As an important component of wind turbines, blades are easily affected by the natural environment, leading to damage such as erosion, cracks, and detachment of rubber coats, thereby affecting the efficiency of wind power generation and the safe operation of the unit. A modified YOLOv8 fan blade defect detection algorithm is proposed to address the issue of low accuracy in detecting blade defects in complex environments. The single module SPPF in the backbone feature extraction network is integrated into the LSKA attention mechanism to enhance the network's attention to important features and improve the performance of the model; Secondly, the Neck section adopts a weighted bidirectional feature pyramid Bi-FPN structure, and use FasterBlock to improve the C2f module. The Bi-YOLOv8 fast lightweight network structure is proposed to enhance the multi-scale feature fusion ability of the model and improve the accuracy of small target detection; Finally, the Inner-iou method, which assists in calculating the loss of bounding boxes, is used to optimize the loss function and improve the accuracy and generalization ability of the model's defect detection. Through the experiment of defect detection on the image of fan blades, the results show that the proposed method improves the accuracy rate of defect detection by 7.3%, mAP50 by 3.3%, and reduces the number of parameters by 27%.