# 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.

## Key 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.