Application of computer vision technology in woven fabric structural parameters testing

YUAN Xiuwen; LUO Jian; XIN Binjie; XU Yingqi

doi:10.12299/jsues.21-0290

Volume 37 Issue 1

Mar. 2023

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Article Navigation > Journal of Shanghai University of Engineering Science > 2023 > 37(1): 7-11

YUAN Xiuwen, LUO Jian, XIN Binjie, XU Yingqi. Application of computer vision technology in woven fabric structural parameters testing[J]. Journal of Shanghai University of Engineering Science, 2023, 37(1): 7-11. doi: 10.12299/jsues.21-0290

Citation:

YUAN Xiuwen, LUO Jian, XIN Binjie, XU Yingqi. Application of computer vision technology in woven fabric structural parameters testing[J]. Journal of Shanghai University of Engineering Science, 2023, 37(1): 7-11. doi: 10.12299/jsues.21-0290

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Application of computer vision technology in woven fabric structural parameters testing

doi: 10.12299/jsues.21-0290

School of Textiles and Fashion, Shanghai University of Engineering Science, Shanghai 201620, China

Received Date: 2021-12-12
Publish Date: 2023-03-31

Abstract

Abstract

The two structural parameters of woven fabric density and weave pattern are important data in textile testing, and their testing indicators determine the quality of woven fabric products. Due to the complexity of fabric structure, the testing of woven fabric structure parameters nowadays still depends on manual analysis. With the development of computer vision technology, its application in the testing of structural parameters of woven fabrics has been made new research progress. The research status of automatic testing of two important structural parameters of woven fabric density and weave pattern at home and abroad in recent five years was described in detail, and the shortcomings of the research were summarized. Finally, it is concluded that the computer vision technology based on objective evaluation has a great development prospect in the field of woven fabric structure parameter testing.
- woven fabric,
- fabric density,
- weave pattern,
- textile testing,
- computer vision

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References(25)

