用合成数据解决印刷缺陷检测数据少难题，让自动化质检更快更便宜。
Synthetic data generation framework for quality control automation in gravure printing
- 自动生成高保真印刷缺陷图像及标注，无需真实样本采集。
- 训练模型在真实工业数据上达到80.9%的平均精度（mAP）。
- 适合工厂快速部署，避免昂贵的人工标注和数据收集。
印刷质量控制，尤其是凹版印刷，仍依赖缓慢、昂贵且主观的人工检查。自动化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提供了可能，但训练如YOLO或视觉变压器等鲁棒模型严重受限于真实工业缺陷图像的极度稀缺。为此，本文提出一种专为凹版印刷质量控制设计的新型合成数据生成框架。该流程可自动生成特定印刷缺陷（如折痕、条纹、错位等）的高保真图像，并输出对应边界框与标注。为验证框架有效性，生成了包含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，基于合成数据训练的模型在真实工业测试样本上达到80.9%的平均精度（mAP）。该框架提供了一种零成本、快速部署的解决方案，无需大量人工数据收集即可实现印刷产线的缺陷检测自动化。
原文摘要 · Abstract (English)
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.
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