## 2 Related Works  
### Deep Learning-Based Thermal Inspection for Internal Defects  
[PERSON] et al. proposed a method for automatically detecting and localizing defects in Carbon Fiber Reinforced Plastic (CFRP) using the flash Infrared Thermography (IRT) technique combined with a deep learning-based model. To address the challenges of the time and cost involved in building large-scale real datasets, the study utilized a numerical simulator (FEM-based) to generate synthetic thermographic datasets. Preprocessing techniques such as Principal Component Analysis (PCA) and Trimmed Scores Regression (TSR) were applied to the generated datasets to compress the thermographic sequences, and the Faster Region-based Convolutional Neural Network (Faster-RCNN) deep learning model was employed for defect detection. The Faster-RCNN model, pretrained on the Common Objects in Context (COCO) dataset, was fine-tuned through transfer learning to accurately detect defect locations. This study experimentally validated the Faster-RCNN model using a small real dataset containing 12 artificially created rectangular delamination defects and evaluated defect detection performance by combining the synthetic and real data [11]. [PERSON] et al. developed a Deep Autoencoder Thermography (DAT) method to improve defect detection in composite materials by effectively processing nonlinear thermographic data. This method enhances the visibility of subsurface defects by leveraging the multilayer structure of deep autoencoders, which extract and highlight defect features while reducing noise and addressing inhomogeneous backgrounds. The intermediate features from hidden layers are visualized, showcasing the model's ability to smooth and denoise thermographic images progressively. The authors also proposed a new evaluation index for defect detection, alongside the Signal-to-Noise Ratio (SNR), to quantitatively assess and compare the effectiveness of various thermographic methods. The DAT method demonstrated superior performance over traditional approaches such as PCT and Manifold Learning Thermography (MLT), achieving a higher SNR and improved visibility of defects [15]. [PERSON] et al. conducted a study that integrated high-resolution RGB images and thermal imaging data using Multi-Spectral Dynamic Imaging (MSX) technology to provide more precise visual information. They constructed a dataset comprising 1000 thermal images of both defect-free and defective wind turbine blades, featuring various types of damage such as cracks, holes, and erosion. By leveraging MSX technology, the researchers incorporated the edges of visible light images into the thermal images, thereby enhancing image clarity. This approach improved the dataset quality, enabling the detection of fine defects that are difficult to distinguish using thermal imaging alone. Their study demonstrated, through deep learning-based ensemble learning with fused RGB and thermal images, that the ensemble model combining ViT and DenseNet161 achieved the highest performance [16]. [PERSON] et al. introduced a method that integrates RGB and infrared thermal images to enhance the accuracy of defect detection in wind turbine blades. To address the challenge of small object detection--where blades occupy only a small portion of the entire image during drone-based inspections--they employed a CNN-based LineNet network to predict two key coordinates of the blade. Using these coordinates, they proposed a regression crop data processing technique to extract images containing only the blade. Additionally, they introduced an RGB-IR feature fusion module, which dynamically adjusts the weighting coefficient during the fusion process of RGB and infrared thermal images. When the dataset obtained through regression crop and the RGB-IR feature fusion module was used to train YOLOv7, their study confirmed that the proposed method achieved higher accuracy than those reported in previous studies [17].