{"title":"The feature selection and bias classification of magnetic tile surface defect","authors":"张振尧 Zhang Zhenyao, 白瑞林 Bai Ruilin, 过志强 Guo Zhiqiang, 姜利杰 Jiang Lijie","doi":"10.3788/GXJS20144005.0434","DOIUrl":null,"url":null,"abstract":"In order to improve the accuracy and reduce the prediction time of detection of magnetic tile surface defect,a method of the feature selection and the bias classification is proposed.While offline training,the subgraphs which are generated from the transformation by Gabor filters are fused.Then the texture features of the pictures are extracted.The Relief algorithm is improved to extract the feature subset which have a strong correlation with category and remove redundant features.In order to decrease the miss rate of defective magnetic tile,the bias classification is performed before used LSSVM to predict the categories.It is proved that the proposed method can achieve about 99.09%as the accuracy rate of the defect magnet and the overall accuracy rate is about 96.89%.Compared with the original method,the online prediction only costs 67.4ms which decreased by nearly 1/4.","PeriodicalId":35591,"journal":{"name":"光学技术","volume":"21 1","pages":"434-439"},"PeriodicalIF":0.0000,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"光学技术","FirstCategoryId":"1089","ListUrlMain":"https://doi.org/10.3788/GXJS20144005.0434","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Physics and Astronomy","Score":null,"Total":0}
引用次数: 2
Abstract
In order to improve the accuracy and reduce the prediction time of detection of magnetic tile surface defect,a method of the feature selection and the bias classification is proposed.While offline training,the subgraphs which are generated from the transformation by Gabor filters are fused.Then the texture features of the pictures are extracted.The Relief algorithm is improved to extract the feature subset which have a strong correlation with category and remove redundant features.In order to decrease the miss rate of defective magnetic tile,the bias classification is performed before used LSSVM to predict the categories.It is proved that the proposed method can achieve about 99.09%as the accuracy rate of the defect magnet and the overall accuracy rate is about 96.89%.Compared with the original method,the online prediction only costs 67.4ms which decreased by nearly 1/4.
光学技术Physics and Astronomy-Atomic and Molecular Physics, and Optics
CiteScore
0.60
自引率
0.00%
发文量
6699
期刊介绍:
The predecessor of Optical Technology was Optical Technology, which was founded in 1975. At that time, the Fifth Ministry of Machine Building entrusted the School of Optoelectronics of Beijing Institute of Technology to publish the journal, and it was officially approved by the State Administration of Press, Publication, Radio, Film and Television for external distribution. From 1975 to 1979, the magazine was named Optical Technology, a quarterly with 4 issues per year; from 1980 to the present, the magazine is named Optical Technology, a bimonthly with 6 issues per year, published on the 20th of odd months.
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