## 2 Related Works
### Deep Learning-Based Thermal Inspection for Internal Defects
Research applying deep learning techniques for defect detection using the infrared inspection (IR) of composite materials such as CFRP and Glass Fiber Reinforced Plastic (GFRP) has been actively conducted. Table 1 summarizes the differences in objectives and approaches between previous studies and the present research. [PERSON] et al. 's study [11] proposed a method for automatically detecting defect locations in CFRP specimens using FEM-based synthetic data. However, it did not address the domain gap between synthetic and real data, which limited the model's performance. [PERSON] et al. 's study [15] focused on enhancing defect visibility through Deep Autoencoder Thermography, achieving significant improvements in visualizing subsurface defects. Nevertheless, it remained limited to visualization without progressing to quantitative detection or localization. These studies highlight the need for approaches that bridge the domain gap and advance beyond visualization to improve defect detection in composite materials. [PERSON] et al. 's study [16] addressed the issue of blurred edges in thermal imaging data by integrating RGB and infrared thermal images using MSX. Their findings demonstrated that utilizing an edge-enhanced dataset in an ensemble learning-based deep learning model significantly improved defect detection accuracy. Similarly, [PERSON] et al. 's study [17] employed a combination of RGB and infrared thermal images to create a dataset, incorporating a regression crop technique using the LineNet and an RGN-IR feature fusion module. Their results confirmed that these enhancements led to improved accuracy in the YOLOv7 model compared to conventional methods. However, both studies faced limitations due to the restricted availability of data collected in laboratory environments, preventing the acquisition of the sufficiently large datasets necessary for robust model generalization. These studies emphasize the necessity for approaches that reduce the domain gap and go beyond mere visualization to enhance defect detection performance in composite materials. Furthermore, they highlight the need for an environment that facilitates the acquisition of a sufficiently large dataset. This study proposes a synthetic data generation method that facilitates data acquisition. Additionally, it aims to improve defect detection performance in composite materials by bridging the domain gap between real and synthetic data and proposing a defect detection method that extends beyond visualization to support decision-making. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Study** & **Objective** & **Data Type** & **Model/Methodology** & **Performance Metrics** \\ \hline [PERSON] et al. [11] & Defect detection and localization in & Real and synthetic data & Faster RCNN with transfer & Localization accuracy \\  & CFRP using Flash IRT & Real data & DATE (Deep Autoencoder & Signal-to-Noise Ratio, D-value \\ \hline [PERSON] et al. [15] & Enhance defect visibility through & Real data & DATE (Deep Autoencoder & Signal-to-Noise Ratio, D-value \\  & feature extraction & & Thermography) & Signal-to-Noise Ratio, D-value \\ \hline [PERSON] et al. [16] & Enhance edge visualization and & Real data & MSX for data processing & \\  & detect defects & Real data & Ensemble model of ViT and & Confusion matrix \\  & & & DenseNet61 & \\ \hline [PERSON] et al. [17] & Small object detection and defect & & LineNet and the RGB-IR & \\  & detection through data fusion & & feature fusion module for data & \\  & detection through data fusion & & processing & \\  & & & YOLOv7 & \\ \hline \hline \end{tabular}
\begin{tabular}{c c c} \hline \hline  & & & \\ \end{tabular}
\end{table} 
Table 1: Summary of Objectives, Data Types, and Methodologies in Deep Learning-Based Thermal Inspection Studies.