Journal of Textile Research ›› 2020, Vol. 41 ›› Issue (02): 69-76.doi: 10.13475/j.fzxb.20181201008

• Textile Engineering • Previous Articles     Next Articles

Method for detecting fluff quality of fabric surface based on BP neural network

JIN Shoufeng1(), LIN Qiangqiang1, MA Qiurui2, ZHANG Hao1   

  1. 1. College of Mechanical and Electrical Engineering, Xi'an Polytechnic University, Xi'an, Shaanxi 710048,China
    2. College of Apparel & Art Design, Xi'an Polytechnic University, Xi'an, Shaanxi 710048, China
  • Received:2018-12-06 Revised:2019-11-20 Online:2020-02-15 Published:2020-02-21

RichHTML

1

PDF (PC)

68

Abstract:

In order to evaluate objectively the fluff state of fabric surface after the raising process, a method for detecting the surface fluff quality of the fabric based on BP neural network was proposed. The fluff contour image was collected by the principle of optical imaging, and the fluff region was segmented by adaptive image segmentation method. The Freeman chain code principle was applied to the obtained binary image to extract the upper edge contour coordinates of the fabric, and the BP neural network was trained as an input of the BP neural network. The two sets of weights obtained through the training were verified according to the calculation process of the BP neural network, and the method of applying the activation function and the weight of the training combined with the direct calculation was proposed. Applying the fluff fabric detection platform built on the principle of optical imaging, the fabrics processed by four different colors and different fluffing processes were tested. The detection accuracy rate reached 93.02%, and the calculation result of the weight is shown consistent with the actual calculation result of the network. This suggests that the weight of the network training may be used directly for the matrix calculation, which can shorten the actual detection time.

Key words: fabric with fluff, machine vision, BP neural network, optical imaging, raising process

CLC Number: 

  • TN911.73

Fig.1

Principle of light-cut imaging"

Fig.2

Tangential image of fabric"

Fig.3

Grayscale histogram"

Fig.4

Fabric binarization image"

Fig.5

Coordinate extraction of fabric fluff contour edge"

Fig.6

Three-layer BP neural network"

Tab.1

Neural network model analysis"

训练函数 隐含层 输出层 训练时间/s 准确率/%
traingd Log-Sigmoid Log-Sigmoid 1 41.86
Tan-Sigmoid 12 86.05
purelin 13 83.72
Tan-Sigmoid
Log-Sigmoid 1 53.49
Tan-Sigmoid 11 93.02
purelin 4 83.72
traingdm Log-Sigmoid Log-Sigmoid 1 53.49
Tan-Sigmoid 1 60.47
purelin 12 88.37
Tan-Sigmoid
Log-Sigmoid 1 55.81
Tan-Sigmoid 1 60.47
purelin 1 30.23
traingdx Log-Sigmoid Log-Sigmoid 1 55.81
Tan-Sigmoid 1 55.81
purelin 1 88.37
Tan-Sigmoid
Log-Sigmoid 1 72.09
Tan-Sigmoid 1 60.47
purelin 1 86.05
trainb Log-Sigmoid Log-Sigmoid 2 41.84
Tan-Sigmoid 1 60.47
purelin 30 48.84
Tan-Sigmoid
Log-Sigmoid 1 44.84
Tan-Sigmoid 1 55.81
purelin 1 37.21
trainscg Log-Sigmoid Log-Sigmoid 1 67.44
Tan-Sigmoid 1 90.70
purelin 1 74.42
Tan-Sigmoid
Log-Sigmoid 1 76.74
Tan-Sigmoid 1 86.05
purelin 2 88.37
trainr Log-Sigmoid Log-Sigmoid 14 74.42
Tan-Sigmoid 10 83.72
purelin 11 81.40
Tan-Sigmoid
Log-Sigmoid 13 88.37
Tan-Sigmoid 7 83.72
purelin 11 76.74

Fig.7

Part of sample data set. (a)Production inspection of unqualified fabrics; (b)Production inspection qualified fabric"

Fig.8

Actual and predicted values for different combinations. (a)Combination 1; (b)Combination 2; (c)Combination 3;(d)Combination 4"

Fig.9

Fabric after raising process. (a) First raising a#;(b) Final raising b#; (c) Final raising c#;(d) Final raising d#"

Tab.2

Comparison of test results"

织物
类型		 期望输
出值		 BP神经网络
实际输出值		 本文方法
评判结果		 人工
评判
a# 0 0.192 4 不合格 不合格
b# 1 0.987 0 合格 合格
c# 1 0.966 5 合格 合格
d# 1 0.900 5 合格 合格

Tab.3

BP neural network weight verification results"

