Enhanced Prediction Performance of Internal Defect Detection in Wind Turbine Blades on Thermography Using Deep Learning Models with Preprocessed Synthetic Data

This study proposes a method for detecting internal defects in wind turbine blades using deep learning, eliminating the reliance on inspectors’ experiments. To address the class imbalance problem inherent in defect detection environments, synthetic thermographic datasets were generated using a synthetic data generation technique. To minimize the domain gap between synthetic and real thermographic data, preprocessing with a transformation module was employed, enhancing the similarity between datasets. ResNet-50, DenseNet-121, and Vision Transformer (ViT) models were trained on the synthetic dataset, and their defect detection performance was evaluated on real thermographic data. The results validated the effectiveness of the transformation module in improving the similarity between synthetic and real data, particularly enhancing precision and recall.