## 1 Introduction  
Wind turbine blades are subject to defects caused by long-term use, variable mechanical loads, and external environmental factors [1]. This can lead to reduced aerodynamic efficiency and significant financial losses [2]. The early detection of such defects is essential for ensuring the reliable operation and long-term durability of wind turbines [3, 4, 5]. Infrared thermography is a technique used for detecting defects by observing thermodynamic changes in blades and is categorized into passive and active methods [6]. The passive method involves conducting inspections while the blade is naturally heated or cooled by solar radiation, making it dependent on weather and time conditions. In contrast, the active method uses artificial heat sources such as flash or halogen lamps but has the limitation of requiring proximity to the blade [7]. This study proposes a novel active inspection method utilizing a wind turbine blade embedded with a heating panel, as illustrated in Figure 1. The embedded heating panel, made of copper, is heated using the Joule heating method, transferring heat to the CFRP. This approach allows the inspectors to acquire thermographic data at their desired timing by utilizing drone technology. As described in the introduction, the active heating method using embedded heaters is a proprietary technique currently under development [8]. Despite the convenience of using digital thermographic images to identify damage existence, it remains challenging to conduct precise defect detection due to insufficient research on blade damage characteristics within the thermographic domain [9]. As a result, conventional infrared thermographic inspections have the limitation of relying on the experience and intuition of the inspector [10]. To address this limitation, this study leverages deep learning methodologies. Deep learning-based image processing techniques autonomously learn and analyze patterns in complex thermographic images, enabling precise defect detection without reliance on the inspector's experience and intuition [11]. Detecting defects using deep learning requires the acquisition of appropriate training datasets. However, in operational scenarios such as anomaly detection in wind turbine blades, obtaining abnormal datasets that contain defect-related information poses a significant challenge. Therefore, due to the difficulty in acquiring abnormal data, a class imbalance problem arises during the construction of training datasets for deep learning models, where the number of normal data samples significantly outweighs that of abnormal data samples [12; 13; 14]. This class imbalance can lead to a learning bias toward the majority class, causing the model to overly focus on patterns in normal data while diminishing its predictive performance on abnormal data patterns. Furthermore, it may distort the performance evaluation results of the trained deep learning model [14]. This study aims to address the class imbalance problem that arises during the construction of training datasets for deep learning models by generating training datasets using synthetic data as a substitute for abnormal data in real-world environments. However, even sophisticated synthetic data exhibits a domain gap compared to real-world data. To ensure that the performance of deep learning models trained using synthetic data does not degrade when applied to real-world data, it is essential to perform preprocessing techniques aimed at minimizing this domain gap. This study generated synthetic data through the heat transfer analysis of composites using the finite element method (FEM) and reduces the domain gap between synthetic and real data by employing style transfer techniques. To evaluate the defect detection performance of a deep learning model trained using the generated synthetic data on wind turbine blades, specimens simulating delamination were fabricated, and real thermographic data were obtained and utilized as validation data.