Defect detection algorithm for multiple texture hierarchical fusion fabric

被引：0
|
作者：
Zhu H. [1 ]
Ding H. [1 ,2 ,3 ]
Shang Y. [1 ,2 ,3 ]
Shao Z. [1 ,2 ,4 ]
机构：
[1] College of Information Engineering, Capital Normal University, Beijing
[2] Beijing Engineering Research Center of High Reliable Embedded System, Beijing
[3] Beijing Advanced Innovation Center for Imaging Theory and Technology, Beijing
[4] Collaborative Innovation Center for Mathematics and Information of Beijing, Beijing
来源：
Fangzhi Xuebao/Journal of Textile Research | 2019年 / 40卷 / 06期
关键词：
Defect detection; Fabric texture; Feature fusion; Local phase quantization feature; Tamura feature;
D O I：
10.13475/j.fzxb.20180704708
中图分类号：
学科分类号：
摘要：
Aiming at the false detection and missing detection caused by complexity and diversity of texture distribution in fabric defect detection, considering the periodicity of fabric texture, an algorithm of multi-texture gradation fusion for fabric defect detection was proposed. In the process of testing, firstly, the defect region was subjected to primary positioning and self-adaptive growth by using the Tamura roughness graph of the fabric defect image, then the primary positioned region was mapped to the original fabric image. The primary positioned region was blocked and the local phase quantization (LPQ) texture feature and Tamura texture feature of each image block were extracted, and the two different texture features were fused. The similarity between the fusion feature and the normal block feature was calculated to obtain the similarity image. Finally, the longitude and latitude feature map and the similarity feature map were fused to find the region of the defects in fabric images. The experimental results on TILDA dataset show that the new approach can reduce the redundancy of defect detection and improve the detection efficiency, and can avoid errors and omissions in the process of defect detection. Copyright No content may be reproduced or abridged without authorization.
引用
收藏
页码：117 / 124
页数：7
相关论文
共 18 条
  • [1] Srinivasan K., Dastoor P.H., Radhakrishnaiah P., Et al., FDAS: A knowledge-based framework for analysis of defects in woven textile structures, Journal of the Textile Institute, 83, 3, pp. 431-448, (1992)
  • [2] Liu B., Zhang H., Image retrieval algorithm based on convolutional neural network and manifold ranking, Journal of Computer Applications, 36, 2, pp. 531-534, (2016)
  • [3] Chan C.H., Pang G.K.H., Fabric defect detection by Fourier analysis, IEEE Transactions on Industry Applications, 36, 5, pp. 1267-1276, (2000)
  • [4] Mak K.L., Peng P., Yiu K.F.C., Fabric defect detection using morphological filters, Image & Vision Computing, 27, 10, pp. 1585-1592, (2009)
  • [5] Chetverikov D., Hanbury A., Finding defects in texture using regularity and local orientation, Pattern Recognition, 35, 10, pp. 2165-2180, (2002)
  • [6] Alata O., Ramananjarasoa C., Unsupervised textured image segmentation using 2-D quarter plane autoregressive model with four prediction supports, Pattern Recognition Letters, 26, 8, pp. 1069-1081, (2005)
  • [7] Du L., Li L., Wang J., Et al., Comparison of several fabric defect detection methods based on image adaptive threshold segmentation, Journal of Textile Research, 35, 6, pp. 56-60, (2014)
  • [8] Ngna H.Y.T., Pang G.K.H., Yung S.P., Et al., Wavelet based methods on patterned fabric defect detection, Pattern Recognition, 38, 4, pp. 559-576, (2005)
  • [9] Vermaak H., Nsengiyumval P., Luwes N., Using the dual-tree complex wavelet transform for improved fabric defect detection, Journal of Sensors, 2016, 7, pp. 1-8, (2016)
  • [10] Bissi L., Baruff G., Placidip, Et al., Automated defect detection in uniform and structured fabrics using Gabor filters and PCA, Journal of Visual Communication and Image Representation, 24, 7, pp. 838-845, (2013)
12