LarGAN: A Label Auto-Rescaling Generation Adversarial Network for Rare Surface Defects

Insufficient defect data significantly limits detection accuracy in continuous casting slab production. This limitation arises from the data collection in fast-paced production environments. To address this issue, we propose LarGAN, a data augmentation approach that synthesizes similar and high-quality defect data from a single image. We utilize a progressive GAN framework to ensure a smooth and stable generation process, starting from low-resolution image synthesis and gradually increasing the network depth. We designed a Label Auto-Rescaling strategy to better adapt to defect data with annotation, enhancing both the quality and morphological diversity of the synthesized defects. To validate the generation results, we evaluate not only standard metrics, such as FID, SSIM, and LPIPS, but also performance, through the downstream detection model YOLOv8. Our experimental results demonstrate that the LarGAN model surpasses other single-image generation models in terms of image quality and diversity. Furthermore, the experiments reveal that the data generated by LarGAN effectively enhances the feature space of the original dataset, thereby improving the accuracy and generalization performance of the detection model.