纺织学报 ›› 2022, Vol. 43 ›› Issue (11): 59-67.doi: 10.13475/j.fzxb.20210310709

• 纺织工程 • 上一篇    下一篇

基于奇异值分解的双算法织物缺陷检测

郑兆伦1,2, 鲁玉军1()   

  1. 1.浙江理工大学 机械工程学院, 浙江 杭州 310018
    2.浙江理工大学龙港研究院, 浙江 温州 325802
  • 收稿日期:2021-03-31 修回日期:2022-07-04 出版日期:2022-11-15 发布日期:2022-12-26
  • 通讯作者: 鲁玉军
  • 作者简介:郑兆伦(1998—),男,硕士生。主要研究方向为机器视觉检测技术。
  • 基金资助:
    浙江省重点研发计划项目(2020C01084);浙江省重点研发计划项目(2022C01242);浙江理工大学龙港研究院项目(LGYJY2021004)

Dual-algorithm for fabric defect detection based on singular value decomposition

ZHENG Zhaolun1,2, LU Yujun1()   

  1. 1. School of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou, Zhejiang 310018, China
    2. Longgang Institute of Zhejiang Sci-Tech University, Wenzhou, Zhejiang 325802, China
  • Received:2021-03-31 Revised:2022-07-04 Published:2022-11-15 Online:2022-12-26
  • Contact: LU Yujun

摘要:

针对难以有效地同时检测洞形缺陷和线形缺陷问题,提出一种基于奇异值分解的双算法织物缺陷检测方法。该方法首先对图像进行奇异值分解,通过对原图与特征值图进行布尔差集运算消除背景纹理并保留缺陷区域;然后采用均值滤波、直方图均值化及方差阈值滤波消除纹理及噪声点的干扰;接着通过形态学处理确定缺陷位置;最后采用面积阈值和方差阈值的方式获取线形缺陷和洞形缺陷。实验结果表明:该方法不仅能够有效地检测洞形缺陷,而且在检测线形缺陷上也有很好的表现,并在准确率上明显高于传统算法,证明了本文算法的有效性和多用途性。

关键词: 织物缺陷检测, 奇异值分解, 方差阈值滤波, 布尔差集运算, 面积阈值滤波

Abstract:

Aiming at the problem that hole and line defects are difficult to be detected simultaneously, a double-algorithm fabric defect detection method based on singular value decomposition was proposed. The image was decomposed by singular value first, and then the background texture was eliminated and the defect area was preserved by Boolean difference set operation between the original image and the eigenvalue image. Following that the mean filtering, histogram average and variance threshold filtering were used to eliminate the interference of texture and noise points and the defect position was determined by morphological processing. The linear defects and hole defects were eventually obtained by using area threshold and variance threshold. Experimental results show that the proposed method not only can effectively detect hole defects, but also has a good performance in detecting linear defects, and the accuracy is significantly higher than the traditional algorithm, which proves the effectiveness and versatility of the proposed method.

Key words: fabric defect detection, singular value decomposition, variance threshold filtering, Boolean difference set operation, area threshold filtering

中图分类号: 

  • TP391.4

图1

缺经织物缺陷图片"

图2

示例图像"

图3

算法流程图"

图4

原图与特征值图布尔差集运算 注:1—洞形缺陷织物;2—线形缺陷织物。"

图5

均值滤波及直方图均值化 注:1—洞形缺陷织物;2—线形缺陷织物。"

图6

方差阈值滤波核"

图7

方差阈值滤波"

图8

膨胀和腐蚀运算原理图"

图9

形态学处理 注:1—洞形缺陷织物;2—线形缺陷织物。"

图10

洞形缺陷检测结果"

图11

线形缺陷与正常织物特征值图对比 注:1—纬纱缺陷;2—经纱缺陷;3—正常织物。"

图12

线形缺陷和正常织物方差阈值滤波结果 注:1—纬纱缺陷;2—经纱缺陷;3—正常织物。"

表1

3种织物缺陷算法比较"

算法 洞形检测准确率/
%
线形检测准确率/
%
平均耗时/
s
文献[1] 80.2 34.3 0.11
文献[8] 75.8 27.5 0.34
本文算法 91.8 83.5 0.83

图13

洞形缺陷对比图 注:1~5分别为5种典型洞形缺陷织物。"

图14

线形缺陷对比图 注:1~5分别为5种典型线形缺陷织物。"

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