基于改进的加权中值滤波与K-means聚类的织物缺陷检测

doi:10.13475/j.fzxb.20181200407

纺织学报 ›› 2019, Vol. 40 ›› Issue (12): 50-56.doi: 10.13475/j.fzxb.20181200407

基于改进的加权中值滤波与K-means聚类的织物缺陷检测

张缓缓(), 马金秀, 景军锋, 李鹏飞

西安工程大学电子信息学院, 陕西西安 710048

收稿日期:2018-12-03 修回日期:2019-06-02 出版日期:2019-12-15 发布日期:2019-12-18
作者简介:张缓缓(1986—),女,讲师,硕士。主要研究方向为图像处理、模式识别。E-mail:zhanghuanhuan0557@163.com。
基金资助:
国家自然科学基金项目(61902302);陕西省高校科协青年人才托举计划项目(20180115);陕西省教育厅科研计划项目(18JK0338)

Fabric defect detection method based on improved fast weighted median filtering and K-means

ZHANG Huanhuan(), MA Jinxiu, JING Junfeng, LI Pengfei

School of Electronic and Information, Xi'an Polytechnic University, Xi'an, Shaanxi 710048, China

Received:2018-12-03 Revised:2019-06-02 Online:2019-12-15 Published:2019-12-18

摘要/Abstract

摘要：

为检测纹理织物在生产过程中产生的各种疵点,提出一种基于改进的加权中值滤波与K-means聚类相结合的纹理织物疵点检测方法。首先利用改进的加权中值滤波对纹理织物图像进行预处理,以减少纹理信息对疵点检测产生的影响,同时通过联合直方图动态数据分配权重和像素,减少寻求中位数的时间来有效地缩短检测时间,提高了执行速度;然后采用K-means算法对滤波后的织物图像进行聚类,计算织物图像疵点和非疵点的聚类中心,进而实现图像疵点区域的分割。实验结果表明,该方法可有效地检测出方格、点形、星形、平纹、斜纹等多类型纹理织物的疵点,并显著提高检测速度。

关键词: 织物疵点检测, 改进加权中值滤波, 联合直方图, K-means聚类

Abstract:

In order to detect various defects in the production process of textured fabrics, a texture fabric defect detection method based on improved weighted median filtering and K-means clustering was proposed. Firstly, the fabric image was preprocessed by the improved weighted median filter to reduce the influence of texture information on the defect detection. At the same time, by assigning weights and pixels to the histogram dynamic data, the time to seek the median was effectively shortened to increase the execution speed. Then, the K-means algorithm was adopted to cluster the filtered fabric images, and the cluster centers of the fabric image defects and non-defects were calculated, thereby realizing the segmentation of the image defect regions. The experimental results show that the method can effectively detect the defects of various types of textured fabrics such as square, dot, star, plain, and twill and significantly increase the detection speed.

Key words: fabric defect detection, improved weighted median filtering, joint histogram, K-means clustering

中图分类号:

TP391

张缓缓, 马金秀, 景军锋, 李鹏飞. 基于改进的加权中值滤波与K-means聚类的织物缺陷检测[J]. 纺织学报, 2019, 40(12): 50-56.

ZHANG Huanhuan, MA Jinxiu, JING Junfeng, LI Pengfei. Fabric defect detection method based on improved fast weighted median filtering and K-means[J]. Journal of Textile Research, 2019, 40(12): 50-56.

