## 3 Thermography Data Collection and Preparation
### Analysis of Similarities Between Real and Synthetic Thermographic Image Datasets  
The similarity of the synthetic thermographic image dataset, constructed for training the deep learning model, was analyzed in comparison to the real thermographic image dataset. Before inputting the synthetic thermographic image dataset obtained from the synthetic specimen into the transformation module, the variation in specific pixel values at certain coordinates in consecutive thermographic images was expressed as a normal distribution. After inputting the synthetic thermographic image dataset into the transformation module, the variation in specific pixel values at the same coordinates in consecutive thermographic images was again expressed as a normal distribution. This was then compared to the normal distribution representing the variation in specific pixel values at the corresponding coordinates in the real thermographic image dataset of the real specimen. The analysis evaluated the effectiveness of preprocessing via the transformation module in enhancing the similarity between the synthetic and real thermographic image datasets. The difference in pixel values at specific coordinates between the synthetic thermographic images at time \(t\) and \(t+1\) in the synthetic thermographic image dataset was defined as the pixel value rate of change. This definition was designed to effectively simulate the contrast in the temperature rate of change resulting from the difference in heat transfer rates between the defect and non-defect regions observed in the real specimen. The similarity between the pixel value rate of change in the synthetic thermographic dataset and the rate of change distribution in the real thermographic image dataset indicates that the temperature data obtained from the synthetic specimen faithfully reproduces the physical characteristics of the real data. Figure 8 presents the pixel value rates of change, ranging from 0 to 255, over time at 15 randomly selected coordinates in the defect region and 15 randomly selected coordinates in the non-defect region, from both the real thermographic image dataset obtained from the RD60-30 specimen and the synthetic thermographic image dataset obtained from the \begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **Number of Images** &
\begin{tabular}{c} **Number of Thermographic** \\ **Image Sequence** \\ \end{tabular} \\ \hline Defect & 8640 & 24 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Class-Wise Data Distribution of the Synthetic Thermographic Image Dataset. SD60-30 specimen. The pixel value rates of change are expressed as a normal distribution. By comparing the rate of change in pixel values over time of the real thermographic image dataset and the pre-transformation and post-transformation synthetic thermographic image datasets using the normal distributions shown in Figure 8, the effectiveness of the transformation module, aimed at making the synthetic thermographic image dataset resemble the real thermographic image dataset, was evaluated. In Figure 8, the blue line represents RID, indicating the distribution of the rate of change in pixel values at specific coordinates extracted from the real thermographic image dataset. The gray line represents SID (pre-transformation), showing the distribution of the rate of change in pixel values extracted from the synthetic thermographic image dataset before being input into the transformation module. The yellow line represents SID (post-transformation), showing the distribution of the rate of change in pixel values extracted from the synthetic thermographic image dataset after processing by the transformation module. The x-axis (rate of change) of the graph displays the rate of change in pixel values at selected coordinates over time. The y-axis represents the probability density, indicating the likelihood of the occurring specific pixel value change rates (x-axis value). The mean pixel value change rate distribution RID in the real thermographic image dataset is 0.139.