论文标题
一个持续的自适应缺陷分类和检查的学习框架
A Continual Learning Framework for Adaptive Defect Classification and Inspection
论文作者
论文摘要
基于机器的缺陷分类技术已被广泛用于制造过程中的自动质量检查。本文介绍了一个通用框架，用于通过有效检查未标记的样本对大量数据批次的缺陷进行分类。该概念是构建一个检测器来识别新的缺陷类型，将其发送到检查站进行标记，并以有效的方式动态更新分类器，以减少先前观察到的批次的数据样本施加的存储和计算需求。对图像分类进行的仿真研究和通过3D点云进行表面缺陷检测的案例研究，以证明该方法的有效性。
Machine-vision-based defect classification techniques have been widely adopted for automatic quality inspection in manufacturing processes. This article describes a general framework for classifying defects from high volume data batches with efficient inspection of unlabelled samples. The concept is to construct a detector to identify new defect types, send them to the inspection station for labelling, and dynamically update the classifier in an efficient manner that reduces both storage and computational needs imposed by data samples of previously observed batches. Both a simulation study on image classification and a case study on surface defect detection via 3D point clouds are performed to demonstrate the effectiveness of the proposed method.
