期刊文献+

基于彩色图像处理与EDLines的数码印花缺陷检测系统 被引量:1

Digital Printing Defect Detection System Based on Color Image Processing and EDLines
下载PDF
导出
摘要 针对数码印花生产过程中由于喷头堵塞、电机偏差引起的PASS道缺陷问题,课题组设计了一套基于彩色图像处理与EDLines的数码印花缺陷检测系统。首先构建颜色补偿矩阵覆盖无关背景花案,增强缺陷与主色间差异性;然后分别在HSI颜色空间3通道采用自定义线型滤波锐化感兴趣区域并基于EDLines实现缺陷配准,并将3通道缺陷匹配结果进行区域融合和形态学处理;最后根据水平投影实现PASS道缺陷定位。实验结果表明:检测系统对印花织物表面缺陷的检测准确率达到96%以上,满足实际检测要求,为数码印花缺陷检测提供新的方法。 A digital printing defect detection system based on color image processing and EDLines was designed to solve the problem of PASS defect caused by nozzle blockage and motor deviation in digital printing production. Firstly the color compensation matrix was built to overlay the irrelevant background pattern and enhance the differences between defects and the main color. Then the region of interest was sharpened in HSI color space three-channel by using custom linear filtering and the defect registration was realized based on EDLines. The three-channel defect registration results were regional integrated and morphologically processed. Finally, PASS channel defect location was realized according to the horizontal projection. The experimental results show that the detection accuracy of printed fabric surface defects can reach above 96%, which meets the actual detection requirements and provides a new method for the detection of digital printing defects.
作者 黄乾玮 张团善 周玲 汤锋 李乐乐 HUANG Qianwei;ZHANG Tuanshan;ZHOU Ling;TANG Feng;LI Lele(School of Mechanical and Electrical Engineering,Xi’an Polytechnic University,Xi’an 710600,China;Shaoxing Keqiao West-Tex Textile Industry Innovative Institute,Shaoxing,Zhejiang 312030,China)
出处 《轻工机械》 CAS 2021年第4期74-79,共6页 Light Industry Machinery
基金 国家自然科学基金(61701384) 西安市现代智能纺织装备重点实验室资助项目(2019220614SYS021CG043)。
关键词 缺陷检测 颜色空间 颜色补偿 EDLines defect detection color space color compensation EDLines
  • 相关文献

参考文献7

二级参考文献57

  • 1王植,贺赛先.一种基于Canny理论的自适应边缘检测方法[J].中国图象图形学报(A辑),2004,9(8):957-962. 被引量:214
  • 2尚丽,郑春厚.基于稀疏编码的自然图像特征提取及去噪[J].系统仿真学报,2005,17(7):1782-1784. 被引量:12
  • 3孙业明,关山,牛海波.基于小波变换的针叶苗木彩色图像分割[J].东北电力学院学报,2005,25(6):9-13. 被引量:2
  • 4GonzalezRC,WoodsRE.ImageProcessing.2nded.,阮秋琦,阮宇智等译.北京:电子工业出版社,2003.476-477. 被引量:1
  • 5Tsai D, Lin C, Chen J. The evaluation of normalized cross correlations for defect detection. Pattern Recognition Letter, 2003(24):2525-2535. 被引量:1
  • 6NGAN Y T, PANG K H, YUNG H C. Automated fabric defect detection: a review [ J 1. Image and Vision Computing, 2011, 29:442-458. 被引量:1
  • 7KUO C F, HSU C T, CHEN W H. Automatic detection system for printed fabric defects [ J ]. Textile Research Journal, 2012, 82(6) : 591 -601. 被引量:1
  • 8LI M, WANG B, CUI S Q. Texture primitive based method for patterned fabric defect detection[ J]. Journal of Computational Information Systems, 2013, 13 ( 9 ) : 5125- 5132. 被引量:1
  • 9ZHOU X S, COMANICIU D, GUPTA A. An information fusion framework for robust shape tracking[ J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27 ( 1 ) : 115 - 129. 被引量:1
  • 10LEE D S. Effective Gaussian mixture learning for video background subtraction [ J ]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(5) :827- 832. 被引量:1

共引文献131

同被引文献8

引证文献1

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部