YOLOv13-ADR: An Adaptive Deformable Convolution and Neighborhood-Aware Recombination Network for Wind Turbine Blade Defect Detection

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Accurate detection of surface defects in wind turbine blades is critical for condition monitoring and preventive maintenance of wind energy systems. Defects such as cracks, burns, deformation, and peeling are characterized by small dimensions, irregular morphologies, and low contrast, limiting the effectiveness of conventional feature extraction methods. Although YOLOv13 enhances high-order feature correlation and information flow, its fixed-grid spatial sampling and content-agnostic upsampling operations remain limited in adapting to irregular defect geometries and preserving fine-grained boundary information. This study proposes YOLOv13-ADR, an enhanced detection framework integrating Adaptive Deformable Convolution (ADConv) and a Nearest Neighbor Content Perception Recombination (NNCPR) module. ADConv applies a geometry-driven kernel permutation strategy to strengthen multi-scale feature representation, while NNCPR improves neighborhood-aware perception for modeling geometric deformations. A Focus-IoU loss function incorporating an anchor-quality perception mechanism is introduced to accelerate training convergence and improve bounding box regression precision. Additional optimizations include modifications to the DS-C3k2 module and upsampling strategy. Experiments on a wind turbine blade defect dataset demonstrate that YOLOv13-ADR achieves a 7.26-percentage-point improvement in mean average precision over YOLOv8n, with enhanced small-defect recognition and reduced localization errors, demonstrating improved detection precision and localization performance relevant to early fault detection and structural health monitoring of wind turbine blades.