Few-Shot Steel Strip Surface Defect Classification via Self-Correlation Enhancement and Feature Refinement

Accurate surface defect classification is essential for industrial quality inspection but remains challenging due to the scarcity of annotated samples, high annotation costs, and significant intraclass variations. To tackle these issues, we propose a novel few-shot learning framework for steel strip surface defect classification. Our approach integrates three key components: a self-correlation enhanced (SCE) module for extracting discriminative local region features, a feature refinement module (FRM) for filtering background noise, and a similarity measurement module (SMM) for robust prototype-based classification. In addition, we design a hybrid loss function that jointly optimizes supervised and metric-based objectives to enhance class separability. Experiments on the FSC-20 dataset show that the proposed method outperforms existing approaches in both five-way one-shot and five-way five-shot settings, while also demonstrating strong cross-domain generalization.