基于稀疏表示的印花织物疵点检测  

Patterned fabric defect detection based on sparse representation

作　　者：刘茁梅 李鹏飞[1] 景军锋[1] LIU Zhuomei;LI Pengfei;JING Junfeng(School of Electronics and Information,Xi′an Polytechnic University,Xi′an 710048,China)

机构地区：[1]西安工程大学电子信息学院,陕西西安710048

出　　处：《西安工程大学学报》2018年第2期197-202,共6页Journal of Xi’an Polytechnic University

基　　金：国家自然科学基金(61301276);陕西省工业科技攻关项目(2015GY034)

摘　　要：纺织品中的织物缺陷分布存在稀疏性,使得缺陷图像能够在特定的变换中进行稀疏表示.根据盲源分离理论和形态成分分析,将织物缺陷图像假设由缺陷、背景和噪声3种成分的线性叠加,通过稀疏表示模型对缺陷图像进行表示.首先对原始缺陷图像进行直方图均衡化处理;接着采用基于稀疏表示模型将织物图像分解为缺陷和纹理部分;最后对缺陷图像采用叠加二值化分割法,显示缺陷区域.实验结果表明,该方法对包括星型、方格型和圆点型在内的印花织物缺陷图像,检测时间短,效率较高,平均检测率可达96.3%.Aimed at the problems of patterned fabric,a defect detection method based on sparse representation are proposed.Due to the sparseness of the fabric defect distribution,a new approach based on sparse representation to addressing patterned fabric defect detection is presented.Inspired by the blind source separation theory and morphological component analysis,the fabric defect image is regarded to consist of a linear superposition of the three part of the defective part,the background part and the noise part.Firstly,histogram equalization preprocessing is used to enhance image contrast.Then the model based on sparse representation is used to decompose the fabric image into cartoon part and texture part.Finally,the defective image is segmented by superimposed binarization to show the defective region.The experimental results demonstrate that the proposed method could efficiently detect defects with shorter time and average detection accuracy reaches 96.3%for patterned fabric defect contained star-,box-and dot-patterned fabric images.

关 键 词：印花织物 疵点检测 稀疏表示 盲源分离 形态成分分析 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]