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.

基于视觉的缺陷分类是控制制造系统中产品质量的一项重要技术。由于在实际生产中很难获得足够的标记样本以进行模型训练，因此从标记样本和未标记样本中学习的半监督学习更适合此任务。但是，表面缺陷的类内差异和类间相似性（称为劣类分离）可能会导致半监督方法在标记小的样本上表现不佳。虽然基于图的方法（例如图卷积网络（GCN））可以很好地解决该问题。因此，本文提出了一种新的基于图的半监督方法，称为多显微图图卷积网络（MMGCN），用于表面缺陷分类。首先，MMGCN通过构造多个显微照片而不是大型图形来执行图卷积，并通过在显微照片中将标记信息从标记样本传播到未标记样本来标记未标记样本以获得多个标记。对标签进行权重可以获得最终标签，从而解决了原始GCN的计算复杂性和实用性的局限性。其次，MMGCN将未标记的数据集分为多个批次，并设置准确性阈值。当模型准确性达到阈值时，将对未标记的数据集进行批量标记。一个著名的案例已被用来评估该方法的性能。实验结果表明，提出的MMGCN比GCN具有更好的计算复杂度和实用性。为了准确性，