工业视觉缺数据？这个框架用7533张合成图训RFDETR，在真实样本上mAP达80.9%，凹版印刷质量控制有救了。
该论文提出一种合成数据生成框架，专为凹版印刷质量控制设计，自动生成高保真印刷缺陷图像（如折痕、条纹、套印不准）及对应边界框和标注。框架生成7533张合成图像，用于训练目标检测模型RFDETR。在真实工业测试样本上，该模型达到80.9%的均值平均精度（mAP）。该方法无需手动采集真实缺陷数据，为零成本快速部署凹版印刷缺陷检测提供了方案。
Synthetic data generation framework for quality control automation in gravure printing
Quality control in printing, particularly in rotogravure printing, still depends on slow, costly, and subjective manual inspection. Automated surface defect detection is critical for maintaining high-quality standards in rotogravure printing. Deep learning models give prospects for automation. However, training robust deep learning models, such as YOLO or Vision Transformers, is heavily hindered by the extreme scarcity of real-world industrial defects images. To overcome this limitation, this paper introduces a novel synthetic data generation framework tailored for rotogravure printing quality control. The proposed pipeline automatically generates high-fidelity images of specific printing defects (creases, streaks, misregistration, etc.) and outputs corresponding bounding boxes and annotations. To validate the framework, a synthetic dataset of 7533 images was generated and used to train the state-of-the-art object-detection model RFDETR. Experimental results demonstrate that the model trained on our synthetic data achieves a Mean Average Precision (mAP) of 80.9\% on real industrial testing samples. This framework provides a zero-cost, rapid-deployment solution for automating defect inspection in printing lines without requiring massive manual data collection.