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.