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April 26, 2026Proceedings of the Institution of Mechanical Engineers Part A Journal of Power and Energy

Investigation of wind turbine blade defects using hybrid YOLOv8 and vision transformer

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Authors

ANA. Auster NesanJRJ. Bruce Ralphin Rose

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Overview

Randomized trial evaluates detection of wind turbine blade defects using UAVs, suggesting enhanced monitoring capabilities.

Key Points

  • The study aims to develop a hybrid model for detecting defects in wind turbine blades using UAV imagery.
  • Utilized a hybrid two-stage pipeline integrating YOLOv8 with CNN and Vision Transformer models.
  • Evaluated detection performance on five defect types using both balanced and imbalanced datasets.
  • Assessed model performance through quantitative criteria such as accuracy, mAP, F1-score, recall, and FPS.
  • The YOLOv8 + DeiT-Small configuration achieved superior performance with enhanced accuracy, improved mAP, and smooth convergence when trained on balanced dataset.
  • Balanced datasets significantly maximized the model's detection capabilities.
  • The pipeline effectively distinguishes between five types of blade defects with high accuracy.

Cite This Study

Nesan et al. (2026) studied this question.

synapsesocial.com/papers/69edac4f4a46254e215b4075https://doi.org/10.1177/09576509261447335
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Comparison study of advanced computer vision models for wind turbine blade defect detection2026
  2. 2AI‐Driven Deep Learning Framework for Detecting Subtle Surface Defects on Wind Turbine Blades2026
  3. 3RCS-YOLOv8: an improved YOLOv8 for wind turbine blade defect detection2026
  4. 4HDS-YOLO: A high-performance real-time framework for wind turbine blade defect detection via synergistic feature preservation2026 · 1 citations
  5. 5A comparative study of CNN and vision transformer architectures for fault detection in small wind turbine blades2026