & 83.85\% & 79.86\% & 66.06\% & 96.34\% & 98.91\% \\
2.9 mm (RD29) & 1.06\% & 0.00\% & 2.27\% & 4.23\% & 13.48\% & 5.81\% \\
4.2 mm (RD42) & 1.49\% & 0.10\% & 3.33\% & 13.18\% & 0.31\% & 8.26\% \\
6.0 mm (RD60) & 3.79\% & 4.30\% & 6.06\% & 18.11\% & 1.79\% & 24.55\% \\
7.8 mm (RD78) & 0.00\% & 3.52\% & 5.46\% & 12.26\% & 0.05\% & 20.33\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of Model Accuracy Pre- and Post-Processing Based on Defect Depth. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Defect Size (mm2)** & **Pre-ResNet-50** & **Post-ResNet-50** & **Pre-DenseNet-121** & **Post-DenseNet-121** & **Pre-ViT** & **Post-ViT** \\ \hline RN-1\(-\)RN-6 & 76.32\% & 83.85\% & 79.86\% & 66.06\% & 96.34\% & 98.91\% \\
10 \(\times\) 10 & 0.79\% & 2.59\% & 8.72\% & 16.18\% & 8.33\% & 12.18\% \\
20 \(\times\)20 & 0.92\% & 0.00\% & 4.98\% & 13.78\% & 11.03\% & 30.28\% \\
30 \(\times\) 30 & 0.62\% & 0.23\% & 4.11\% & 11.62\% & 3.56\% & 23.70\% \\
35 \(\times\) 35 & 3.46\% & 6.00\% & 1.84\% & 8.46\% & 0.00\% & 0.00\% \\
40 \(\times\) 40 & 3.45\% & 3.14\% & 4.29\% & 10.73\% & 0.00\% & 6.82\% \\
50 \(\times\) 50 & 0.23\% & 0.00\% & 1.88\% & 11.34\% & 0.08\% & 15.99\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of Model Accuracy Pre- and Post-Processing Based on Defect Size.