Autolabeling-Enhanced Active Learning for Cost-Efficient Surface Defect Visual Classification

Abstract

Active learning can reduce the human effort required for labeling training samples while preserving the performance of visual classifiers. However, existing active learning frameworks cannot be used to perform visual classification of industrial product surface defects because they still require intensive manual annotation efforts. In this study, we propose a cost-efficient autolabeling-enhanced active learning (ALEAL) framework to reduce the human annotation effort required for surface defect visual classification. The proposed ALEAL framework employs a deep convolutional neural network (CNN) as a visual classifier trained from an initial set of human-labeled training samples. Then, the collected unlabeled training samples are input into the classifier for category confidence estimation. Next, a novel diverse cost-effective query strategy (DCEQS) is proposed to select some high-confidence samples for autolabeling and some informative samples for sample proposals that need labeling. Subsequently, to further reduce the human annotation effort, a novel autolabeling module is proposed and introduced in ALEAL that can automatically label a portion of the informative unlabeled training samples selected by the DCEQS. In this study, a novel attention-based similarity measurement network (ASMN) is proposed as an implementation of this autolabeling module by measuring the similarity between unlabeled and labeled samples. Finally, the remaining unlabeled samples are annotated by human experts, and all the newly labeled samples are used to retrain the classifier. Through the autolabeling process from the DCEQS and ASMN, ALEAL can automatically label additional training samples and achieve a competitive performance while requiring few human-labeled training samples, which is highly important in industrial applications. Extensive experimental results show that, compared with popular active learning methods, ALEAL can dramatically reduce the effort involved in human annotation and achieve state-of-the-art cost efficiency for the visual classification of industrial product surface defects.