Journal of Textile Research ›› 2020, Vol. 41 ›› Issue (10): 58-66.doi: 10.13475/j.fzxb.20200102909

• Textile Engineering • Previous Articles     Next Articles

Fabric defect detection based on similarity location and superpixel segmentation

ZHU Lei(), REN Mengfan, PAN Yang, LI Botao   

  1. School of Electronics and Information, Xi'an Polytechnic University, Xi'an, Shaanxi 710048, China
  • Received:2020-01-20 Revised:2020-06-04 Online:2020-10-15 Published:2020-10-27

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Abstract:

Aiming at the problem in defect detection and accurate contour segmentation of periodic texture fabric image, a method of fabric defect detection was proposed based on similarity location and superpixel segmentation techniques. The median filter and logarithm enhancement were applied for the detected image, and the saliency image of the enhancement image was estimated by frequency-tuned algorithm to facilitate the preprocessing of the detected image. Combining gray similarity detection parameters based on the normalized local mean difference with structural similarity detection parameters, a similarity metric function capable of measuring more types of periodic texture fabric images was constructed. The rough localization of defects was identified by thresholding the similarity measurement value of the enhancement image blocks. Finally, superpixel fine segmentation and binarization were performed on the rough localization image blocks, and the outliers were eliminated via connected domain analysis to obtain a complete defect contour. The experimental results show that, compared with the three conrentional methods, the proposed method has a higher accuracy in detecting the defects in the periodic texture fabric image, and the extracted defect contour is more accurate.

Key words: fabric defect detection, similarity location, superpixel segmentation, similarity metric function, normalized local mean difference

CLC Number: 

  • TS101.9

Fig.1

Test images(a) and defect detection results by method of reference [7](b), reference [9](c) and reference [15](d)"

Fig.2

Flow chart of fabric defect detection method based on similarity location and superpixel segmentation"

Fig.3

Comparison on similarity rough location results of defects using SSIM and proposed method. (a) Test images; (b) Similarity rough location results of SSIM method; (c) Similarity rough location results of proposed method"

Fig.4

Similarity rough location results of defects at different weights α. (a) Test image; (b) α=0.7; (c) α=0.8; (d) α=0.9"

Fig.5

Comparison on segmentation results of similarity rough location image in Fig.3 using global threshold(a) and superpixel segmentation(b)"

Tab.1

Calculation process of proposed method"

方法1:基于相似性定位和超像素分割的织物疵点检测方法
输入:织物图像Ir
输出:检测结果B
1:使用式(1)将待检测图像Ir进行处理得到亮度信息Iv
2:对Iv进行中值滤波和对数增强,得到增强图像I
3: 利用式(4)估计增强图像I对应的显著图Ism
4:将I分为大小相同且互不重叠的N个小块Pn(n=1,2,…,N)和 以20为步长互相重叠的M个小块Qi(i=1,2,…,M)
5:for n=1:N
6: for i=1:M
7: if Pn≠Qi
8: S(Pn,Qi)=α×SSIM(Pn,Qi)+(1-α)×GRAY(Pn,Qi)
9: end if
10: end for
11: 使用式(11)对Pn进行处理,得到相似性粗定位图像Ic
12: end for
13:对Ic进行超像素分割,并使用式(13)对其进行二值化得到Bc
14:对Bc进行连通域分析,得到检测结果B

Fig.6

Comparison of defect detection results for simple texture fabric images using different methods. (a) Simple texture fabric images; (b) Defect detection results by method of reference [7]; (c) Defect detection results by method of reference [9]; (d) Defect detection results by method of reference [15]; (e) Defect detection results of proposed method"

Fig.7

Comparison on defect detection results for complex texture fabric images using different methods. (a) Complex texture fabric images; (b) Defect detection results by method of reference [7]; (c) Defect detection results by method of reference [9]; (d) Defect detection results by method of reference [15]; (e) Defect detection results of proposed method"

Tab.2

Comparison of parameters of fabric image detection results by different methods"

