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

TL;DR

This paper introduces a new method for detecting wind turbine blade defects using advanced AI techniques, achieving high accuracy and real-time performance.

Contribution

The novel framework combines DINOv2 and SCN for improved defect recognition in wind turbine blades.

Findings

The method achieved 97.8% classification accuracy for blade defects.

The average inference time was 19.65 ms per image, meeting real-time requirements.

The framework outperforms traditional methods in scalability and efficiency.

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.

Figures