Automated detection of surface defects in wind turbine blades is essential for cost-effective and reliable maintenance in modern energy infrastructure. This study presents a comprehensive evaluation of recent single-stage (YOLOv8, YOLOv9, YOLOv10, YOLOv11) and two-stage (Faster R-CNN) deep learning-based object detection models for wind turbine blade inspection. To address class imbalance, StyleGAN2-ADA augmentation was applied to a real-world dataset, and detection accuracy, class-wise performance, inference speed, and model size were assessed using stratified cross-validation and an independent holdout set. Results show that all YOLO models consistently outperform Faster R-CNN in mean Average Precision (mAP@0.5), with YOLOv11 achieving the highest overall score of 0.969 on the holdout test set. The integration of synthetic data led to substantial performance gains for the minority classes and reduced variance across folds. In addition to superior accuracy, YOLO models demonstrate faster inference (<11 ms per image) and compact model sizes (48–84 MB), highlighting their suitability for real-time and edge-based industrial deployments. These findings establish the technical and practical benefits of combining advanced YOLO models with data augmentation strategies for automated wind turbine blade defect detection.
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