Open Access

ARTICLE

Dual-Attention Multi-Path Deep Learning Framework for Automated Wind Turbine Blade Fault Detection Using UAV Imagery

Mubarak Alanazi^1,*, Junaid Rashid²

1 Electrical Engineering Department, Jubail Industrial College, Royal Commission for Jubail & Yanbu, Jubail Industrial City, Saudi Arabia
2 Department of Artificial Intelligence and Data Science, Sejong University, Seoul, Republic of Korea

* Corresponding Author: Mubarak Alanazi. Email:

(This article belongs to the Special Issue: Advances in Deep Learning and Computer Vision for Intelligent Systems: Methods, Applications, and Future Directions)

Computer Modeling in Engineering & Sciences 2026, 146(2), 17 https://doi.org/10.32604/cmes.2026.077956

Received 20 December 2025; Accepted 27 January 2026; Issue published 26 February 2026

Abstract

Wind turbine blade defect detection faces persistent challenges in separating small, low-contrast surface faults from complex backgrounds while maintaining reliability under variable illumination and viewpoints. Conventional image-processing pipelines struggle with scalability and robustness, and recent deep learning methods remain sensitive to class imbalance and acquisition variability. This paper introduces TurbineBladeDetNet, a convolutional architecture combining dual-attention mechanisms with multi-path feature extraction for detecting five distinct blade fault types. Our approach employs both channel-wise and spatial attention modules alongside an Albumentations-driven augmentation strategy to handle dataset imbalance and capture condition variability. The model achieves 97.14% accuracy, 98.65% precision, and 98.68% recall, yielding a 98.66% F1-score with 0.0110 s inference time. Class-specific analysis shows uniformly high sensitivity and specificity; lightning damage reaches 99.80% for sensitivity, precision, and F1-score, and crack achieves perfect precision and specificity with a 98.94% F1-score. Comparative evaluation against recent wind-turbine inspection approaches indicates higher performance in both accuracy and F1-score. The resulting balance of sensitivity and specificity limits both missed defects and false alarms, supporting reliable deployment in routine unmanned aerial vehicle (UAV) inspection.

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Keywords

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