Overview
Field: Computer Vision Authors: Korota Arsene Coulibaly, Mohamed Hamlich, Khalid Hmali Published: 2026-07-24 arXiv: 2507.19313
Summary
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 offer prospects for automation, but training robust models such as YOLO or Vision Transformers is heavily hindered by the extreme scarcity of real-world industrial defect images.
To overcome this limitation, the 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.
Key Results
- A synthetic dataset of 7,533 images was generated to validate the framework.
- The state-of-the-art object detection model RF-DETR was trained on this synthetic data.
- The model achieved 80.9% mAP on real industrial test samples, demonstrating strong sim-to-real transfer.
Significance
The framework provides a zero-cost, rapid-deployment solution for automated defect detection on printing lines, without requiring extensive manual data collection.
--- *Auto-collected on 2026-07-25*