MorphControl: Nuclear Fuel Rod Defect Image Generation via Morphology Prior Adaptation and Quality-Feedback Prior Regulation

Surface defect datasets for nuclear fuel rods are typically scarce and class-imbalanced, while defects within the same category vary considerably in shape, scale, and local texture. These characteristics make it difficult for generative models to simultaneously preserve class consistency, intra-class diversity, and the structural fidelity of industrial backgrounds. Existing methods often rely on instance-level conditioning and therefore do not explicitly capture morphological patterns shared across samples of the same class. Moreover, fixed conditioning strengths cannot account for differences among defect categories, backgrounds, and stochastic sampling states, potentially leading to weak defect expression, excessive enhancement, or local artifacts. To address these limitations, we propose MorphControl, an industrial defect generation framework that combines defect-morphology-prior-driven multi-condition adaptation with quality-feedback prior regulation. During training, MorphControl attenuates defect regions in reference images to construct defect-suppressed background conditions while keeping the pretrained diffusion backbone frozen. It jointly learns class-level defect morphology priors from samples of the same category. Combining these priors with instance-specific industrial backgrounds, geometric layouts, and textual conditions enables class-shared morphological knowledge to be recombined under diverse generation conditions. A region-weighted diffusion objective and a background–morphology decorrelation constraint further strengthen fine-grained defect learning and reduce information coupling between conditions. During inference, MorphControl adaptively regulates the injection strength of the morphology prior according to regional feature consistency and artifact risk, and then selects the highest-quality candidate that satisfies the quality constraints. Experiments show that MorphControl outperforms existing methods in generation fidelity and intra-class diversity, achieving lower KID and higher IC-LPIPS. Using the generated samples for data augmentation further improves downstream defect detection in both mAP50 and mAP50:95, with larger gains for underrepresented defect categories. These results show that class-level defect morphology priors and inference-time quality-feedback regulation improve both synthesis quality and the utility of generated data for downstream defect detection.