Wind-YOLO: A Lightweight Detector for Wind Turbine Damage

Wind turbine blades are prone to multiscale and weak-feature damage in complex natural environments. Accurate and efficient detection is crucial for ensuring the safe operation of wind turbine units. However, existing models struggle to balance detection precision, robustness, and lightweight deployment requirements. In this paper, we propose a lightweight model, Wind-YOLO, for wind turbine blade defect detection based on YOLOv11, with three core innovations: (1) We design a DynamicC3k2 that adaptively adjusts the convolutional receptive field for feature extraction, enhancing fine-grained feature capture of micro-cracks and weak-texture defects. (2) We construct a Cross-Stage Partial with Focused Linear Attention (C2FLA) that precisely focuses on defect regions via a linear attention mechanism, effectively mitigating complex background and noise interference. (3) We propose a Spatially Guided Gated Feature Pyramid Network (SGG-FPN) that optimizes multiscale feature transmission and aggregation through a gated fusion mechanism, improving adaptability to cross-scale defects from millimeter-level cracks to meter-level spalling. Extensive experiments on a dedicated wind turbine defect dataset show that Wind-YOLO achieves an mAP@0.5 of 80.9% and an mAP@0.5:0.95 of 37.1%, achieving an increase of 3.9 percentage points and 2.4 percentage points, respectively, compared with the baseline YOLOv11. Meanwhile, the model has only 2.34 million parameters (2.34 M) and a computational complexity of 6.0 GFLOPs. It delivers dual improvements in precision and lightweight performance, with superior environmental adaptability for real-time wind turbine inspection.