A Novel Inspection System For Variable Data Printing Using Deep Learning
This work addresses quality control in printing and potentially other domains like aerial imagery, but it is incremental as it builds on existing change detection methods.
The paper tackles the problem of inspecting variable data prints with low-cost scanners by introducing two new fusion methods for change detection, achieving an ultra-low false alarm rate of 0.005% and outperforming state-of-the-art baselines on aerial imagery change detection.
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.