Dual Feature-Integration Network for Faster and More Pragmatic Few-Shot Strip Steel Surface Defect Classification

Rapid and accurate classification of surface defects in the strip steel production process contributes to reducing production costs in factories. In this article, a dual feature-integration network (DFINet) is constructed for faster and more pragmatic few-shot strip steel surface defect classification scenarios. The model utilizes a relatively simple convolutional neural network (CNN) as the backbone to improve real-time performance. Bidirectional long short-term memory networks are employed to extract cross-image features (CIFs) from the support set, which are, then, fused with the structural features extracted by the backbone to obtain aggregated features for updating deeper support set features. The positions of support samples are adjusted by randomly cropping them into several subimages and fusing the features of these subimages to further update the positions of support samples in the metric space. Leveraging the nonparametric structure and strong adaptability of Euclidean distance, the model employs it as a classifier to further enhance real-time and classification performance. Additionally, to simulate more pragmatic few-shot strip steel surface defect classification scenarios, a modification is made to the common N-way, K-shot training mode, by changing the number of samples in the support set from fixed-shot to Random-shot. Extensive experiments demonstrate that the proposed method can rapidly and accurately classify surface defects in strip steel, even in more challenging few-shot classification scenarios.