Adaptive Graph Neural Networks with Semi-Supervised Multi-Modal Fusion for Few-Shot Steel Strip Defect Detection

In recent years, deep learning-based methods for surface defect detection in steel strips have advanced rapidly. Nevertheless, existing approaches still face several challenges in practical applications, such as insufficient dimensionality of feature information, inadequate representation capability for single-modal samples, poor adaptability to few-shot scenarios, and difficulties in cross-domain knowledge transfer. To overcome these limitations, this paper proposes a multi-modal fusion framework based on graph neural networks for few-shot classification and detection of surface defects. The proposed architecture consists of three core components: a multi-modal feature fusion module, a graph neural network module, and a cross-modal transfer learning module. By integrating heterogeneous data modalities—including visual images and textual descriptions—the method facilitates the construction of a more efficient and accurate defect classification and detection model. Experimental evaluations on steel strip surface defect datasets confirm the robustness and effectiveness of the proposed method under small-sample conditions. The results demonstrate that our approach provides a novel and reliable solution for automated quality inspection of surface defects in the steel industry.