凹版印刷质量控制自动化的合成数据生成框架
Synthetic data generation framework for quality control automation in gravure printing
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中文总结 AI 辅助
针对凹版印刷质量控制中人工检测的不足，提出合成数据生成框架，自动生成特定印刷缺陷图像及标注，用其训练模型在实际工业测试样本上达80.9%的mAP，提供零成本、快速部署的缺陷检测自动化方案。
中文摘要 AI 辅助
印刷中的质量控制，尤其是轮转凹版印刷，仍依赖缓慢、昂贵且主观的人工检查。自动表面缺陷检测对维持凹版印刷的高质量标准至关重要。深度学习模型为自动化带来希望，但训练如YOLO或视觉Transformer等强大的深度学习模型因现实工业缺陷图像极度稀缺而受阻。本文引入专为凹版印刷质量控制定制的新型合成数据生成框架。该框架自动生成特定印刷缺陷（褶皱、条纹、套准误差等）的高保真图像，并输出相应边界框和注释。为验证框架，生成7533张图像的合成数据集并用于训练最先进的目标检测模型RFDETR。实验结果表明，在我们的合成数据上训练的模型在实际工业测试样本上实现了80.9%的平均精度均值（mAP）。该框架为印刷生产线中的缺陷检测自动化提供了零成本、快速部署的解决方案，无需大量人工数据收集。
英文摘要
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.
发表机构
- univh2c（滨海大学）
机构由 AI 辅助整理，请以论文原文为准。
