Enhancing Wind Turbine Blade Damage Detection with YOLO-Wind
This study presents an enhanced YOLOv8n framework for wind turbine surface damage detection, achieving 83.9% mAP@0.5 on the DTU dataset—a 2.3% improvement over baseline models. The architecture replaces standard convolutions with depthwise separable convolutions (DWConv) to optimize computational efficiency without compromising detection accuracy. The C2f module is restructured by integrating MobileNetV2’s MBConv blocks with efficient channel [...]