Metal surface defect segmentation can play an important role in dealing with the issue of quality control during the production and manufacturing stages. There are still two major challenges in industrial applications. One is the case that the number of metal surface defect samples is severely insufficient, and the other is that the most existing algorithms can only be used for specific surface defects and it is difficult to generalize to other metal surfaces. In this work, a theory of few-shot metal generic surface defect segmentation is introduced to solve these challenges. Simultaneously, the Triplet-Graph Reasoning Network (TGRNet) and a novel dataset Surface Defects- 4i are proposed to achieve this theory. In our TGRNet, the surface defect triplet (including triplet encoder and trip loss) is proposed and is used to segment background and defect area, respectively. Through triplet, the few-shot metal surface defect segmentation problem is transformed into few-shot semantic segmentation problem of defect area and background area. For few-shot semantic segmentation, we propose a method of multi-graph reasoning to explore the similarity relationship between different images. And to improve segmentation performance in the industrial scene, an adaptive auxiliary prediction module is proposed. For Surface Defects- 4i, it includes multiple categories of metal surface defect images to verify the generalization performance of our TGRNet and adds the nonmetal categories (leather and tile) as extensions. Through extensive comparative experiments and ablation experiments, it is proved that our architecture can achieve state-of-the-art results.

中文翻译：

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用于少射击金属通用表面缺陷分割的三重图推理网络


金属表面缺陷分割在处理生产和制造阶段的质量控制问题方面可以发挥重要作用。工业应用仍面临两大挑战。一种是金属表面缺陷样本数量严重不足的情况，另一种是大多数现有算法只能用于特定的表面缺陷，很难推广到其他金属表面。在这项工作中，引入了少样本金属通用表面缺陷分割理论来解决这些挑战。同时，提出了三重图推理网络（TGRNet）和一个新颖的数据集 Surface Defects-4i 来实现这一理论。在我们的 TGRNet 中，提出了表面缺陷三元组（包括三元组编码器和行程损失），并分别用于分割背景和缺陷区域。通过三元组，将少样本金属表面缺陷分割问题转化为缺陷区域和背景区域的少样本语义分割问题。对于少样本语义分割，我们提出了一种多图推理方法来探索不同图像之间的相似关系。为了提高工业场景中的分割性能，提出了自适应辅助预测模块。对于 Surface Defects-4i，它包括多个类别的金属表面缺陷图像，以验证我们的 TGRNet 的泛化性能，并添加非金属类别（皮革和瓷砖）作为扩展。通过大量的比较实验和烧蚀实验，证明我们的架构可以达到state-of-the-art的结果。