A Continual Learning Framework for Adaptive Defect Classification and Inspection

Recent development of advanced sensing and high computing technologies has enabled the wide adoption of machine vision to automatically inspect products' dimensional quality for efficient process control and reducing the manual inspection cost. The process control procedure requires effective data analysis methods to provide reliable inspection results. In this paper, we consider a high-volume manufacturing system that uses machine vision at the quality inspection station for automatic classification of product defects. Here classification implies both; identifying a defect and classifying its corresponding type. As a motivating example, we consider the scenario where batches of three-dimensional (3D) point cloud data are independently collected from a manufacturing process. The 3D point cloud data is obtained by measuring the 3D location of points on the product surface using a 3D scanner. The location measurements can then be used for fast classification of surface defects, and thus provide timely feedback for process control. Figure 1 (right) shows some exemplar surface defects on a wood product and the corresponding 3D point cloud measurements. The 3D point cloud measurements have a set of defining characteristics that should be considered in the development of defect classification techniques.