Surface Defects Classification of Hot Rolled Strip Based on Few-shot Learning

Abstract

Surface defect classification plays an important role in the assessment of production status and analyzing possible defect causes of hot rolled strip steel. It is extremely challenging owing to the rare occurrence and various appearances of defects. In this work, an improved deep learning model is proposed to solve the problem of poor classification accuracy when only a few labeled samples can be available. Different from most inductive small-sample learning methods, a transductive learning algorithm is designed where a new classifier is trained in the test phase and therefore can fit in with the needs of unknown samples. In addition, a simple feature fusion technique is implemented to extract more sample information. Based on a real-world steel surface defect dataset NEU, the proposed method can achieve a high classification accuracy of 97.13% with only one labeled sample. The experimental results show that the improved model is superior to other existing few-shot learning methods for surface defects classification of hot-rolled steel strip.