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...
| Authors | Korota Arsène Coulibaly, Mohamed Hamlich, Khalid Hmali et al. |
|---|---|
| Primary category | cs.CV |
| Published | 23 July 2026 |
| Last updated at source | 23 July 2026 |
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