References

[1]	周建, 潘如如, 高卫东. 机器视觉在纺织中的应用现状与发展趋势[J] . 棉纺织技术,2019,47(2):15 − 17. doi: 10.3969/j.issn.1001-7415.2019.02.004
[2]	张雷. 数字图像处理技术在纺织检测中的研究和应用[J] . 大众标准化,2020,325(14):217 − 218. doi: 10.3969/j.issn.1007-1350.2020.14.107
[3]	谭园园. 计算机图像处理技术在纺织工业的应用与分析[J] . 轻纺工业与技术,2020,49(10):111 − 112. doi: 10.3969/j.issn.2095-0101.2020.10.047
[4]	辛斌杰, 余序芬, 吴兆平. 应用图像分析技术自动识别织物的组织结构[J] . 东华大学学报 (自然科学版),2011,37(1):35 − 41. XIN B J, Yu X F, Wu Z P. Automatic Identifying the Woven Fabric Pattern by Image Analysis Technique[J]. Journal of Donghua University(Natural Science), 2011, 37(01): 35-41.
[5]	洪乾耀. 基于图像处理的机织物密度自动检测方法研究[D]. 杭州: 浙江理工大学, 2015.
[6]	辛斌杰, 余序芬, 吴兆平. 机织物经纬密测量的图像处理技术[J] . 中国纺织大学学报,1999(3):34 − 37.
[7]	ESCOFET J, MILLÁN MARÍA S, M RALLÓ. Modeling of woven fabric structures based on Fourier image analysis[J] . Applied Optics,2001,40(34):6170 − 6176. doi: 10.1364/AO.40.006170
[8]	AJALLOUIAN F, TAVANAI H, PALHANG M, et al. A novel method for the identification of weave repeat through image processing[J] . Journal of the Textile Institute,2009,100(3):195 − 206. doi: 10.1080/00405000701660244
[9]	XIN B J, HU J L, BACIU G, et al. Investigation on the classification of weave pattern based on an active grid model[J] . Textile Research Journal,2009,79(12):1123 − 1134.
[10]	PAN R R, GAO W D, LI Z J, et al. Measuring thread densities of woven fabric using the fourier transform[J] . Fibres & Textiles in Eastern Europe,2015,23(1):35 − 40.
[11]	ZHANG J, PAN R R, GAO W D. Automatic inspection of density in yarn-dyed fabrics by utilizing fabric light transmittance and Fourier analysis[J] . Applied Optics,2015,54(4):966 − 972. doi: 10.1364/AO.54.000966
[12]	ZHANG J, PAN R R, WANG J A, et al. An efficient method for density measurement for high-tightness woven fabrics[J] . Textile Research Journal,2017,87(3):329 − 339. doi: 10.1177/0040517516629147
[13]	ZHANG R, XIN B J. An investigation of density measurement method for yarn-dyed woven fabrics based on dual-side fusion technique[J] . Measurement Science and Technology,2016,27(8):085403. doi: 10.1088/0957-0233/27/8/085403
[14]	XIANG Z, CHEN K F, QIAN M, et al. Yarn-dyed woven fabric density measurement method and system based on multi-directional illumination image fusion enhancement technology[J] . Journal of the Textile Institute,2020,111(10):1489 − 1501. doi: 10.1080/00405000.2019.1706222
[15]	WU J, ZHONG P, LING J Y, et al. Design method of fabric density sensor based on the virtual grating with gradual constant[J] . IEEE Access,2019,7:160345 − 160362. doi: 10.1109/ACCESS.2019.2951233
[16]	SCHNEIDER D, GLOY Y S, MERHOF D. Vision-based on-loom measurement of yarn densities in woven fabrics[J] . IEEE Transactions on Instrumentation and Measurement,2015,64(4):1063 − 1074. doi: 10.1109/TIM.2014.2363580
[17]	SCHNEIDER D, MERHOF D. Blind weave detection for woven fabrics[J] . Pattern Analysis and Applications,2015,18(3):725 − 737. doi: 10.1007/s10044-014-0403-9
[18]	张瑞. 基于双面成像技术的色织物组织识别研究[D]. 上海: 上海工程技术大学, 2016.
[19]	ZHANG J, WANG J A, PAN R R, et al. A computer vision-based system for automatic detection of misarranged warp yarns in yarn-dyed fabric. Part I: Continuous segmentation of warp yarns[J] . Journal of the Textile Institute,2018,109(5):577 − 584.
[20]	ZHANG J, WANG J A, PAN R R. A computer vision-based system for automatic detection of misarranged warp yarns in yarn-dyed fabric. Part II: Warp region segmentation[J] . Journal of the Textile Institute,2019,110(9):1359 − 1367.
[21]	WANG J A, ZHANG J, WANG L, et al. A computer vision-based system for automatic detection of misarranged color warp yarns in yarn-dyed fabric. Part III: Yarn layout proofing[J] . Journal of the Textile Institute,2020,111(11):1614 − 1622.
[22]	MENG S, PAN R R, GAO W D, et al. A multi-task and multi-scale convolutional neural network for automatic recognition of woven fabric pattern[J] . Journal of Intelligent Manufacturing,2021,32(4):1147 − 1161. doi: 10.1007/s10845-020-01607-9
[23]	SABUNCU M, OZDEMIR H. Recognition of fabric weave patterns using opticalcoherence tomography[J] . Journal of the Textile Institute,2016,107(11):1406 − 1411. doi: 10.1080/00405000.2015.1114791
[24]	SABUNCU M, OZDEMIR H, AKDOGAN M U. Automatic identification of weavepatterns of checked and colored fabrics using optical coherence tomography[J] . IEEE Photonics Journal,2017,9(5):1 − 8.
[25]	孟朔, 夏旭文, 潘如如, 等. 基于卷积神经网络的机织物密度均匀性检测[J] . 纺织学报,2021,42(2):101 − 106. doi: 10.13475/j.fzxb.20201008406