Aiming at the problems of complex background environment and insufficient detection accuracy of small target defects in surface defect detection of wind turbines, an efficient surface defect detection method for wind turbines is proposed. Firstly, a backbone network with feature extraction and fusion capabilities is constructed, and an improved channel attention is introduced in the residual part to help the network better extract feature information. Secondly, a new generation of convolution deformation module is used for output, so that the model can better capture the correlation between space and time in the input data, simplify the model and improve the detection speed. Finally, an efficient spatial-depth information conversion module is introduced in the down-sampling part of the model to reduce the spatial dimension in the input feature map to the channel dimension, retain the salient features while reducing the loss of fine-grained information, and further improve the ability of the model to detect small targets.The experimental results show that compared with the YOLOv7 network, the accuracy of the proposed network is improved by 3.5%, the recall rate is improved by 2.3%, and the average accuracy is improved by 3.1% when the intersection over union is 0.5.In the data set 2 with better image quality, the accuracy rate reaches 96%, the recall rate reaches 94%, and the average accuracy reaches 96.7% when IoU is 0.5. The proposed model has obvious advantages in solving the problem of false detection and missed detection, and has faster detection speed. It is more suitable for application in the actual detection environment and has good engineering application prospects.