## 5 Conclusions and Future Directions  
This study aimed to address the class imbalance problem that arises in anomaly detection environments when applying deep learning for defect detection in wind turbine blades. To achieve this, synthetic thermographic datasets were generated through heat transfer analysis using the finite element method. To minimize the domain gap between the generated synthetic datasets and the real data, a transformation module was proposed. The performance of deep learning models trained on synthetic thermographic datasets with the proposed transformation module was evaluated through a comparative analysis in experimental settings, both before and after applying the module.  
By comparing the performance of models trained using synthetic thermographic datasets without the transformation module to those trained using datasets with the module applied, it was confirmed that the transformation module effectively reduces the gap between synthetic and real data, thereby improving defect detection performance. Notably, it proved effective in enhancing precision and recall for defect detection. This demonstrates that the transformation module contributed to aligning the physical characteristics of synthetic thermographic images more closely with those of real data.  
Among the selected models, ViT recorded the highest accuracy (51.62%), specificity (98.91%), and precision (94.57%), showcasing its overall superior performance. This outcome underscores ViT's capability to effectively capture the global relationships between spatially distributed temperatures in thermographic data through its self-attention mechanism.  
However, all models, including ViT, demonstrated limitations in specific performance metrics for defect detection. In particular, recall remained low across all models, highlighting the inability to completely eliminate false positives and false negatives in defect detection. Moreover, despite ViT's high specificity and precision, its performance was insufficient to meet the requirements for practical application in defect detection.  
It was also observed that the physical characteristics of thermal equilibrium present in the thermographic dataset can negatively impact model performance. This issue arises from the temporal properties inherent in thermographic data and should be considered to provide inspectors with reliable defect detection results. Further research is necessary to enhance the overall defect detection performance by taking into account the temporal characteristics of thermographic data.  
This study demonstrated that defect detection performance can be enhanced through the utilization of synthetic data and preprocessing techniques, thereby establishing a foundation for the development of more advanced defect detection technologies to ensure the stable operation and maintenance of wind turbine blades. Future research should aim to further enhance overall defect detection performance, with particular attention to improving recall for wind turbine blades.  
Conceptualization: H.C. and J.N.; methodology, analysis: H.C. and J.N.; software, investigation: H.C.; data curation, validation, and visualization: H.C.; writing--originaldraft preparation: H.C.; writing--review and editing: J.N. and D.O.; project administration: J.N. All authors have read and agreed to the published version of the manuscript.  
This research was supported by \"regional Innovation Strategy (RIS)\" through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (MOE) (2023 RIS-008) and partly supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) grand funded by the Korean government (MOTIE) (2022400000220, Jeonbuk Regional Energy Cluster Training of human resources).  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.