An Extending Interclass Distance Real-Time Network Using Positional Orientation Transformation for Few-Shot Strip Steel Surface Defect Classification

Abstract

In the era of intelligent manufacturing, the rapid and accurate classification of strip steel surface defects is crucial. Deep learning typically relies on a large number of parameters and labeled samples to achieve outstanding performance. However, acquiring a sufficient number of defects in actual steel production poses challenges, and a high number of parameters can impact the real-time performance of defect classification. To tackle these issues, an extending interclass distance (Eid) real-time network using positional orientation transformation for few-shot strip steel surface defect classification is proposed (called the EidNet). EidNet utilizes a fewer parameters neural network as the feature extractor, enabling quick model convergence. To overcome the potential limitations of the fewer parameters model in expressing features, EidNet employs a no learnable parametric technique to artificially extend the interclass distance in the metric space, utilizing directional transformation of prototype positions to manually design the direction of extending, dispersing prototypes that are different from the query sample class as much as possible, thereby enhancing classification performance. The model uses the Euclidean distance as its classifier to maintain a low overall number of parameters. Experimental results demonstrate that EidNet significantly enhances the real-time performance of defect classification, striking a balance between real-time requirement and classification accuracy, while also exhibiting superior generalization capabilities.