# A Continual Learning Framework for Adaptive Defect Classification and Inspection

Source: [A Continual Learning Framework for Adaptive Defect Classification and Inspection](<https://www.sota2.com/research/paper/68fa6fe41e209131cea47f1f>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:29:17.533Z

Authors: Wenbo Sun, Raed Al Kontar, Judy Jin, Tzyy-Shuh Chang

Published: 2022-03-16T17:57:41.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2203.08796>)

[PDF](<https://arxiv.org/pdf/2203.08796.pdf>)