-0.080 0 -0.021 0 -0.037 0 0.079 5 0.003 5 … -0.007 0 0.059 9
-0.037 0 -0.067 0 0.055 6 0.011 4 -0.069 0 -0.052 0 0.009 0
wmi隐含层权值 -0.040 0 -0.064 0 -0.074 0 -0.040 0 -0.025 0 0.031 3 -0.001 0
?
-0.138 0 -0.021 0 0.083 6 -0.016 0.016 5 0.048 3 0.030 9
b1隐含层阈值 1.473 4 1.222 4 -1.076 0 0.788 0 0.660 1 …… 1.192 5 -1.413 6
wij输出层权值 0.830 7 0.797 6 -0.671 8 -0.465 0.803 3 …… -0.311 0 -0.602 0
b2输出层阈值 -0.006 5

Fig.10

Calculated weight verification"

[1] 孙艳, 郭娟琛, 杨建忠. 毛织物视觉风格研究[J]. 毛纺科技, 2008(12):37-40.
SUN Yan, GUO Juanchen, YANG Jianzhong. Study on the visual style of worsted fabrics[J]. Wool Textile Journal, 2008(12):37-40.
[2] 陈慧. 毛纺面料特性对服装设计的影响[J]. 毛纺科技, 2016(8):48-50.
CHEN Hui. Influence of clothing design of characteristics of wool fabric[J]. Wool Textile Journal, 2016(8):48-50.
[3] MAK K L, PENG P, YIU K F C. Fabric defect detection using multi-level tuned-matched Gabor filter[J]. Journal of Industrial and Management Optimization, 2012,8(2):325-341.
[4] JING J F, ZANG H H, LI P F, et al. Fabric defect detection using Gabor filters and defect classification based on LBP and Tamura method[J]. Journal of the Textile Institute, 2013,104(1):18-27.
doi: 10.1080/00405000.2012.692940
[5] 李春雷, 高广帅, 刘洲峰, 等. 应用方向梯度直方图和低秩分解的织物疵点检测算法[J]. 纺织学报, 2017,38(3):149-154.
LI Chunlei, GAO Guangshuai, LIU Zhoufeng, et al. Fabric defect detection algorithm based on histogram of oriented gradient and low-rank decomposition[J]. Journal of Textile Research, 2017,38(3):149-154.
[6] 杨曼, 李仁忠, 刘阳阳, 等. 基于改进迭代匹配滤波的织物疵点检测[J]. 西安工程大学学报, 2017,31(3):383-389.
YANG Man, LI Renzhong, LIU Yangyang, et al. Fabric defect detection based on improved iterative match filter algorithm[J]. Journal of Xi'an Polytechnic University, 2017,31(3):383-389.
[7] 何峰, 周亚同, 赵翔宇, 等. 纹理织物疵点窗口跳步形态学法检测[J]. 纺织学报, 2017,38(10):124-131.
HE Feng, ZHOU Yatong, ZHAO Xiangyu, et al. Textured fabric defect detection based on windowedhop-step morphological algorithm[J]. Journal of Textile Research, 2017,38(10):124-131.
[8] KIM S M, PPAR C K. Evaluation of fabric pilling using hybrid imaging methods[J]. Fibers and Polymers, 2006,7(1):71-89.
[9] 汪亚明, 崔新辉, 韩永华. 基于小波变换及Gabor滤波的起毛起球图像分割[J]. 丝绸, 2016,53(3):37-40.
WANG Yaming, CUI Xinhui, HAN Yonghua. Fabric pilling image segmentation based on wavelet transform and Gabor filter[J]. Journal of Silk, 2016,53(5):37-40.
[10] 周圆圆, 潘如如, 高卫东, 等. 基于标准样照与图像分析的织物起毛起球评等方法[J]. 纺织学报, 2010,31(10):29-33.
ZHOU Yuanyuan, PAN Ruru, GAO Weidong, et al. Evaluation of fabric pilling based on standard images and image analysis[J]. Journal of Textile Research, 2010,31(10):29-33.
[11] 杨松林, 马帅, 丁朝鹏, 等. 应用机器视觉的织物表面绒毛率测试系统[J]. 纺织学报, 2017,38(6):118-123.
YANG Songlin, MA Shuai, DING Zhaopeng, et al. Fabric surface fluff rate detecting system based on machine vision[J]. Journal of Textile Research, 2017,38(6):118-123.
[12] 张恒, 张欣, 贺兴时. 应用BP神经网络估算服装铺料长度[J]. 纺织学报, 2009,30(5):109-113.
ZHANG Heng, ZHANG Xin, HE Xingshi. Using BP neural network to predict the length of garment marking[J]. Journal of Textile Research, 2009,30(5):109-113.
[13] 刘为敏, 谢红. BP神经网络下的智能化合体服装样板生成[J]. 纺织学报, 2018,39(7):116-121.
LIU Weimin, XIE Hong. Generation of intelligent fitting pattern based on BP neural network[J]. Journal of Textile Research, 2018,39(7):116-121.
[14] ZHOU H, DING W F, LI Z, et al. Predicting the grinding force of titanium matrix composites using the genetic algorithm optimizing back-propagation neural network model[J]. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 2019,233(4):1157-1167.
doi: 10.1177/0954405418780166
[15] ZHANG R, QIANG L, TAO J, et al. Data driven modeling using an optimal principle component analysis based neural network and its application to a nonlinear coke furnace[J]. Industrial & Engineering Chemistry Research, 2018,57(18):6344-6352.
[16] NICOLA G D, COCCIA G, PIERANTOZZI M, et al. Artificial neural network for the second virial coefficient of organic and inorganic compounds: an ANN for B of organic and inorganic compounds[J]. Chemical Engineering Communications, 2018,205(8):1077-1095.
