Synthetic data generation framework for quality control automation in gravure printing
2026-07-23 • Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionArtificial IntelligenceMachine Learning
AI summaryⓘ
The authors address the problem of slow and expensive manual checks for printing defects in rotogravure printing. They created a new way to make fake images showing common printing mistakes, like creases and streaks, to help train computer models. Using these fake images, they trained a deep learning model that could recognize defects well on real printer samples. Their approach allows fast and cheap automation without needing lots of real defect photos.
rotogravure printingsurface defect detectiondeep learningsynthetic data generationobject detectionRFDETRMean Average Precisionimage annotationprinting defects
Authors
Korota Arsène Coulibaly, Mohamed Hamlich, Khalid Hmali, Andrea Trombin
Abstract
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.