Rail surface defect detection via sample generation and style transfer under extremely limited sample conditions

Surface defects are a major cause of railway failures and pose serious safety risks, making accurate defect detection essential. However, existing methods often exhibit limited performance due to the scarcity of defect samples and the complexity of operating environments. To address these challenges, this paper proposes a sample self-generation-based framework for rail surface defect detection in extremely data-scarce scenarios. A multi-mode defect generation model is introduced, enabling effective data augmentation and style transfer using only one or two real samples. By decoupling the generation process into a Feature Learning Network with a sub-discriminator architecture and a Sample Generation Network, the proposed method achieves high sample diversity and quality with low computational cost. The generated samples are used to train YOLO-based detectors. Experiments show that the proposed approach improves mAP@0.5 by over 8 percentage points on YOLOv9, outperforming models trained with twice the amount of real data.