Journal of Textile Research ›› 2020, Vol. 41 ›› Issue (05): 72-78.doi: 10.13475/j.fzxb.20190806807

• Textile Engineering • Previous Articles     Next Articles

Detection for fabric defects based on low-rank decomposition

YANG Enjun, LIAO Yihui, LIU Andong(), YU Li   

  1. College of Information Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang 310023, China
  • Received:2019-08-27 Revised:2020-01-22 Online:2020-05-15 Published:2020-06-02
  • Contact: LIU Andong E-mail:lad@zjut.edu.cn

Abstract:

Aiming at excessive loss of image information in fabric defect detection caused by the commonly used low-rank decomposition method and the weft skew caused by fabric elasticity, an improved low-rank decomposition detection method based on Beta norm was proposed. This method starts by constructing a prior map by extracting the texton feature of the fabric image. Second, a Beta norm was used to replace the nuclear norm in the low-rank decomposition process, whereas the low-rank decomposition was guided by the prior map to decompose the fabric image. Compared with the nuclear norm, it was found that the proposed method does not lead to excessive loss of image information. Furthermore, a posterior map was constructed by extracting the HOG (histogram of oriented gradients) feature of the fabric image, and a saliency map was obtained by the Hadamard product between the posterior map and the sparse component obtained by the low-rank decomposition which can solve the skew problem caused by fabric elasticity. Finally, optimal threshold segmentation was used to obtain the defect figure. Compared with the existing four methods, the experimental results demonstrate that the proposed method can effectively suppress the skewness in the fabric and the detection time is shorter.

Key words: fabric defect, defect detection, low-rank decomposition, posterior map, Beta norm, skew interference

CLC Number: 

  • TP391

Fig.1

Flow chart of proposed method"

Fig.2

Posterior map and saliency map. (a) Segmentation result of broken end; (b) Segmentation result of netting multiple; (c) Segmentation result of hole; (d) Segmentation result of oil"

Fig.3

Detection results for star-patterned fabric image. (a) Fabric image; (b) Method of reference [15]; (c) Method of reference [18]; (d) Method of reference [19]; (e) Method of reference [20]; (f) Method of this paper; (g) Ground-truth"

Fig.4

Detection results for box-patterned fabric image. (a) Fabric image; (b) Method of reference [15]; (c) Method of reference [18]; (d) Method of reference [19]; (e) Method of reference [20]; (f) Method of this paper; (g) Ground-truth"

Fig.5

Compared results of time for single fabric image"

Tab.1

Compared results of average time"

算法来源 平均用时/s
文献[15]
文献[19]
文献[20]
本文
0.621
0.397
0.478
0.245

Fig.6

Compared results of total time for processing FID database"

Tab.2

Compared results of algorithms stability"

