Graph Embedding and Optimal Transport for Few-Shot Classification of Metal Surface Defect

Defect classification exhibits great importance in metal surface defect inspection. Most previous defect classification models are based on fully supervised learning, which requires a large amount of training data with image labels. However, collecting defective images in industrial scenarios is quite difficult due to the well-optimized manufacturing techniques. Besides, image annotation is also expensive and time-consuming. In this article, we propose a novel few-shot defect classification method, which aims to recognize novel defective classes with few labeled samples. Specifically, the proposed method follows a transductive paradigm and consists of two modules, i.e., graph embedding and distribution transformation (GEDT) module and optimal transport (OPT) module. The GEDT module not only makes full use of the relevant correlation information between different features in the support set and the query set but also ensures the consistent distribution of the graph embedding results. Then, the OPT module is leveraged to implement few-shot classification in a transductive manner. Finally, experiments conducted on the proposed metal surface defect dataset, and the results demonstrate that the proposed method achieves the state-of-the-art performance under both one-shot and five-shot settings.