Abstract To solve the problem of tensile deformation of periodic fabric, a fabric defect detection method based on template correction and low-rank decomposition is proposed. Firstly, the original image is corrected by the template to reduce the influence of stretching deformation on the detection results. Then, a low-rank correction decomposition model is proposed including a low-rank term, sparse term and correction term. The model can be solved by the alternating direction method to generate a low-rank matrix and a sparse matrix. Finally, the optimal threshold segmentation algorithm is utilized to segment the significant images generated by the sparse matrix. Experiments on standard databases show that the recall rate of the proposed method is improved.
Fund:Supported by National Natural Science Foundation of China( No.61170121)
Corresponding Authors:
LIANG Jiuzhen, Ph.D., professor. His research interests include computer vision.
About author:: (JI Xuan, master student. His research interests include computer vision.) (HOU Zhenjie, Ph.D., professor. His research interests include computer vision.) (CHANG Xingzhi, Ph.D., associate senior engineer. His research interests include computer vision.) (LIU Wei, master student. His research interests include computer vision.)
[1] AZIZ M A, HAGGAG A S, SAYED M S. Fabric Defect Detection Algorithm Using Morphological Processing and DCT // Proc of the 1st International Conference on Communications, Signal Processing, and Their Applications. Washington, USA: IEEE, 2013. DOI: 10.1109/ICCSPA.2013.6487269. [2] YANG X Z, PANG G K H, YUNG N H C. Discriminative Fabric Defect Detection Using Adaptive Wavelets. Optical Engineering, 2002, 41(12): 3116-3126. [3] MAK K L, PENG P. An Automated Inspection System for Textile Fabrics Based on Gabor Filters. Robotics and Computer-Integrated Manufacturing, 2008, 24(3): 359-369. [4] OZDEMIR S, ERCIL A. Markov Random Fields and Karhunen-Loeve Transforms for Defect Inspection of Textile Products // Proc of the IEEE Conference on Emerging Technologies and Factory Automation. Washington, USA: IEEE, 1996: 697-703. [5] ZHOU J, SEMENOVICH D, SOWMYA A, et al. Sparse Dictionary Reconstruction for Textile Defect Detection // Proc of the 11th International Conference on Machine Learning and Applications. Wa-shington, USA: IEEE, 2013: 21-26. [6] NGAN H Y T, PANG G K H, YUNG S P, et al. Wavelet Based Methods on Patterned Fabric Defect Detection. Pattern Recognition, 2005, 38(4): 559-576. [7] TSANG C S C, NGAN H Y T, PANG G K H. Fabric Inspection Based on the ELO Rating Method. Pattern Recognition, 2016, 51(4): 378-394. [8] NGAN H Y T, PANG G K H. Novel Method for Patterned Fabric Inspection Using Bollinger Bands. Optical Engineering, 2006, 45(8). DOI: 10.1117/1.2345189. [9] NGAN H Y T, PANG G K H. Regularity Analysis for Patterned Texture Inspection. IEEE Transactions on Automation Science and Engineering, 2009, 6(1): 131-144. [10] 梁久祯,顾程熙,常兴治.基于相似关系的纺织品瑕疵检测方法.模式识别与人工智能, 2017, 30(5): 456-464. (LIANG J Z, GU C X, CHANG X Z. Fabric Defect Detection Based on Similarity Relation. Pattern Recognition and Artificial Intelligence, 2017, 30(5): 456-464.) [11] JIA L, CHEN C, LIANG J Z, et al. Fabric Defect Inspection Based on Lattice Segmentation and Gabor Filtering. Neurocompu-ting, 2017, 238: 84-102. [12] JIA L, LIANG J Z. Fabric Defect Inspection Based on Isotropic Lattice Segmentation. Journal of the Franklin Institute, 2017, 354(13): 5694-5738. [13] 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.模式识别与人工智能, 2018, 31(2): 182-189. (LIU W, CHANG X Z, LIANG J Z, et al. Fabric Defect Detection Based on Local Optimum Analysis. Pattern Recognition and Artificial Intelligence, 2018, 31(2): 182-189.) [14] GAO G S, ZHANG D, LI C L, et al. A Novel Patterned Fabric Defect Detection Algorithm Based on GHOG and Low-Rank Recovery // Proc of the 13th IEEE International Conference on Signal Processing. Washington, USA: IEEE, 2017: 1118-1123. [15] ZHENG Z L, YU M D, JIA J, et al. Fisher Discrimination Based Low Rank Matrix Recovery for Face Recognition. Pattern Recognition, 2014, 47(11): 3502-3511. [16] 刘洲峰,王九各,赵全军,等.基于改进自适应阈值的织物疵点检测算法研究.微型机与应用, 2013, 32(10): 38-40, 47.) (LIU Z F, WANG J G, ZHAO Q J, et al. Research on Fabric Defect Detection Algorithm Based on Improved Adaptive Threshold. Microcomputer and Its Applications, 2013, 32(10): 38-40, 47.) [17] CHANG X Z, GU C X, LIANG J Z, et al. Fabric Defect Detection Based on Pattern Template Correction[J/OL]. [2018-09-25]. https://doi.org/10.1155/2018/3709821. [18] CANDS E J, LI X D, MA Y, et al. Robust Principal Component Analysis? Journal of the ACM, 2009, 58(3). DOI: 10.1145/1970392.1970395. [19] 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. International Journal of Clothing Science and Technology, 2016, 28(4): 516-529. [20] CAI J F, CANDS E J, SHEN Z W. A Singular Value Thresho-lding Algorithm for Matrix Completion. SIAM Journal on Optimization, 2010, 20(4): 1956-1982. [21] NG M K, NGAN H Y T, YUAN X M, et al. Patterned Fabric Inspection and Visualization by the Method of Image Decomposition. IEEE Transactions on Automation Science and Engineering, 2014, 11(3): 943-947.
2019, Vol. 32 
