Journal of Textile Research ›› 2021, Vol. 42 ›› Issue (05): 143-149.doi: 10.13475/j.fzxb.20200906407

• Apparel Engineering • Previous Articles     Next Articles

Objective evaluation of woven garment bagging performance by laser measurement

ZHENG Xiaoping1, LIU Chengxia1,2()   

  1. 1. School of Fashion Design & Engineering, Zhejiang Sci-Tech University, Hangzhou, Zhejiang 310018, China
    2. Zhejiang Province Engineering Laboratory of Clothing Digital Technology, Zhejiang Sci-Tech University, Hangzhou, Zhejiang 310018, China
  • Received:2020-09-24 Revised:2021-02-21 Online:2021-05-15 Published:2021-05-20
  • Contact: LIU Chengxia E-mail:glorior_liu@163.com

RichHTML

11

PDF (PC)

66

Abstract:

To study the objective evaluation of woven garment bagging behavior caused through daily wear, 20 pieces of fabrics were made into pant-tubes for actual wearing and bagging. A camera and a 3-D laser scanner were used to acquire images and point cloud data of fabrics respectively. Then feature extraction was performed on the processed data to get the 3-D indexes: bagging height, bagging volume, warp and weft maximum bagging rate. At the same time, the 2-D gray level co-occurrence matrix indexes were extracted from the bagging image to compare with the 3-D indexes. 12 pieces of fabrics were used for verification. The results showed that the highest coefficient of correlation with subjective grade is the bagging volume. The warp and weft maximum bagging rate has good correlation with the degree of bagging and the difference can be used to judge the bagging shape. The 3-D indexes obtained by 3-D laser scanning are more accurate than the 2-D indexes because it can overcome the influence of fabric pattern and fabric structure.

Key words: garment bagging, 3-D laser scanning, maximum bagging rate, feature extraction, objective evaluation

CLC Number: 

  • TS941.2

Tab.1

Fabric specification parameters"

试样
编号		 面料成分 组织 密度/
(根·(10 cm)-1)		 面密度/
(g·m-2)		 厚度/
mm
经向 纬向
1# 涤/棉(65/35) 平纹 250 320 101 0.24
2# 涤/氨纶(95/5) 平纹 320 150 200 0.17
3# 涤/粘胶(70/30) 斜纹 380 240 148 0.35
4# 涤100% 平纹 230 180 112 0.39
5# 涤100% 平纹 380 170 103 0.36
6# 棉100% 平纹 560 300 94 0.42
7# 棉100% 斜纹 800 200 172 0.70
8# 涤100% 平纹 200 300 110 0.45
9# 棉100% 斜纹 600 360 221 0.79
10# 棉100% 平纹 270 210 97 0.40
11# 涤/粘胶(70/30) 平纹 600 370 193 0.20
12# 涤/氨纶(96/4) 斜纹 360 180 103 0.36
13# 涤/粘胶(75/25) 平纹 380 130 188 0.65
14# 棉/亚麻(65/35) 平纹 250 350 194 0.47
15# 涤/棉(65/35) 平纹 380 270 88 0.16
16# 涤/棉(65/35) 平纹 250 300 165 0.43
17# 棉100% 斜纹 480 300 147 1.15
18# 棉100% 缎纹 350 200 202 0.82
19# 涤100% 平纹 320 180 153 0.47
20# 棉100% 平纹 200 250 160 0.27

Fig.1

Sample of pants used in experiment. (a) Front part; (b) Back part"

Tab.2

Figure data of the wearer and sample size of the pants-tube"

被试者腿型数据/cm 裤筒样板尺寸/cm
大腿围 膝围 小腿围 上筒围 中裆围 下筒围
45 36 33 48 44 42

Fig.2

Alignment of XY plane. (a) Spatial coordinate of scanned data; (b) Spatial coordinate of GS; (c) Spatial coordinate after alignment"

Fig.3

Acquisition of bagging height. (a) Diagram of bagging height; (b) Extremum of scatter diagram"

Fig.4

Bagging shape under actual wear. (a) Longitudinal ellipse; (b) Transverse ellipse; (c) Cross-section of longitudinal ellipse; (d) Cross-section of transverse ellipse"

Fig.5

Objects of subjective evaluation. (a) Image of fabric; (b) 3-D reconstruction of fabric"

Fig.6

Drape coefficient of fabric"

Tab.3

3-D feature index based on laser scanning"

试样
编号		 起拱高度/
cm		 起拱体积/
cm3		 经向最大
起拱率		 纬向最大
起拱率		 起拱
等级
1# 0.222 2 5.784 4 1.000 4 1.000 7 5
2# 0.537 7 18.361 3 1.002 9 1.003 9 5
3# 0.950 6 51.879 6 1.004 5 1.002 9 4
4# 1.277 9 75.331 5 1.011 2 1.007 4 3
5# 1.733 2 95.181 4 1.021 0 1.019 8 4
6# 2.938 2 285.283 3 1.091 4 1.074 4 1
7# 1.911 7 103.309 7 1.020 2 1.018 1 3
8# 0.696 7 20.582 5 1.001 9 1.004 4 5
9# 2.744 5 250.211 2 1.065 8 1.042 8 1
10# 1.920 5 143.982 4 1.025 4 1.022 5 2
11# 1.479 8 99.094 2 1.012 6 1.006 3 3
12# 1.501 4 87.075 9 1.031 1 1.019 6 3
13# 1.144 6 61.597 9 1.011 1 1.007 6 4
14# 1.875 6 151.766 6 1.035 6 1.020 9 2
15# 2.145 2 180.440 5 1.039 1 1.026 7 1
16# 2.147 8 149.781 9 1.038 4 1.024 2 2
17# 2.409 6 155.917 0 1.060 3 1.037 3 2
18# 0.942 8 48.401 2 1.007 8 1.005 2 4
19# 1.031 2 44.092 9 1.010 2 1.010 4 4
20# 0.338 0 7.351 5 1.001 2 1.003 3 5

