Journal of Textile Research ›› 2020, Vol. 41 ›› Issue (02): 44-51.doi: 10.13475/j.fzxb.20190401708

• Textile Engineering • Previous Articles     Next Articles

Defect detection on surface of draw texturing yarn packages in gradient space

JING Junfeng(), ZHANG Junyang, ZHANG Huanhuan, SU Zebin   

  1. School of Electronics and Information, Xi'an Polytechnic University, Xi'an, Shaanxi 710048, China
  • Received:2019-04-04 Revised:2019-10-19 Online:2020-02-15 Published:2020-02-21

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Abstract:

A method based on the difference of image information entropy and energy distribution in gradient space was proposed to solve the low efficiency and high missing rate of manual defect detection on the surface of DTY (draw texturing yarn) packages in industrial production. Firstly, the image acquisition device based on machine vision was designed to acquire the surface image of DTY packages during transmission. Then, the image of DTY packages was transformed into gradient space domain, and a combination feature of information entropy and energy was constructed to characterize the defect. Appropriate threshold was selected to distinguish the defect area from the normal area. Finally, the final detection result was obtained by morphological processing. The experimental results show that the method has a good detection effect for the defects such as stain, indentation and hairiness on the surface of DTY packages. The accuracy of defect recognition method is high and the speed is fast, which meets the requirements of the factory for accuracy and real-time detection, and realizes the automatic detection of defects on the surface of the DTY packages.

Key words: machine vision, DTY packages, defect detection, gradient space, image information entropy

CLC Number: 

  • TP391.4

Fig.1

Image acquisition device for DTY packages"

Fig.2

Flow chart for surface defect detection of DTY packages"

Fig.3

Upper surface original images of DTY packages. (a)Normal DTY packages;(b)Combination of defects; (c)Stain;(d)Indentation;(e)Hairiness"

Fig.4

Effect of image preprocessing"

Fig.5

Processing effect of DTY packages image. (a)Edge detection;(b)Region of interest extraction; (c) Expansion map of polar coordinate transformation; (d) Image in gradient space"

Tab.1

Distribution of image information entropy of DTY packages"

编号 列1 列2 列3 列4 列5 列6 列7 列8 列9 列10
行1 5.240 8 5.202 1 5.286 6 5.246 1 5.260 7 5.277 8 5.283 1 5.286 3 5.118 9 5.299 5
行2 4.232 7 4.325 3 4.313 7 4.264 1 4.230 9 4.304 8 4.257 5 4.285 5 4.334 8 4.234 5
行3 3.903 7 4.032 1 4.195 2 4.350 3 4.231 7 4.348 7 4.259 6 4.268 3 4.277 2 4.273 2
行4 3.815 1 4.064 2 4.294 8 4.250 4 4.288 7 4.172 6 4.285 8 4.370 3 4.392 8 6.483 0
行5 3.954 9 4.179 4 4.352 3 4.338 0 3.914 6 3.983 5 4.130 2 3.946 0 4.250 5 6.522 3
行6 3.976 0 3.924 2 4.198 3 6.731 2 4.336 8 4.217 5 3.981 6 4.145 0 4.283 8 6.926 1
行7 3.866 7 3.840 3 3.921 4 3.886 9 3.962 8 6.301 5 7.156 9 3.719 1 4.280 8 6.536 8
行8 4.022 5 4.075 9 3.806 8 3.914 6 3.943 2 7.855 4 7.228 3 3.274 4 3.925 9 3.949 7
行9 4.200 1 4.261 3 4.223 6 3.928 6 4.103 4 3.977 4 3.970 5 3.967 1 7.301 8 3.842 3
行10 4.290 7 4.306 9 4.280 2 4.237 3 4.323 3 3.861 0 3.794 4 3.997 1 8.343 2 4.321 4

Fig.6

Energy diagram of DTY packages"

Fig.7

Detection result of DTY packages defects"

Tab.2

Statistics of defect detection results of DTY packages"

指标 正常无
缺陷		 异常有缺陷 合计
污渍 压痕 起毛 组合缺陷
数量/个 300 500 500 500 200 2 000
正确检测数/个 300 498 487 493 199 1 977
漏检数目/个 0 2 13 7 1 23
检测准确率/% 98.85
检测漏检率/% 1.35
检测时间/ms 87.00

Tab.3

Comparison of experimental results"

检测方法 检测准确率/% 检测漏检率/% 检测时间/ms
Gabor滤波法 98.00 2.18 1 036
显著性检测法 90.15 9.24 2 153
迟滞阈值法 96.40 4.06 248
自编码器 96.00 4.18 142
本文方法 98.85 1.35 87

Fig.8

Enlarged images and comparison of result of defective region for DTY packages. (a)Manual labeling image;(b)Detection result for proposed method;(c)Comparison of results;(d) Detection result for Gabor filter;(e)Comparison of results 1;(f) Detection result for saliency;(g)Comparison of results 2;(h)Detection result for hysteresis thresholding segmentation;(i)Comparison of results 3;(j)Detection result for autoencoder;(k)Comparison of results 4"

Tab.4

Comparison of accurate evaluation results"

