纺织高校基础科学学报2017，Vol.30Issue(1)：134-141,147,9.DOI:10.13338/j.issn.1006-8341.2017.01.023
基于非下采样轮廓波变换和朴素贝叶斯分类器的织物缺陷检测
Fabric defect detection based on NSCT and naive Bayes classifier
摘要
Abstract
In order to detect fabric defects that generated during the production process,the algorithm which combines Naive Bayesian classifier (NBC) and nonsubsampled Contourlet transform (NSCT) is presented.The proposed approach is divided into two phases:the learning phase and detection phase.In the learning phase,sub-blocks of defects and defect-free fabric are extracted,respectively.Firstly,NSCT is used to filter and denoise the image.Then,the mixture of the generalized Gaussian distribution (MoGG) model of each sub-block is extracted,and the Kullback-Leibler divergence (KLD) between the sub-blocks is calculated.Finally,NBC is trained by the obtained data.In the detection phase,the detected image is divided into subblocks.The trained NBC is used to detect the sub-blocks,and the defect detection results are output.Experiment results show that the algorithm of the defect detection has a good effect for the uniform gray and color textured fabric and can detect many kinds of fabric defects.The detection accuracy can reach 97％ which can meet demand of industrial production.关键词
非下采样轮廓波变换/朴素贝叶斯分类器/广义高斯分布的混合模型Key words
NSCT/naive Bayesian classifier/mixture of the generalized Gaussian distribution(MoGG) model分类
信息技术与安全科学引用本文复制引用
郝磐霞,景军锋,张蕾,张宏伟,王晓华..基于非下采样轮廓波变换和朴素贝叶斯分类器的织物缺陷检测[J].纺织高校基础科学学报,2017,30(1):134-141,147,9.基金项目
国家自然科学基金资助项目(61301276) （61301276）
陕西省工业科技攻关项目(2015GY034) （2015GY034）
陕西省科技厅自然基金资助项目(2014JQ-5029) （2014JQ-5029）
