Detection of Fan Blade Defects Based on Improved YOLOv8n

As the core component of the wind turbine, blades are susceptible to deterioration caused by natural environmental factors. This can manifest as erosion, fissures, and gel coat detachment, which collectively impair the efficiency of wind power generation and the safe operation of the turbine. In order to address the issue of low detection accuracy for wind turbine blade defects in complex environments, an enhanced YOLOv8n wind turbine blade defect detection algorithm has been proposed. First, the Large Separable Kernel Attention (LSKA) attention mechanism is introduced into the Spatial Pyramid Pooling Fast (SPPF) module of the backbone network, thereby enhancing the network’s attention and improving the model’s feature extraction capability. Second, the neck employs a weighted bidirectional feature pyramid (Bi-FPN) structure and integrates a P2 small target detection layer, thus enhancing the model’s multi-scale feature fusion capability and improving its small target detection accuracy. Finally, the loss function of the original model is optimized using WIoU, thereby improving the model’s defect detection accuracy. The results of the defect detection experiments on wind turbine blade images demonstrate that the accuracy of the proposed method has been enhanced by 7.9%, the mAP 50 has been improved by 2.6%, and the number of parameters has been reduced by 23%.