A Novel Inspection System For Variable Data Printing Using Deep Learning

We present a novel approach for inspecting variable data prints (VDP) with an ultra-low false alarm rate (0.005%) and potential applicability to other real-world problems. The system is based on a comparison between two images: a reference image and an image captured by low-cost scanners. The compar...

Full description

Saved in:
Bibliographic Details
Published inarXiv.org
Main Authors Haik, Oren, Perry, Oded, Chen, Eli, Klammer, Peter
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 13.01.2020
Subjects
Change detection
Color imagery
Datasets
Deep learning
False alarms
Image detection
Inspection
Low cost
Machine learning
Scanners
Online AccessGet full text

Cover

More Information
Summary:We present a novel approach for inspecting variable data prints (VDP) with an ultra-low false alarm rate (0.005%) and potential applicability to other real-world problems. The system is based on a comparison between two images: a reference image and an image captured by low-cost scanners. The comparison task is challenging as low-cost imaging systems create artifacts that may erroneously be classified as true (genuine) defects. To address this challenge we introduce two new fusion methods, for change detection applications, which are both fast and efficient. The first is an early fusion method that combines the two input images into a single pseudo-color image. The second, called Change-Detection Single Shot Detector (CD-SSD) leverages the SSD by fusing features in the middle of the network. We demonstrate the effectiveness of the proposed deep learning-based approach with a large dataset from real-world printing scenarios. Finally, we evaluate our models on a different domain of aerial imagery change detection (AICD). Our best method clearly outperforms the state-of-the-art baseline on this dataset.
ISSN:2331-8422