Multi-class classification method for steel surface defects with feature noise

Abstract

Defect classification is the key task of a steel surface defect detection system. The current defect classification algorithms have not taken the feature noise into consideration. In order to reduce the adverse impact of feature noise, an anti-noise multi-class classification method was proposed for steel surface defects. On the one hand, a novel anti-noise support vector hyper-spheres (ASVHs) classifier was formulated. For N types of defects, the ASVHs classifier built N hyper-spheres. These hyper-spheres were insensitive to feature and label noise. On the other hand, in order to reduce the costs of online time and storage space, the defect samples were pruned by support vector data description with parameter iteration adjustment strategy. In the end, the ASVHs classifier was built with sparse defect samples set and auxiliary information. Experimental results show that the novel multi-class classification method has high efficiency and accuracy for corrupted defect samples in steel surface.