检测
方法		 输入织物
图像类型		 正确检
测数量		 错误检
测数量		 TPR FPR ACC T/s
文献[7] 含疵点 55 15 78.57 64.29 57.14 3.41
不含疵点 25 45
文献[9] 含疵点 54 16 77.14 71.43 52.86 0.16
不含疵点 20 50
文献[15] 含疵点 57 13 81.43 57.14 62.14 23.45
不含疵点 30 40
本文 含疵点 68 2 97.14 1.43 97.86 4.85
不含疵点 69 1
[1] 杜帅, 李岳阳, 王孟涛, 等. 基于改进局部自适应对比法的织物疵点检测[J]. 纺织学报, 2019,40(2):44-50.
DU Shuai, LI Yueyang, WANG Mengtao, et al. Fabric defect detection based on improved local adaptive contrast method[J]. Journal of Textile Research, 2019,40(2):44-50.
[2] 王明景, 白瑞林, 何薇, 等. 图案布匹瑕疵的在线视觉检测[J]. 光电工程, 2014,41(6):19-26.
WANG Mingjing, BAI Ruilin, HE Wei, et al. Online visual inspection of pattern fabric defects[J]. Opto-Electronic Engineering, 2014,41(6):19-26.
[3] 李文羽, 程隆棣. 基于机器视觉和图像处理的织物疵点检测研究新进展[J]. 纺织学报, 2014,35(3):158-164.
LI Wenyu, CHENG Longdi. New development of fabric defect detection based on machine vision and image processing[J]. Journal of Textile Research, 2014,35(3):158-164.
[4] 胡克满, 罗少龙, 胡海燕. 应用Canny算子的织物疵点检测改进算法[J]. 纺织学报, 2019,40(1):153-158.
HU Keman, LUO Shaolong, HU Haiyan. Improved algorithm of fabric defect detection using Canny ope-rator[J]. Journal of Textile Research, 2019,40(1):153-158.
[5] LI Y, LUO H, YU M, et al. Fabric defect detection algorithm using RDPSO-based optimal Gabor filter[J]. Journal of The Textile Institute, 2018,110(4):487-495.
doi: 10.1080/00405000.2018.1489951
[6] ZHANG B, TANG C M. A method for defect detection of yarn-dyed fabric based on frequency domain filtering and similarity measurement[J]. Autex Research Journal, 2019,19(3):257-262.
doi: 10.1515/aut-2018-0040
[7] CAO J J, WANG N N, ZHANG J, et al. Detection of varied defects in diverse fabric images via modified RPCA with noise term and defect prior[J]. International Journal of Clothing Science & Technology, 2016,28(4):516-529.
[8] 祝双武, 郝重阳. 基于纹理周期性分析的织物疵点检测方法[J]. 计算机工程与应用, 2012,48(21):163-166.
ZHU Shuangwu, HAO Chongyang. Fabric defect detection method based on texture periodicity ana-lysis[J]. Computer Engineering and Applications, 2012,48(21):163-166.
[9] HOU X D, ZHANG L Q. Saliency detection: a spectral residual approach[C]// IEEE Conference on Computer Vision and Pattern Recognition. Minneapolis:IEEE, 2007: 18-23.
[10] ZHANG K B, YAN Y D, LI P F, et al. Fabric defect detection using salience metric for color dissimilarity and positional aggregation[J]. IEEE Access, 2018,6:49170-49181.
doi: 10.1109/ACCESS.2018.2868059
[11] 钱晓亮, 张鹤庆, 张焕龙, 等. 基于视觉显著性的太阳能电池片表面缺陷检测[J]. 仪器仪表学报, 2017,38(7):1570-1578.
QIAN Xiaoliang, ZHANG Heqing, ZHANG Huanlong, et al. Detection of solar cell surface defects based on visual significance[J]. Chinese Journal of Scientific Instrument, 2017,38(7):1570-1578.
[12] 景军锋, 范晓婷, 李鹏飞, 等. 应用深度卷积神经网络的色织物缺陷检测[J]. 纺织学报, 2017,38(2):68-74.
JING Junfeng, FAN Xiaoting, LI Pengfei, et al. Defect detection of dyed fabrics using deep convolution neural network[J]. Journal of Textile Research, 2017,38(2):68-74.
[13] 王理顺, 钟勇, 李振东, 等. 基于深度学习的织物缺陷在线检测算法[J]. 计算机应用, 2019,39(7):2125-2128.
WANG Lishun, ZHONG Yong, LI Zhendong, et al. On line detection algorithm of fabric defects based on deep learning[J]. Journal of Computer Applications, 2019,39(7):2125-2128.
[14] 吴志洋, 卓勇, 李军, 等. 基于卷积神经网络的单色布匹瑕疵快速检测算法[J]. 计算机辅助设计与图形学学报, 2018,30(12):2262-2270.
WU Zhiyang, ZHUO Yong, LI Jun, et al. Fast detection algorithm of single color fabric defects based on convolution neural network[J]. Journal of Computer-Aided Design & Computer Graphics, 2018,30(12):2262-2270.
[15] CAO J J, ZHANG J, WEN Z J, et al. Fabric defect inspection using prior knowledge guided least squares regression[J]. Multimedia Tools and Applications, 2017,76(3):4141-4157.
doi: 10.1007/s11042-015-3041-3
[16] ACHANTA R, HEMAMI S, ESTRADA F, et al. Frequency-tuned salient region detection[C]// 2009 IEEE Conference on Computer Vision and Pattern Recognition, Florida: IEEE, 2009: 1597-1604.
[17] ACHANTA R, SHAJI A, SMITH K, et al. SLIC superpixels compared to state-of-the-art superpixel methods[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012,34(11):2274-2282.
doi: 10.1109/TPAMI.2012.120
[18] WANG Z, BOVIK A C, SHEIKH H R, et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 2004,13(4):600-612.
doi: 10.1109/tip.2003.819861 pmid: 15376593
[1] 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.
[2] . Detection of fabric defects based on Gabor filters and Isomap [J]. JOURNAL OF TEXTILE RESEARCH, 2017, 38(03): 162-167.
[3] . Warp knit fabric defect detection method based on optimal Gabor filters [J]. JOURNAL OF TEXTILE RESEARCH, 2016, 37(11): 48-54.
[4] . Fast fabric defect detection algorithm based on integral image [J]. JOURNAL OF TEXTILE RESEARCH, 2016, 37(11): 141-147.
[5] . Fabric defects detection method based on texture saliency features [J]. JOURNAL OF TEXTILE RESEARCH, 2016, 37(10): 42-049.
[6] . Fabric defect detection using monogenic wavelet analysis [J]. JOURNAL OF TEXTILE RESEARCH, 2016, 37(09): 59-64.
[7] . Research on detection of defects in fabrics using improved singular value decomposition [J]. JOURNAL OF TEXTILE RESEARCH, 2014, 35(6): 62-0.
[8] TIAN Chengtai;BU Honggang;WANG Jun;CHEN Xia;. Fabric defect detection based on fractal feature of time series [J]. JOURNAL OF TEXTILE RESEARCH, 2010, 31(5): 44-47.
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