There is a problem of motion blur caused by force majeure factors in the unmanned aerial vehicle aerial photography of insulator images. Thus a blur insulator image restoration method based on improved Wasserstein generative adversarial networks (WGAN) with multi structure fusion is proposed. An improved generative adversarial network based on Wasserstein distance is proposed to solve the problem of blur repair, and a gradient penalty is introduced into the loss function to optimize the training process. This ensures the stability of model training and improves the quality of image restoration. A dilated convolution residual network and convolutional attention mechanism are integrated into the generating network to strengthen the learning of effective features of images by the neural network. The results of experiment show that both the peak signal-to-noise ratio and the structural similarity index measure of the proposed method are higher than with other algorithms. The comparison of images generated by different algorithms proves that this method can effectively extract the detailed features and improve the quality of deblur image restoration. YOLOv5s is used for detection experiments. These demonstrate that the method presented enhances the accuracy of object detection.