Aiming at the problem of insufficient precision and poor generalization in the traditional way of wind turbine surface defect detection, an improved YOLOv5s wind turbine surface defect detection model is proposed. In terms of network structure, an improved MobileNetv3 network is introduced into the backbone feature extraction network to coordinate and balance the lightweight and accuracy relationship of the model. Secondly, the BiFPN fusion method is adopted to enhance the multi-scale adaptability of the neural network and improve the fusion speed and efficiency. Finally, for the lightweight adaptive adjustment of feature weights, the ECAnet channel attention mechanism is used to further improve the feature extraction ability of the neural network. In terms of loss function, the loss function of bounding box regression is modified to αIoU Loss, which further improves the accuracy of bbox regression. The experimental results show that the improved algorithm based on YOLOv5s can quickly and accurately identify the defect targets on the surface of the wind turbine in complex environments, and can meet the practical application requirements of real-time target detection.