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.

缺陷分类是钢表面缺陷检测系统的关键任务。当前的缺陷分类算法没有考虑特征噪声。为了减少特征噪声的不利影响，提出了一种针对钢表面缺陷的抗噪多类分类方法。一方面，提出了一种新型的抗噪支持向量超球体（ASVHs）分类器。对于N种类型的缺陷，ASVH分类器构建了N超球体。这些超球体对特征和标签噪声不敏感。另一方面，为了减少在线时间和存储空间的成本，使用支持向量数据描述和参数迭代调整策略对缺陷样本进行修剪。最后，使用稀疏缺陷样本集和辅助信息构建了ASVHs分类器。实验结果表明，该新的多类分类方法对钢表面缺陷样本的识别具有很高的效率和准确性。