| 屈尔庆,刘坤,陈海永,孙鹤旭.基于P-ReliefF特征选择方法的带钢表面缺陷识别[J].电子测量与仪器学报,2017,31(7):1053-1060 |
| 基于P-ReliefF特征选择方法的带钢表面缺陷识别 |
| Steel strip surface defect recognition based on P-ReliefF feature selection method |
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| DOI：10.13382/j.jemi.2017.07.010 |
| 中文关键词: 特征选择 带钢表面缺陷 ReliefF 相关性 缺陷识别 |
| 英文关键词:feature selection strip surface defect ReliefF correlation defect identification |
| 基金项目:国家自然科学基金(61403119)、河北省自然科学基金(F2014202166)、天津市特派员科技计划(15JCTPJC55500)资助项目 |
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| Author | Institution |
| Qu Erqing | College of Control Science and Engineering, Hebei University of Technology, Tianjin, 300130, China |
| Liu kun | College of Control Science and Engineering, Hebei University of Technology, Tianjin, 300130, China |
| Chen Haiyong | College of Control Science and Engineering, Hebei University of Technology, Tianjin, 300130, China |
| Sun Hexu | 1.College of Control Science and Engineering, Hebei University of Technology, Tianjin, 300130, China;
2. College of electrical engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China |
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| 摘要点击次数: 3228 |
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| 中文摘要: |
| 带钢表面缺陷纹理的复杂性和多样性、背景纹理中存在的伪缺陷等给现有的带钢表面缺陷特征提取和识别带来了极大的困难。为此，提出了一种新的带钢表面缺陷选择与识别方法。首先，通过各向异性扩散算法对带钢表面的伪缺陷干扰进行抑制；其次，利用提出的P ReliefF方法对表面缺陷特征进行选择，相比传统的ReliefF方法，该方法考虑了不同维度特征之间的关联性；最后，利用筛选的特征集和支持向量机(SVM)核分类器对带钢表面缺陷进行分类与识别。实验结果表明，提出的方法能够提取出具有高区分性和鲁棒性的带钢表面缺陷特征，并且对于划痕、褶皱、凸起和污渍等不同类型的带钢表面缺陷，本方法相比传统的方法可以获得更高的识别率。 |
| 英文摘要: |
| The complexity and diversity of the texture of the surface defects on the surface of strip steel, as well as the false defects in the background texture, have brought great difficulties to the feature extraction and recognition of the strip steel surface defects. Therefore, this paper presents a new method for the selection and identification of steel strip surface defects. First of all, through the inhibition of anisotropic diffusion algorithm for suppression of false defect on strip surface; secondly, selection of surface defect features using P Relief method in this paper, compared with the traditional Relief method, this method considers the different dimensions of feature correlation. Finally, using the feature set and SVM kernel classifier to classify and recognize the surface defects of steel strip. The experimental results show that the proposed method can extract the features of strip surface defect pairwise independence and robustness, and for scratches, bumps and folds, stains and other different types of defects, this method compared with the traditional method can get higher recognition rate. |
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