Wind Turbine Blade Defect Detection Algorithm Based on Lightweight MES-YOLOv8n

Wind turbine blades are key wind turbine components, and owing to constraints in their operating environments and year-round high-speed rotation, wind turbines may suffer from many types of damage. However, traditional defect detection algorithms for wind turbine blades have limitations, such as low accuracy for detecting tiny defects and a high number of parameters, leading to model deployment difficulties. Therefore, this study proposes a wind turbine blade detection algorithm MES-YOLOv8n (M_C2f-ECA-SBiFPN-YOLOv8n) based on YOLOv8n design to address these challenges. In the proposed algorithm, a multi-kernel, grouped convolution-based C2f module (M_C2f) is employed in the backbone network to enhance the feature extraction capability of the backbone network. In addition, an efficient channel attention mechanism (ECA) is introduced in the backbone network, which can accurately capture channel-level information between network layers. Finally, the feature fusion network adopts a structure that combines a small target detection head and a bidirectional feature pyramid network (SBiFPN) to capture the multi-scale information of defective blades, enhance the feature fusion capability of the model, and reduce the leakage rate and model parameters of small target defects of wind turbine blades. Experimental results demonstrate that the proposed MES-YOLOv8n algorithm has a mean average precision (mAP@0.5) of 85.5%, which is 1.7% higher than YOLOv8n, and the number of parameters is reduced by 30%. Thus, the algorithm can accurately detect small target defects on wind turbine blades and is sufficiently lightweight for practical use cases.