检测方法 RD RTD 精确
率/%		 召回
率/%		 调和平
均数/%
Gabor滤波法 54 020 38 790 71.81 67.68 69.68
显著性检测法 32 509 27 394 84.27 47.80 61.00
迟滞阈值法 47 377 44 139 93.17 77.02 84.33
自编码器 50 257 46 783 93.09 81.63 86.98
本文方法 56 238 55 362 98.44 96.60 97.51
[1] WHITTAKER C M. The dyeing of viscose rayon yarn in cake form[J]. Coloration Technology, 2010,60(5):109-114.
[2] MACHALABA N, RODIONOV I. Development issues of the Russian chemical-fiber industry[J]. Fibre Chemistry, 2015,47(4):1-9.
[3] 景军锋, 郭根. 基于机器视觉的丝饼毛羽检测[J]. 纺织学报, 2019,40(1):147-152.
JING Junfeng, GUO Gen. Yarn packages hairiness detection based on machine vision[J]. Journal of Textile Research, 2019,40(1):147-152.
[4] 汤勃, 孔建益, 伍世虔. 机器视觉表面缺陷检测综述[J]. 中国图象图形学报, 2017,22(12):1640-1663.
TANG Bo, KONG Jianyi, WU Shiqian. Review of surface defect detection based on machine vision[J]. Journal of Image and Graphics, 2017,22(12):1640-1663.
[5] MOVAFEGHI A, MOHAMMADZADEH N, YAHAGHI E, et al. Defect detection of industrial radiography images of ammonia pipes by a sparse coding model[J]. Journal of Nondestructive Evaluation, 2018,37(1):1-7.
[6] JING Junfeng, CHEN Shan, LI Pengfei. Fabric defect detection based on golden image subtraction[J]. Coloration Technology, 2016,133(1):26-39.
[7] 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. Solar cell surface defect detection based on visual saliency[J]. Chinese Journal of Scientific Instrument, 2017,38(7):1570-1578.
[8] SELVER M, AVŞAR V, ÖZDEMIR H . Textural fabric defect detection using statistical texture transformations and gradient search[J]. Journal of the Textile Institute, 2014,105(9):998-1007.
[9] 张帆, 张团善, 冀永乐, 等. 基于机器视觉的纺纱管颜色分拣算法研究[J]. 西安工程大学学报, 2018,32(5):560-566.
ZHANG Fan, ZHANG Tuanshan, JI Yongle, et al. Research on color sorting algorithm of spinning tube based on machine vision[J]. Journal of Xi'an Polytechnic University, 2018,32(5):560-566.
[10] LI Chunlei, GAO Guangshuai, LIU Zhoufeng, et al. Fabric defect detection based on biological vision modeling[J]. IEEE Access, 2018,6(1):27659-27670.
[11] ZHAO Shiwei, ZHANG Nan, ZHOU Xiaowen, et al. Particle shape effects on fabric of granular random packing[J]. Powder Technology, 2017,310(1):175-186.
[12] KANG Xuejuan, ZHANG Erhu. A universal defect detection approach for various types of fabrics based on the Elo-rating algorithm of the integral image[J]. Textile Research Journal, 2019,89(21/22):1-28.
[13] YE Guodong, PAN Chen, HUANG Xiaoling, et al. A chaotic image encryption algorithm based on information entropy[J]. International Journal of Bifurcation and Chaos, 2018,28(1):1850010-1850021.
[14] 包晓敏, 汪亚明, 罗一平, 等. 基于最大多符号信息熵的织物图像匹配[J]. 纺织学报, 2005,26(2):69-71.
BAO Xiaomin, WANG Yaming, LUO Yiping, et al. Matching of textile image based on the biggest multi-symbol information entropy[J]. Journal of Textile Research, 2005,26(2):69-71.
[15] WANG Yong, QIAN Guangzhao. Novel approach for InSAR sensors imaging via gradient-based algorithm for the sparse signal reconstruction[J]. IEEE Sensors Journal, 2018,18(6):2385-2394.
[16] 张波, 汤春明. 基于相对总变差模型与自适应形态学的织物瑕疵检测[J]. 纺织学报, 2017,38(5):145-149.
ZHANG Bo, TANG Chunming. Fabric defect detection based on relative total variation model and adaptive mathematical morphology[J]. Journal of Textile Research, 2017,38(5):145-149.
[17] HAIRER E, LUBICH C. Energy-diminishing integration of gradient systems[J]. IMA Journal of Numerical Analysis, 2014,34(2):452-461.
[18] ANWAR S, ABDULLAH M. Micro-crack detection of multicrystalline solar cells featuring an improved anisotropic diffusion filter and image segmentation technique[J]. EURASIP Journal on Image and Video Processing, 2014,2014(1):1-17.
[19] 黄森林, 王耀南, 彭玉, 等. 基于迟滞阈值分割的瓶口缺陷检测方法[J]. 电子测量与仪器学报, 2017,31(8):1289-1296.
HUANG Senlin, WANG Yaonan, PENG Yu, et al. Bottle mouth defect detection method based on hysteresis thresholding segmentation[J]. Journal of Electronic Measurement and Instrumentation, 2017,31(8):1289-1296.
[20] 张宏伟, 汤文博, 李鹏飞, 等. 基于去噪卷积自编码器的色织衬衫裁片缺陷检测[J]. 纺织高校基础科学学报, 2019,32(2):119-125.
ZHANG Hongwei, TANG Wenbo, LI Pengfei, et al. Defect detection and location of yarn-dyed shirt piece based on denoising convolutional autoencoder[J]. Basic Sciences Journal of Textile Universities, 2019,32(2):119-125.
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