Wind Turbine Blade Surface Defect Detection via UAV High-Resolution Images

Accurate surface defect detection is crucial for maintaining the structural integrity and performance of wind turbine blades (WTB), thereby optimizing their operational reliability. Due to limitations in drone flight and the small size of certain defects, existing algorithms struggle to detect minor target defects within the high-resolution images associated with WTB. This paper proposes KGP-YOLO, an innovative detection method for WTB surface defects in high-resolution images, which for the first time introduces keypoint localization and Bi-Level Routing Cross-Attention (BRCA). The network comprises a filter and a detector. We propose the Keypoint-aware Geometric-guided Preprocessing Network (KGP-Net) as the filter, select the advanced object detection model YOLOv11 as the detector, and integrate the BRCA module into YOLOv11.Initially, KGP-Net is employed to identify blade regions within high-resolution images and extract cropped images containing only the blade regions. This ensures the model focuses exclusively on the target blade areas, minimizing interference from irrelevant background regions. Subsequently, integrating the BRCA module into YOLOv11 introduces a dynamic sparse self-attention mechanism, significantly enhancing the network’s ability to capture deep-level features essential for effectively identifying complex WTB surface defects. The proposed method achieves a mean Average Precision (mAP) of 87.3% on the self-built SY-PLUS dataset, 92.4% on the public DTU dataset, and 88.5% on the public Blade30 dataset, while maintaining acceptable inference latency. Experimental results confirm that this method excels in detecting small object defects, even in high-resolution scenarios (e.g., 1080P), meeting the demands of practical applications.