Teacher-Student Collaboration: Effective Semi-Supervised Model for Defect Instance Segmentation

Abstract

Recent defect instance segmentation methods heavily rely on pixel-level annotated images. However, acquiring labeled defect data from modern manufacturing industries takes significant time and effort. In this paper, we propose a novel semi-supervised approach for defect instance segmentation via Teacher-Student model Collaboration (TSC) to address the challenges of small defect dataset sizes and the blurring boundaries of defects. Specifically, we propose a generalized distribution fusion module (GDFM) to improve the quality of pseudo-labels. This module constructs a Gaussian mixture model to estimate the feature distributions from the student model. Leveraging Bayes’ theorem, we calculate the posterior probability, which significantly enhances the accuracy of classification pseudo-labels and refines the ambiguous regions in segmentation pseudo-labels produced by the teacher model. To manage the blurring boundaries of defects, we propose a cross-supervision contrastive learning module (CSCL). By combining the idea of online hard example mining with contrastive learning, we propose a simple yet effective method to distinguish the easy/hard and positive/negative areas of defect instances of unlabeled and labeled images. Extensive experiments demonstrate that our TSC model achieves state-of-the-art performance across three semi-supervised defect instance segmentation datasets with low annotation ratios. Note to Practitioners—The defect instance segmentation task aims to accurately locate each defect with a corresponding mask. Recent CNN models for defect instance segmentation heavily rely on pixel-level annotations, which demand significant time and effort within modern manufacturing industries. Therefore, we expand the semi-supervised framework to encompass defect instance segmentation and propose a semi-supervised approach for defect instance segmentation. We use labeled images to train the model and utilize the highly confident output of unlabeled images as pseudo-labels to improve the model instance segmentation performance. Our approach is tailored to address two prominent characteristics in detection inspection: small dataset sizes and blurring boundaries, thereby undergoing corresponding improvements. Meanwhile, our method demonstrates promising defect detection performance in real-world industrial settings with minimal annotation requirements. Extensive experiments demonstrate that our TSC model achieves state-of-the-art performance on three defect instance datasets.