算法来源 标准差
文献[15]
文献[19]
文献[20]
本文
0.445
0.044
0.036
0.032
[1] TONG L, WONG W K, KWONG C K. Differential evolution-based optimal Gabor filter model for fabric inspection[J]. Neurocomputing, 2016,173:1386-1401.
doi: 10.1016/j.neucom.2015.09.011
[2] KUMAR A. Computer-vision-based fabric defect detection: a survey[J]. IEEE Transactions on Industrial Electronics, 2008,55(1):348-363.
doi: 10.1109/TIE.1930.896476
[3] 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):38-44.
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):38-44.
[4] REDDY R O K, REDDY B E, REDDY E K. Classifying similarity and defect fabric textures based on GLCM and binary pattern schemes[J]. International Journal of Information Engineering and Electronic Business, 2013,5(5):25.
doi: 10.5815/ijieeb
[5] ZHU D, PAN R, GAO W, et al. Yarn-dyed fabric defect detection based on autocorrelation function and GLCM[J]. Autex Research Journal, 2015,15(3):226-232.
doi: 10.1515/aut-2015-0001
[6] 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 for fabric defect detection based on Canny operator[J]. Journal of Textile Research, 2019,40(1):153-158.
[7] VERMAAK H, NSENGIYUMVA P, LUWES N. Using the dual-tree complex wavelet transform for improved fabric defect detection[J]. Journal of Sensors, 2016,2016:1-8.
[8] JING J, FAN X, LI P. Automated fabric defect detection based on multiple Gabor filters and KPCA[J]. International Journal of Multimedia and Ubiquitous Engineering, 2016,11(6):93-106.
[9] 李敏, 崔树芹, 谢治平. 高斯混合模型在印花织物疵点检测中的应用[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]. Journal of Textile Research, 2015,36(8):94-98.
[10] SUSAN S, SHARMA M. Automatic texture defect detection using gaussian mixture entropy modeling[J]. Neurocomputing, 2017,239:232-237.
doi: 10.1016/j.neucom.2017.02.021
[11] 刘威, 常兴治, 梁久祯, 等. 基于局部最优分析的纺织品瑕疵检测方法[J]. 模式识别与人工智能, 2018,31(2):182-189.
LIU Wei, CHANG Xingzhi, LIANG Jiuzhen, et al. Fabric defect detection based on local optimum analysis[J]. Pattern Recognition and Artificial Intelligence, 2018,31(2):182-189.
[12] JIA L, ZHANG J, CHEN S, et al. Fabric defect inspection based on lattice segmentation and lattice templates[J]. Journal of the Franklin Institute, 2018,355(15):7764-7798.
doi: 10.1016/j.jfranklin.2018.07.005
[13] JIA L, CHEN C, LIANG J, et al. Fabric defect inspection based on lattice segmentation and Gabor filtering[J]. Neurocomputing, 2017,238:84-102.
doi: 10.1016/j.neucom.2017.01.039
[14] JIA L, LIANG J. Fabric defect inspection based on isotropic lattice segmentation[J]. Journal of the Franklin Institute, 2017,354(13):5694-5738.
doi: 10.1016/j.jfranklin.2017.05.035
[15] CHETVERIKOV D. Residual of resonant SVD as salient feature [C]//International conference on computer vision and graphics. Berlin: Heidelberg, 2008: 143-153.
[16] BOUWMANS T, JAVED S, ZHANG H, et al. On the applications of robust PCA in image and video processing[J]. Proceedings of the IEEE, 2018,106(8):1427-1457.
doi: 10.1109/PROC.5
[17] 李春雷, 高广帅, 刘洲峰, 等. 应用方向梯度直方图和低秩分解的织物疵点检测算法[J]. 纺织学报, 2017,38(3):149-154.
LI Chunlei, GAO Guangshuai, LIU Zhoufeng, et al. Fabric defect detection algorithm based on histogram of oriented gradient and low-rank decomposition[J]. Journal of Textile Research, 2017,38(3):149-154.
doi: 10.1177/004051756803800207
[18] LI C, GAO G, LIU Z, et al. Defect Detection for patterned fabric images based on GHOG and low-rank decomposition[J]. IEEE Access, 2019,7:83962-83973.
doi: 10.1109/Access.6287639
[19] CAO J, ZHANG J, WEN Z, 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
[20] CAO J, WANG 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 and Technology, 2016,28(4):516-529.
doi: 10.1108/IJCST-10-2015-0117
[21] ZHU S C, GUO C E, WANG Y, et al. What are textons?[J]. International Journal of Computer Vision, 2005,62(1-2):121-143.
doi: 10.1007/s11263-005-4638-1
[22] CANDÈS E J, LI X, MA Y, et al. Robust principal component analysis?[J]. Journal of the ACM (JACM), 2011,58(3):11.
[23] KANG Z, PENG C, CHENG Q. Robust PCA via nonconvex rank approximation[C] //2015 IEEE international conference on data mining(ICDM). Atlantic: IEEE, 2015: 211-220.
[1] ZHU Lei, REN Mengfan, PAN Yang, LI Botao. Fabric defect detection based on similarity location and superpixel segmentation [J]. Journal of Textile Research, 2020, 41(10): 58-66.
[2] DI Lan, YANG Da, LIANG Jiuzhen, MA Mingyin. Fabric defect detection method based on primitive segmentation and Gabor filtering [J]. Journal of Textile Research, 2020, 41(09): 59-66.
[3] ZHOU Wenming, ZHOU Jian, PAN Ruru. Yarn-dyed fabric defect detection based on context visual saliency [J]. Journal of Textile Research, 2020, 41(08): 39-44.
[4] LU Hao, CHEN Yuan. Surface defect detection method of carbon fiber prepreg based on machine vision [J]. Journal of Textile Research, 2020, 41(04): 51-57.
[5] JING Junfeng, ZHANG Junyang, ZHANG Huanhuan, SU Zebin. Defect detection on surface of draw texturing yarn packages in gradient space [J]. Journal of Textile Research, 2020, 41(02): 44-51.
[6] 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.
[7] XIAO Zhitao, GUO Yongmin, GENG Lei, WU Jun, ZHANG Fang, WANG Wen, LIU Yanbei. Internal defect detection method for thin test pieces of woven laminated composites based on ultrasonic phased array [J]. Journal of Textile Research, 2019, 40(11): 81-87.
[8] ZHU Hao, DING Hui, SHANG Yuanyuan, SHAO Zhuhong. Defect detection algorithm for multiple texture hierarchical fusion fabric [J]. Journal of Textile Research, 2019, 40(06): 117-124.
[9] DU Shuai, LI Yueyang, WANG Mengtao, LUO Haichi, JIANG Gaoming. Fabric defect detection based on improved local adaptive contrast method [J]. Journal of Textile Research, 2019, 40(02): 38-44.
[10] HU Keman, LUO Siolong, HU Haiyan. Improved algorithm for fabric defect detection based on Canny operator [J]. Journal of Textile Research, 2019, 40(01): 153-158.
[11] . Fabric defect inspection based on modified discriminant complete local binary pattern and lattice segmentation [J]. Journal of Textile Research, 2018, 39(09): 57-64.
[12] . Segmentation of fabric defect images based on improved frequency-tuned salient algorithm [J]. JOURNAL OF TEXTILE RESEARCH, 2018, 39(05): 125-131.
[13] . Application of algorithm with improved frequency-tuned salient region [J]. JOURNAL OF TEXTILE RESEARCH, 2018, 39(03): 154-160.
[14] . Fabric defect detection based on relative total variation model and adaptive mathematical morphology [J]. JOURNAL OF TEXTILE RESEARCH, 2017, 38(05): 145-149.
[15] . Detection of fabric defects based on Gabor filters and Isomap [J]. JOURNAL OF TEXTILE RESEARCH, 2017, 38(03): 162-167.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!