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Activity Number: 72 - SPEED: Statistical Learning and Data Challenge Part 2
Type: Contributed
Date/Time: Sunday, August 7, 2022 : 4:00 PM to 4:45 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #323718
Title: A Continual Learning Framework for Adaptive Defect Classification and Inspection
Author(s): Wenbo Sun* and Raed Al Kontar and Judy Jin and Tzyy-Shuh Chang
Companies: University of Michigan Transportation Research Institute and University of Michigan and University of Michigan and OG Technology
Keywords: defect classification; continual learning; out-of-distribution learning; 3D point cloud data

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.

Authors who are presenting talks have a * after their name.

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