论文标题
使用深度学习的新型检查系统，用于可变数据打印
A Novel Inspection System For Variable Data Printing Using Deep Learning
论文作者
论文摘要
我们提出了一种新的方法，用于检查具有超低误报率（0.005％）的可变数据打印（VDP），并潜在适用于其他现实世界中的问题。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，因为低成本成像系统会创建可能错误地将其归类为真（真实）缺陷的工件。为了应对这一挑战，我们介绍了两种新的融合方法，以供更快速和高效的变更检测应用程序。第一个是一种早期的融合方法，将两个输入图像结合到单个伪色图像中。第二个称为变更检测单射击检测器（CD-SSD）通过在网络中间融合特征来利用SSD。我们通过现实世界印刷方案的大型数据集证明了拟议的基于深度学习的方法的有效性。最后，我们在空中图像变化检测的不同领域（AICD）上评估了我们的模型。我们的最佳方法显然优于此数据集上的最新基线。
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.