图/表 12

图1

图2

图3

图4

图5

图6

表1

图7

图8

图9

表2

表3

参考文献 17

[1]	CONCI A, PROENÇA C B. A fractal image analysis system for fabric inspection based on a box-counting method[J]. Computer Networks & Isdn Systems, 1998,30(20/21):1887-1895.
[2]	朱丹丹, 潘如如, 高卫东. 基于傅里叶特征谱和相关系数的织物瑕疵检测[J]. 计算机工程与应用, 2014,50(19):182-186.
	ZHU Dandan, PAN Ruru, GAO Weidong. Fabric defect detection using characteristic spectrum of Fourier transform and correlation coefficient[J]. Computer Engineering and Applications, 2014,50(19):182-186.
[3]	JING J F, YANG P, LI P F. Supervised defect detection on textile fabrics via optimal Gabor filter[J]. Journal of Industrial Textile, 2013,44(1):40-57.
[4]	LI W, XUE W, CHENG L. Intelligent detection of defects of yarn-dyed fabrics by energy-based local binary patterns[J]. Textile Research Journal, 2012,82(19):1960-1972.
[5]	AMET A L, ERTÜZÜN A, ERÇIL A. An efficient method for texture defect detection: sub-band domain co-occurrence matrices[J]. Image and Vision Computing, 2000,18(6/7):543-553. doi: 10.1016/S0262-8856(99)00062-1
[6]	KUMBHAR P Y, MATHPATI T. Textile fabric defects detection and sorting using image processing[J]. International Journal for Research in Emerging Science and Technology, 2016,3(3):19-24.
[7]	KUMAR A, PANG G K H. Defect detection in textured materials using Gabor filters[J]. IEEE Transactions on Industry Applications, 2002,38(2):425-440.
[8]	TSAI I S, HU M C. Automatic inspection of fabric defects using an artificial neural network technique[J]. Textile Research Journal, 1996,66(7):474-482.
[9]	HAJIMOWLANA S H, MUSCEDERE R, JULLIEN G A. et al. 1D autoregressive modeling for defect detection in web inspection systems[C]// Midwest Symposium on Systems & Circuits. [S. l.]: IEEE Computer Society, 1998: 318-321.
[10]	李敏, 崔树芹, 谢治平. 高斯混合模型在印花织物瑕疵检测中的应用[J]. 纺织学报, 2015,36(8):94-98.
	LI Min, CUI Shuqin, XIE Zhiping. Application of Gaussian mixture model on defect detection of print fabric[J]. Textile Research Journal, 2015,36(8):94-98.
[11]	MEI S, WANG Y, WEN G. Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model[J]. Sensors, 2018,18(4):1064.
[12]	YAPI D, MEJRI M, ALLILI M S. A Learning-based approach for automatic defect detection in textile images[J]. Biochemical & Biophysical Research Communications, 2015,48(3):2423-2428.
[13]	TSANG C S C, NGAN H Y T, PANG G K H. Fabric inspection based on the Elo rating method[J]. Pattern Recognition, 2016,51(4):378-394.
[14]	LI Y, ZHAO W, PAN J. Deformable patterned fabric defect detection with fisher criterion-based deep lear-ning[J]. IEEE Transactions on Automation Science and Engineering, 2017,14(2):1256-1264.
[15]	ZHANG Q, XU L, JIA J. 100+ times faster weighted median filter[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Columbus: CUPR, 2014: 2830-2837.
[16]	NG M K, NGAN H Y T, YUAN X, et al. Patterned Fabric Inspection and Visualization by the Method of Image Decomposition[J]. IEEE Transactions on Automation Science & Engineering, 2014,11(3):943-947.
[17]	张缓缓, 李仁忠, 景军锋. Frangi滤波器和模糊C均值算法相结合的织物瑕疵检测[J]. 纺织学报, 2015,36(9):120-124.
	ZHANG Huanhuan, LI Renzhong, JING Junfeng. Fabric defect detection based on Frangi filter and fuzzy C-means algorithm in combination[J]. Journal of Textile Research, 2015,36(9):120-124.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

织物类型	r	σ
方格	[12.5~13.0]	[135.0~245.0]
点形	[8.0~11.0]	[90.0~200.0]
星形	[55.0~58.0]	[250.0~390.0]
平纹	[5.0~10.0]	[12.0~25.0]
斜纹	[9.0~10.0]	[21.5~25.0]

方法	方格织物	点形织物	星形织物	平纹织物	斜纹织物
文献[3]	9.156 7	14.998 6	8.536 5	10.015 8	38.782 4
文献[17]	0.941 5	0.913 4	1.095 7	0.974 1	49.459 7
本文方法	0.193 7	0.233 4	0.312 2	0.049 5	0.507 7

织物类型	检测方法	A_CC/%	T_PR/%	F_PR/%
	文献[3]	93.93	95.07	12.87
方格	文献[17]	92.50	94.29	20.00
	本文方法	97.50	98.09	6.67
	文献[3]	92.49	96.01	29.97
点形	文献[17]	95.09	97.82	20.00
	本文方法	94.17	95.24	13.57
	文献[3]	93.62	97.14	25.88
星形	文献[17]	91.67	97.73	13.29
	本文方法	94.36	95.89	19.73
	文献[3]	95.83	98.09	20.05
平纹	文献[17]	90.94	95.23	30.48
	本文方法	94.23	96.84	25.75
	文献[3]	91.58	98.56	10.98
斜纹	文献[17]	92.79	96.76	17.79
	本文方法	94.64	93.42	5.96

基于改进的加权中值滤波与K-means聚类的织物缺陷检测

Fabric defect detection method based on improved fast weighted median filtering and K-means

RichHTML

PDF (PC)

摘要/Abstract

引用本文

使用本文

图/表 12

参考文献 17

相关文章 9

Metrics

本文评价

推荐阅读 0

[1]	朱磊, 任梦凡, 潘杨, 李博涛. 基于相似性定位和超像素分割的织物疵点检测[J]. 纺织学报, 2020, 41(10): 58-66.
[2]	夏凤勤毋戈谢昊洋钟跃崎. 基于人体纵截面特征曲线的体型分类[J]. 纺织学报, 2017, 38(06): 86-91.
[3]	尉苗苗李岳阳蒋高明丛洪莲. 应用最优Gabor滤波器的经编织物疵点检测[J]. 纺织学报, 2016, 37(11): 48-54.
[4]	石美红张正郭仙草陈永当. 基于显著纹理特征的织物疵点检测方法[J]. 纺织学报, 2016, 37(10): 42-049.
[5]	厉征鑫周建潘如如刘建立高卫东. 应用单演小波分析的织物疵点检测[J]. 纺织学报, 2016, 37(09): 59-64.
[6]	景军锋范晓婷李鹏飞张蕾张宏伟. 应用Gaussian回代交替方向图像分解算法的色织物疵点检测[J]. 纺织学报, 2016, 37(06): 136-141.
[7]	倪世明金娟凤庞程方邹奉元. 基于纵向轮廓曲线的青年女性体型细分研究[J]. 纺织学报, 2014, 35(8): 87-0.
[8]	景军锋李江南李鹏飞. 改进型奇异值分解在织物疵点检测上的应用[J]. 纺织学报, 2014, 35(6): 62-0.
[9]	管声启高照元吴宁徐帅华. 基于视觉显著性的平纹织物疵点检测[J]. 纺织学报, 2014, 35(4): 56-0.