论文标题
通过语义分段打印缺陷映射
Print Defect Mapping with Semantic Segmentation
论文作者
论文摘要
高效的自动打印缺陷映射对印刷行业很有价值，因为此类缺陷直接影响了以客户感知的打印机质量，并且手动映射它们具有成本感知。常规方法由复杂和手工制作的功能工程技术组成，通常仅针对一种类型的缺陷。在本文中，我们提出了第一个端到端框架，用于在像素级别绘制打印缺陷，采用基于语义分割的方法。我们的框架使用卷积神经网络，特别是DeepLab-V3+，并在识别印刷图像中的缺陷方面取得了令人鼓舞的结果。我们通过模拟两种类型的印刷缺陷以及具有图像处理和计算机图形技术的印刷效果来使用合成训练数据。与常规方法相比，我们的框架具有多功能性，允许两种推理策略，一个是实时的，提供更粗糙的结果，另一个则重点是通过更细粒度的检测到离线处理。我们的模型在真实印刷图像的数据集上进行了评估。
Efficient automated print defect mapping is valuable to the printing industry since such defects directly influence customer-perceived printer quality and manually mapping them is cost-ineffective. Conventional methods consist of complicated and hand-crafted feature engineering techniques, usually targeting only one type of defect. In this paper, we propose the first end-to-end framework to map print defects at pixel level, adopting an approach based on semantic segmentation. Our framework uses Convolutional Neural Networks, specifically DeepLab-v3+, and achieves promising results in the identification of defects in printed images. We use synthetic training data by simulating two types of print defects and a print-scan effect with image processing and computer graphic techniques. Compared with conventional methods, our framework is versatile, allowing two inference strategies, one being near real-time and providing coarser results, and the other focusing on offline processing with more fine-grained detection. Our model is evaluated on a dataset of real printed images.
