A Multi-scale Defect Detection Network for Wind Turbines Utilizing Margin Aware Features

The long-term operation of wind turbines (WTs) leads to multi-scale surface defects that critically compromise operational reliability. Drone-based defect detection offers a viable approach for real-time assessment of WT operational status. However, the current deployment of UAV-based detection systems struggles to simultaneously achieve both sensitivity and positioning accuracy for such multi-scale defects. To address this limitation, we propose a novel Defect Marginal-aware and Multi-scale Collaborative Attention Network (DMCA-Net). First, we propose a Defect Marginal Detail Transfer backbone (DMDT) to enhance edge information in shallow features, which can be fused with multi-scale features. Second, a Triple-layer Anchor Attention Feature Selection and Fusion Pyramid Network (TAAFSFPN) is introduced to optimize channel-space interactions, which can dynamically balance local details and global features, thereby improving defect localization accuracy. In addition, a Histogram-based Synergistic Attention Head encoder (HSAH) is designed to detect small object defects by co-optimizing frequency-domain split-box attention and cross-box attention to enhance the feature intensity of small object defects. Finally, the Normalized Wasserstein Distance–Inner Distance–IoU (NWD-InnerDIoU) loss is introduced to enhance model generalization and mitigate severe data imbalance, effectively reducing performance fluctuations resulting from interactions among multi-scale targets. Experimental results demonstrate that DMCA-Net achieves state-of-the-art performance with 83.1% mAP50, representing a 3.1% improvement over baseline, while maintaining real-time detection capability at 81.3 FPS on the WT defect dataset. Especially, it outperforms commonly used detection models in terms of detection performance.