FCD-YOLO: An Improved Object Detection Algorithm for Wind Turbine Blade Surface Damage Detection      

Among routine wind turbine maintenance activities, the timely and accurate detection of blade surface damage is crucial for maintaining structural integrity and ensuring operational safety. In practical on-site wind farm inspection workflows, aerial images captured via unmanned aerial vehicles (UAVs) frequently suffer from miniature, geometrically irregular defect targets, highly cluttered natural backgrounds, fluctuating lighting intensity, heavy shadow occlusion, and persistent surface dirt contamination, which creates an intractable performance conflict that conventional detection frameworks struggle to balance high localization precision and low inference latency simultaneously. To address these challenges, this study proposes an improved detection model named FCD-YOLO. Within this architecture, lightweight FasterNet Blocks are fully embedded into both the backbone feature extraction pipeline and the neck multi-scale fusion branch to reduce computational redundancy and improve inference efficiency. In parallel, a Channel Prior Convolutional Attention (CPCA) module is embedded to selectively enhance damage-sensitive features by jointly considering channel-wise and spatial information and suppressing irrelevant background feature activations. Moreover, an auxiliary detection branch (DetectAux) is incorporated to introduce supplementary supervisory cues during training, which facilitates more robust learning of features across different scales and alleviates missed detection risks for tiny, low-contrast blade defects. Comprehensive experimental validation indicates that the proposed model achieves mAP@0.5 and mAP@0.5:0.95 scores of 80.98% and 57.77%, respectively, surpassing the corresponding results of the YOLO11s baseline (79.22% and 56.08%). Side-by-side benchmark tests against multiple state-of-the-art mainstream detectors further validate the comprehensive competitive superiority of the proposed FCD-YOLO framework. Overall, this work provides a promising approach for workstation-based offline or near-real-time wind turbine blade damage inspection with remarkable lightweight characteristics and robust anti-interference capability for complex field environments.