Real-time non-intrusive monitoring of wind turbines, blades, and defect surfaces poses a set of complex challenges related to accuracy, safety, cost, and computational efficiency. This work introduces an enhanced deep learning-based framework for real-time detection of wind turbine blade defects. The WindDefNet is introduced, which features the Inception-ResNet modules, Visual Transformer (ViT), and multi-scale attention mechanisms. WindDefNet utilizes modified cross-convolutional blocks, including the powerful Inception-ResNet hybrid, to capture both fine-grained and high-level features from input images. A multi-scale attention module is added to focus on important regions in the image, improving detection accuracy, especially in challenging areas of the wind turbine blades. We employ pertaining to Inception-ResNet and ViT patch embedding architectures to achieve superior performance in defect classification. WindDefNet’s capability to capture and integrate multi-scale feature representations enhances its effectiveness for robust wind turbine condition monitoring, thereby reducing operational downtime and minimizing maintenance costs. Our model WindDefNet integrates a novel advanced attention mechanism, with custom-pretrained Inception-ResNet combining self-attention with a Visual Transformer encoder, to enhance feature extraction and improve model accuracy. The proposed method demonstrates significant improvements in classification performance, as evidenced by the evaluation metrics attain precision, recall, and F1-scores of 0.88, 1.00, and 0.93 for the damage, 1.00, 0.71, and 0.83 for the edge, and 1.00, 1.00, and 1.00 for both the erosion and normal surfaces. The macro-average and weighted-average F1 scores stand at 0.94, highlighting the robustness of our approach. These results underscore the potential of the proposed model for defect detection in industrial applications.