基于复杂网络特性的带钢表面缺陷识别

任海鹏; 马展峰

doi:10.3724/SP.J.1004.2011.01407

基于复杂网络特性的带钢表面缺陷识别

doi: 10.3724/SP.J.1004.2011.01407

任海鹏^,,
马展峰

1.
西安理工大学信息与控制工程系西安 710048

详细信息

通讯作者:
任海鹏西安理工大学信息与控制工程系教授. 主要研究方向为非线性动力学系统, 复杂网络, 高效电力电子电路. E-mail: renhaipeng@xaut.edu.cn

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出版历程
- 收稿日期: 2010-12-01
- 修回日期: 2011-05-28
- 刊出日期: 2011-11-20

Strip Steel Surface Defect Recognition Based on Complex Network Characteristics

REN Hai-Peng^,,
MA Zhan-Feng

1.
Department of Information and Control Engineering, Xi'an University of Technology, Xi'an 710048

摘要

摘要: 针对带钢表面缺陷识别问题,提出一种基于动态演化复杂网络特性的特征描述方法, 这些特征同时具有位移、旋转不变性、大小不变性、较强的抗干扰能力和鲁棒性,为缺陷识别提供良好的分类特征;为了提高分类器的效率,应用主成分分析法 (Principal component analysis, PCA) 对复杂网络特征向量进行特征降维处理;采用最优有向无环图支持向量机 (Directed acyclic graph support vector machine, DAG-SVM)算法进行缺陷分类.结果表明该方法识别率高而且识别速度快.
- 缺陷识别 /
- 复杂网络特征 /
- 主成分分析法 /
- 有向无环图支持向量机
Abstract: A feature extraction method based on the characteristics of dynamic evolution complex networks is proposed for the strip steel surface defect recognition. The extracted features possess displacement, rotation and size invariability, strong anti-interference ability and robustness, therefore they are good classification features for steel surface defect recognition. In order to improve the efficiency of classification, the principal component analysis (PCA) is adopted to reduce the dimension of the feature vector. The directed acyclic graph support vector machine (DAG-SVM) algorithm is used for the defect classification. The experimental results show that this method is of high recognition rate and fast recognition speed.
- Defect recognition /
- complex network characteristics /
- principal component analysis (PCA) /
- directed acyclic graph support vector machine (DAG-SVM)

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参考文献(1)

[1]

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