🤖 AI Summary
This study addresses the challenge of deploying automated quality inspection in gravure printing, where real defect samples are extremely scarce. To overcome this limitation, the authors propose the first high-fidelity synthetic data generation framework tailored to this domain. Leveraging procedural image synthesis techniques, the framework automatically generates realistic instances of common defects—such as wrinkles, streaks, and misregistration—along with precise pixel-level annotations, enabling zero-shot deployment without any reliance on real defective images. A detection model, RFDETR, trained exclusively on 7,533 synthetically generated images achieves a mean average precision (mAP) of 80.9% on a real-world industrial test set, demonstrating the effectiveness and practical utility of the proposed approach.
📝 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.