@article{Alanazi2026, 
author = {Mubarak Alanazi and Junaid Rashid},
title = {Dual-Attention Multi-Path Deep Learning Framework for Automated Wind Turbine Blade Fault Detection Using UAV Imagery},
year = {2026},
journal = {Computer Modeling in Engineering & Sciences},
volume = {146},
number = {2},
pages = {17},
keywords = {Wind energy, aerial imagery, surface condition monitoring, wind turbine blades, surface defect detection, attention mechanism, computer vision, deep learning, artificial intelligence},
url = {https://www.sciopen.com/article/10.32604/cmes.2026.077956},
doi = {10.32604/cmes.2026.077956},
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.}
}