To address the issues of limited sample size and imbalanced categories in existing rural road image datasets, a data augmentation method based on an improved StyleGAN is proposed. This approach introduces a decoupled mapping network into the original StyleGAN framework to reduce the coupling degree of the W-space latent code. By integrating the advantages of convolution and Transformer, this study designs a convolution-coupled transfer block (CCTB). The core cross-window self-attention mechanism within this module enhances the network’s ability to capture complex context and spatial layouts. These two improvements significantly boost network performance. Ablation experiments comparing the original and improved StyleGAN networks show that the IS index increases from 42.38 to 77.31, and the FID value decreases from 25.09 to 12.42, demonstrating a substantial improvement in data generation quality and authenticity. To verify the impact of data augmentation on model performance, two classic and mainstream object detection algorithms are used for testing. Performance differences between the original and augmented datasets are compared, further confirming the effectiveness of the improved methods.