Inspection of remaining yarn on bobbin based on odd Gabor filters

被引:0
|
作者
Gao C. [1 ]
Liu J. [1 ]
机构
[1] Key Laboratory of Eco-Textiles(Jiangnan University), Ministry of Education, Wuxi, 214122, Jiangsu
来源
Liu, Jihong (liujihongtex@hotmail.com) | 2018年 / China Textile Engineering Society卷 / 39期
关键词
Bobbin sorting; Combined spinning and winding frame; Edge detection; Gabor filter; Maximum response;
D O I
10.13475/j.fzxb.20170906705
中图分类号
学科分类号
摘要
In order to avoid the friction caused by mechanical remaining yarn inspecting devices in combined spinning and winding frames and preserve the quality of surface yarn around bobbins, a non-contact algorithm based on computer vision designed for bobbin inspection was introduced. Each bobbin tube was detected and segmented from a single frame. The interest region(ROI) of a single bobbin was then selected from the background and adjusted to vertical state. An optimized odd Gabor filter group was implemented on the ROI to enhance boundaries between the tube and yarn. Finally, the yarn region was examined and confirmed by connectivity checking. In the next section, the influence of ROI extraction threshold on detection accuracy was analyzed by experiments. The optimization of determining rule of filter parameters was elaborated based on maximum output principle. The results show that the odd Gabor filter with the ratio of wavelength to standard deviation of 4.808, the ratio of standard deviation to diameter of 0.81and the direction parallel to the yarn has the best effect on remaining yarn inspection. Copyright No content may be reproduced or abridged without authorization.
引用
收藏
页码:138 / 142
页数:4
相关论文
共 10 条
  • [1] Li F., The design proposal of automatic bobbin sorting machine, Electronic Instrumentation Customer, 19, 1, pp. 28-30, (2012)
  • [2] Liu Z., Song L., The study on the color-coded system of automatical captivity tube machine, Electronic Instrumentation Customer, 19, 6, pp. 30-32, (2012)
  • [3] Zhang M., Ding X., Li X., Neural network based color recognition for bobbin sorting machine, Telkomnika, 11, 7, pp. 3728-3735, (2013)
  • [4] Wang X., Zhao Y., Liao M., Et al., Automatic segmentation for retinal vessel based on multi-scale 2D gabor wavelet, Acta Automatica Sinica, 5, pp. 970-980, (2015)
  • [5] Sandler R., Lindenbaum M., Optimizing Gabor filter design for texture edge detection and classifica-tion, International Journal of Computer Vision, 84, 3, pp. 308-324, (2009)
  • [6] Wang C., Hu F., Xu Q., Et al., Detection of fabric defects based on Gabor filters and isomap, Journal of Textile Research, 38, 3, pp. 162-167, (2017)
  • [7] Wei M., Li Y., Jiang G., Et al., Warp knit fabric defect detection method based on optimal Gabor filter, Journal of Textile Research, 37, 11, pp. 48-54, (2016)
  • [8] Bissi L., Patch based yarn defect detection using Gabor filters, Instrumentation and Measurement Technology Conference (I2MTC), pp. 240-244, (2012)
  • [9] Yao C., Bai X., Liu W., Et al., Detecting texts of arbitrary orientations in natural images, Computer Vision and Pattern Recognition (CVPR), pp. 1083-1090, (2012)
  • [10] Fu Y., Li Z., Yuan D., Design of optimized Gabor filter for multi-edges detection, Journal of Zhejiang University (Engineering Science), 38, 7, pp. 839-844, (2004)