A new graph-based semi-supervised method for surface defect classification

Abstract

Abstract Vision-based defect classification is an important technology to control the quality of product in manufacturing system. As it is very hard to obtain enough labeled samples for model training in the real-world production, the semi-supervised learning which learns from both labeled and unlabeled samples is more suitable for this task. However, the intra-class variations and the inter-class similarities of surface defect, named as the poor class separation, may cause the semi-supervised methods to perform poorly with small labeled samples. While graph-based methods, such as graph convolution network (GCN), can solve the problem well. Therefore, this paper proposes a new graph-based semi-supervised method, named as multiple micrographs graph convolutional network (MMGCN), for surface defect classification. Firstly, MMGCN performs graph convolution by constructing multiple micrographs instead of a large graph, and labels unlabeled samples by propagating label information from labeled samples to unlabeled samples in the micrographs to obtain multiple labels. Weighting the labels can obtain the final label, which can solve the limitations of computation complexity and practicality of original GCN. Secondly, MMGCN divides unlabeled dataset into multiple batches and sets an accuracy threshold. When the model accuracy reaches the threshold, the unlabeled datasets are labeled in batches. A famous case has been used to evaluate the performance of the proposed method. The experimental results demonstrate that the proposed MMGCN can achieve better computation complexity and practicality than GCN. And for accuracy, MMGCN can also obtain the best performance and the best class separation in the comparison with other semi-supervised surface defect classification methods.