基于深度学习的可变数据印刷检测系统
A Novel Inspection System For Variable Data Printing Using Deep Learning
摘要：
我们提出了一种新的方法来检查可变数据打印（VDP）具有超低的误报率（0.005%）和潜在的适用于其他现实世界的问题。该系统基于两个图像之间的比较：参考图像和低成本扫描仪捕获的图像。比较任务是具有挑战性的，因为低成本的成像系统会产生伪影，这些伪影可能被错误地归类为真实（真正）缺陷。为了解决这一挑战，我们引入了两种新的融合方法，变化检测应用程序，这是快速和有效的。第一种是早期融合方法，将两幅输入图像组合成单个伪彩色图像。第二种称为变化检测Single Shot检测器（CD-SSD），它通过融合网络中间的特征来利用SSD。我们证明了所提出的基于深度学习的方法的有效性与来自现实世界的打印场景的大数据集。最后，我们评估我们的模型在不同领域的航空影像变化检测（AICD）。我们最好的方法在这个数据集上明显优于最先进的基线。
英文摘要：
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.
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