doi: 10.1080/00986445.2018.1433664
[17] 袁冰清, 程功, 郑柳刚. BP神经网络基本原理[J]. 数字通信世界, 2018(8):28-29.
YUAN Bingqing, CHENG Gong, ZHENG Liugang. Basic principle of BP neural networks[J]. Digital Communication World, 2018(8):28-29.
[18] ZIEGLER J C, MONTANT M, BRIESEMEISTER B B, et al. Do Words stink? neural reuse as a principle for understanding emotions in reading[J]. Journal of Cognitive Neuroscience, 2018,30(7):1023-1032.
doi: 10.1162/jocn_a_01268 pmid: 29668395
[19] LEI X, ZHEN Z, SHENG Y, et al. Modeling and simulation of coal gas concentration prediction based on the BP neural network[J]. International Symposium on Information Engineering & Electronic Commerce, 2011(3):562-565.
[20] 温宗周, 刘现华. 基于BP算法的含沙量预测模型研究[J]. 西安工程大学学报, 2015,29(5):600-605.
WEN Zongzhou, LIU Xianhua. Study on prediction model of sediment concentrationbased on BP algorithm[J]. Journal of Xi'an Polytechnic University, 2015,29(5):600-605.
[1] ZHU Shigen, YANG Hongxian, BAI Yunfeng, DING Hao, ZHU Qiaolian. Investigation on automatic deformation inspection system of long and thin parts with hooks [J]. Journal of Textile Research, 2020, 41(10): 158-163.
[2] ZHANG Xiaoxia, LIU Fengkun, MAI Wei, MA Chongqi. Prediction of loom efficiency based on BP neural network and its improved algorithm [J]. Journal of Textile Research, 2020, 41(08): 121-127.
[3] ZHANG Jianxin, LI Qi. Online cheese package yarn density detection system based on machine vision [J]. Journal of Textile Research, 2020, 41(06): 141-146.
[4] DONG Chaoqun, DU Yuhong, REN Weijia, ZHAO Di. Research progress in optical imaging technology for detecting foreign fibers in cotton [J]. Journal of Textile Research, 2020, 41(06): 183-189.
[5] LU Hao, CHEN Yuan. Surface defect detection method of carbon fiber prepreg based on machine vision [J]. Journal of Textile Research, 2020, 41(04): 51-57.
[6] WANG Wensheng, LI Tianjian, RAN Yuchen, LU Ying, HUANG Min. Method for position detection of cheese yarn rod [J]. Journal of Textile Research, 2020, 41(03): 160-167.
[7] JING Junfeng, ZHANG Junyang, ZHANG Huanhuan, SU Zebin. Defect detection on surface of draw texturing yarn packages in gradient space [J]. Journal of Textile Research, 2020, 41(02): 44-51.
[8] WANG Xiaohui, LIU Yuegang, MENG Zhuo, SUN Yize. Optimization of process parameters for 3D additive screen printing based on genetic algorithm and neural network [J]. Journal of Textile Research, 2019, 40(11): 168-174.
[9] ZHOU Jie, MA Qiurui. Prediction of relationship between shoulder strap attribute and breast amplitude of sports bra by BP neural network [J]. Journal of Textile Research, 2019, 40(09): 186-191.
[10] SUN Weihong, RUAN Mianjiang, SHAO Tiefeng, LIANG Man. Detection method of cohesive performance of raw silk based on machine vision [J]. Journal of Textile Research, 2019, 40(08): 164-168.
[11] JING Junfeng, ZHANG Xingxing. Fiber glass bobbin yarn hairiness detection based on machine vision [J]. Journal of Textile Research, 2019, 40(05): 157-162.
[12] ZHOU Jie, MA Qiurui. Application of BP neural network in prediction of shapewear pressure [J]. Journal of Textile Research, 2019, 40(04): 111-116.
[13] XU Yang, ZHU Zhichao, SHENG Xiaowei, YU Zhiqi, SUN Yize. Improvement recognition method of vamp's feature points based on machine vision [J]. Journal of Textile Research, 2019, 40(03): 168-174.
[14] JING Junfeng, GUO Gen. Yarn packages hairiness detection based on machine vision [J]. Journal of Textile Research, 2019, 40(01): 147-152.
[15] ZHA Liugen, XIE Chunping. Prediction of cotton yarn quality based on four-layer BP neural network [J]. Journal of Textile Research, 2019, 40(01): 52-56.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
京ICP备14006648号
Copyright 2021 © Editorial office of Journal of Textile Research
Supported by Beijing Magtech Co.Ltd