Semisupervised Defect Segmentation With Pairwise Similarity Map Consistency and Ensemble-Based Cross Pseudolabels

Deep-learning-based automatic defect segmentation is one of the hot research areas in computer vision application for the task of intelligent industrial inspection. Recently, several state-of-the-art models for image segmentation task have been proposed. However, their high performance is vastly dependent on the availability of large set of labeled data, which is one of the hindering factors in achieving full potential with deep learning methods in industrial inspection. In this article, we propose a novel method based on pairwise similarity map consistency with ensemble-based cross pseudolabels for semisupervised defect segmentation that uses limited labeled samples while exploiting additional label-free samples. The proposed approach uses three network branches that are regularized by pairwise similarity map consistency, and each of them is supervised by the pseudolabels generated by ensemble of predictions of the other two networks for the unlabeled samples. The proposed method achieved significant performance improvement over the baseline of learning only from the labeled images and the current state-of-the-art semisupervised methods. We perform ablation studies and extensive experiments on various parameters and components to demonstrate that our method achieved state-of-the-art results on three different datasets.