Wind Turbine Blade Defect Recognition Method Based on Large-Vision-Model Transfer Learning

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Abstract
Timely and accurate detection of wind turbine blade surface defects is crucial for ensuring operational safety and improving maintenance efficiency with respect to large-scale wind farms. However, existing methods often suffer from poor generalization, background interference, and inadequate real-time performance. To overcome these limitations, we developed an end-to-end defect recognition framework, structured as a three-stage process: blade localization using YOLOv5, robust feature extraction via the large vision model DINOv2, and defect classification using a Stochastic Configuration Network (SCN). Unlike conventional CNN-based approaches, the use of DINOv2 significantly improves the capability for representation under complex textures. The experimental results reveal that the proposed method achieved a classification accuracy of 97.8% and an average inference time of 19.65 ms per image, satisfying real-time requirements. Compared to traditional methods, this framework provides a more scalable, accurate, and efficient solution for the intelligent inspection and maintenance of wind turbine blades.
Funding Information
  • Natural Science Foundation of Liaoning Province, China (2023JH2/101700261, 2024-MS-217, LJ222411632035, LJ212411632075, 24-213-3-29)
  • Department of Education of Liaoning Province, China (2023JH2/101700261, 2024-MS-217, LJ222411632035, LJ212411632075, 24-213-3-29)
  • Shenyang Science and Technology Plan Project (2023JH2/101700261, 2024-MS-217, LJ222411632035, LJ212411632075, 24-213-3-29)

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