In order to solve the problem that traditional detection methods cannot detect surface defects of wind turbines sufficiently, the paper proposes a surface defect detection algorithm for wind turbines based on SAFPN-YOLO. Firstly, in order to solve the problem of difficulty in multi-scale object detection, the SAFPN network based on the idea of asymptotic fusion is used to replace the classical feature pyramid fusion network, so as to reduce the semantic gap of information during feature fusion. Secondly, in order to solve the problem of redundancy of background information, the original SCDown module was replaced by the NAMBlock module embedded in the deep embedding of the algorithm backbone network, so that the model could extract features in a broader field of view while retaining the key information of local features. Finally, in order to solve the problem that textured defects are difficult to detect and locate, an attention mechanism is proposed to strengthen the spatial feature interaction ability and feature expression ability, and further improve the detection performance of the model. The experimental results show that the mAP50 based on SAFPN-YOLO fan surface defect detection algorithm reaches 82.4%, which is 3.3% higher than that of the baseline model, and can achieve more accurate defect detection on the surface of the fan.