MSRC-based defective nanocrystalline soft magnetic ribbon detection
MSRC-based defective nanocrystalline soft magnetic ribbon detection
复制标题
基于MSRC的缺陷纳米晶软磁带检测
DOI：
10.1088/0957-0233/26/9/095604
复制
发表时间：
2015-07
影响因子：
2.4
通讯作者：
Zhao Xinyue
中科院分区：
文献类型：
--
作者：
He Zaixing;Zhao Xinyue
传统的基于金相样品的纳米晶软磁材料的人工检测是一项费时且不可靠的工作。简单地采用现有的自动信号处理方法作为替代也很难达到高精度。针对这一问题，提出了一种基于高分辨率光学显微图像的纳米软磁带材微观缺陷自动识别方法。目标问题被看作是一个模式识别问题，其中图像被分为无缺陷和有缺陷两类。利用一种有效而高效的随机特征来描述纳米晶软磁薄带的结构。然后，通过改进的基于稀疏表示的分类器(MSRC)将提取的特征用于缺陷识别。在实验中，比较了局部二值模式(LBP)和主成分分析(PCA)这两种常见的特征，以及不同的分类器(支持向量机和稀疏表示分类器)。实验结果表明，该方法对带材缺陷的识别具有较低的误识率。
The traditional manual inspection of nanocrystalline soft magnetic materials based on metallographic samples is a time-consuming and somewhat unreliable task. It is also difficult to achieve high accuracy by simply adopting existing automatic signal processing methods as an alternative. To address the issue, a novel automatic microscopic defect recognition method for nanocrystalline soft magnetic ribbon using high-resolution optical microscopic images is proposed. The target problem is viewed as a pattern recognition problem, in which images are classified as non-defective and defective. An effective and highly efficient random feature is used to describe the structures of the nanocrystalline soft magnetic ribbons. Then the extracted features are used to recognize defects via a modified sparse representation-based classifier (MSRC). In the experiment, two well-known features, LBP (local binary pattern) and PCA (principal component analysis), and different classifiers, SVM (support vector machine) and SRC (sparse representation classifier), are compared. The experimental results demonstrate that the proposed method can provide low error rates in recognizing ribbon defects.
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DOI：
10.2307/2982750
发表时间：
1992-03
期刊：
--
影响因子：
--
作者：
G. McLachlan
通讯作者：
G. McLachlan
影响因子：
4.2
作者：
T. Santos;R. Miranda;C. Carvalho
通讯作者：
T. Santos;R. Miranda;C. Carvalho
影响因子：
2.4
作者：
Jung‐Ryul Lee;S. Y. Chong;Nitam Sunuwar;Chan Yik Park
通讯作者：
Jung‐Ryul Lee;S. Y. Chong;Nitam Sunuwar;Chan Yik Park
影响因子：
2.4
作者：
Zheng Liu;P. Ramuhalli;S. Safizadeh;D. Forsyth
通讯作者：
Zheng Liu;P. Ramuhalli;S. Safizadeh;D. Forsyth
影响因子：
1.2
作者：
K. Yubuta;Enrico Mund;A. Makino;A. Inoue
通讯作者：
K. Yubuta;Enrico Mund;A. Makino;A. Inoue