WTB-YOLO:Wind Turbine Blade Defect Detection with Scale Sensitivity and Cascade Structure

The wind turbine blade represents a pivotal element within the context of wind power generation systems, with its performance and reliability exerting a direct influence on the overall efficiency and safety of the system. The identification of defects in wind turbine blades is of considerable economic and safety significance. The existing YOLOv8 target detector is still inadequate in terms of loss function in the process of detecting minor defects in wind turbine blades, which affects the learning efficiency and model performance. Therefore, a new loss function is proposed, which is realised by combining scale sensitivity and cascade structure. The proposed method, implemented in the YOLOv8 detector, markedly enhances both the accuracy of detection and the computational efficiency. Moreover, wind turbine blade defect detection is typically implemented in edge devices with constrained performance. To address this issue, a lightweight convolution network is introduced, reducing the number of parameters by 60% to 1.12M, thereby achieving a balance between performance and efficiency. The experimental results on the DTU and the datasets constructed by our own research team demonstrate improvements in detection accuracy by 2.28% and 1.8%, respectively.