Image Identification for Surface Defects of Steel Ball Based on Support Vector Machine

被引：2

作者：

Yu, Yong Wei ^{[1
]}

Yin, Guo Fu ^{[1
]}

Du, Liu Qing ^{[1
]}

机构：

[1] Sichuan Univ, Chengdu 610064, Peoples R China

来源：

ADVANCES IN MECHANICAL DESIGN, PTS 1 AND 2 | 2011年 / 199-200卷

关键词：

Image Identification; Support Vector Machine; Surface Defect; Steel Ball; REDUCTION;

D O I：

10.4028/www.scientific.net/AMR.199-200.1769

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

In response to the dilemma for image identification by the existing classifier toward surface defects of steel ball, an improved support vector machine (SVM) for multiclass problems is proposed. Minimum distance method is presented to resolve the unclassifiable region of the multiclass SVMs. The 16 image features of the surface defects are selected as input vector of the SVMs. The experiment results show that more accurate identification toward surface defects of steel ball was achieved by the improved multiclass SVM and the accuracy can reach 95%.

引用

页码：1769 / 1772

页数：4

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