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This research aims to develop an effective method for detecting small blade faults in wind turbines using deep learning techniques.

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

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This research aims to develop an effective method for detecting small blade faults in wind turbines using deep learning techniques.
Introduced TurbineBladeDetNet, a convolutional architecture with dual-attention mechanisms.
Employed multi-path feature extraction to improve fault detection.
Utilized Albumentations for data augmentation to address class imbalance.
Evaluated model performance based on accuracy, precision, recall, and F1-score.
Achieved 97.14% accuracy and high precision and recall values above 98%.
Notably high sensitivity of 99.80% for lightning damage and 98.94% F1-score for crack detection.
Demonstrated superior performance compared to conventional wind-turbine inspection methods.

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Novel dual-attention framework detects wind turbine blade faults in UAV imagery, suggesting enhanced reliability.

Alanazi et al. (2026) studied this question.

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