Fig.7

Correlation coefficients between 3-D index and bagging grade"

Tab.4

Gray-level co-occurrence matrix parameter of bagging image"

试样编号 能量REne 熵REnt 惯性矩RCon 相关性RCor 起拱等级
均值M1 标准差σ1 均值M2 标准差σ2 均值M3 标准差σ3 均值M4 标准差σ4
1# 0.860 0 0.006 6 0.366 3 0.018 0 0.035 5 0.007 2 12.269 3 1.179 1 5
2# 0.387 8 0.013 3 0.341 9 0.025 1 0.119 4 0.022 3 2.214 1 0.071 1 5
3# 0.235 1 0.014 2 1.744 1 0.046 4 0.171 5 0.031 1 1.272 3 0.032 9 4
4# 0.517 4 0.015 3 1.517 4 0.055 0 0.105 8 0.022 3 3.060 5 0.165 7 3
5# 0.242 2 0.017 8 1.793 3 0.037 7 0.220 7 0.046 8 1.341 9 0.062 9 4
6# 0.190 2 0.020 6 2.023 9 0.108 2 0.510 9 0.094 7 0.865 4 0.024 5 1
7# 0.264 5 0.016 9 1.694 5 0.073 9 0.183 3 0.045 2 1.441 0 0.065 6 3
8# 0.289 8 0.011 1 1.068 9 0.033 6 0.387 8 0.045 0 1.286 3 0.164 8 5
9# 0.301 3 0.022 0 2.499 6 0.120 7 0.423 3 0.027 5 1.706 0 0.053 7 1
10# 0.189 5 0.019 4 2.210 1 0.117 6 0.332 5 0.091 9 0.789 1 0.041 4 2
11# 0.290 9 0.018 9 1.576 5 0.076 5 0.171 7 0.040 3 1.690 6 0.084 8 3
12# 0.205 1 0.017 1 2.100 6 0.108 7 0.266 7 0.053 3 0.824 5 0.042 6 3
13# 0.267 8 0.012 6 1.683 1 0.069 3 0.241 2 0.043 9 1.589 4 0.101 2 4
14# 0.098 3 0.015 9 2.169 9 0.102 3 0.207 9 0.172 9 0.476 0 0.027 1 2
15# 0.120 4 0.011 3 2.475 7 0.180 7 0.265 8 0.073 3 0.441 6 0.008 7 1
16# 0.168 1 0.013 2 2.236 6 0.087 8 0.237 4 0.058 2 0.601 4 0.012 7 2
17# 0.065 8 0.013 4 2.109 5 0.103 9 0.271 6 0.084 5 0.619 1 0.023 2 2
18# 0.189 1 0.012 0 2.087 8 0.053 2 0.122 7 0.057 2 1.000 8 0.060 2 4
19# 0.388 3 0.011 2 1.290 8 0.043 5 0.102 7 0.018 4 2.196 3 0.058 6 4
20# 0.370 1 0.016 5 0.723 8 0.035 8 0.101 6 0.024 6 2.395 1 0.088 1 5

Fig.8

Correlation coefficients between 2-D index and bagging grade"

Tab.5

Objective indexes and bagging grade of woven fabric used for verification"

试样编号 三维 熵的标准差 三维预测等级 二维预测等级 四舍五入后的预测等级 主观评价等级
起拱体积/cm3 纬向起拱率 三维 二维
1# 6.914 54 1.000 04 0.006 13 4.85 5.25 5 5 5
2# 14.654 49 1.007 29 0.028 82 4.94 4.55 5 5 5
3# 27.859 26 1.000 86 0.060 44 4.35 3.59 4 4 4
4# 52.426 44 1.003 96 0.045 78 3.84 4.04 4 4 4
5# 235.195 68 1.052 03 0.141 84 1.13 1.11 1 1 1
6# 66.745 23 1.006 40 0.025 89 3.58 4.64 4 5 4
7# 11.829 44 1.000 34 0.027 52 4.73 4.59 5 5 5
8# 179.067 32 1.037 95 0.096 61 1.99 2.49 2 2 2
9# 105.309 20 1.022 67 0.076 30 3.25 3.11 3 3 3
10# 78.113 33 1.016 08 0.023 44 3.68 4.72 4 5 4
11# 194.780 77 1.028 48 0.114 47 1.20 1.94 1 2 2
12# 120.195 07 1.031 30 0.084 55 3.22 2.86 3 3 3
[1] ZHANG X, LI Y, YEUNG K W, et al. Fabric bagging: part II: objective evaluation and physical mechanism[J]. Textile Research Journal, 1999,69(8):598-606.
doi: 10.1177/004051759906900809
[2] AMIRBAYAT J, NAMIRANIA B. An improved analysis of bagging of textile fabrics: part II: experimental work[J]. International Journal of Clothing Science and Technology, 2006,18(5):308-313.
doi: 10.1108/09556220610685230
[3] THOMAS W. Celanese bagging test for knit fabrics[J]. Textile Chemist & Colorist, 1971,3:31-233.
[4] SULAR V. A new testing instrument with a.pngicial arm to simulate fabric bagging by human elbow[J]. Industria Textila, 2011,62(5):259-264.
[5] GRONEWALD K H, ZOLL W. Practical methods for determining the "bagging" tendency in textile[J]. Inter Textile Bull(Weaving), 1973,3:273-275.
[6] JAOUACHI B. Study of knitting factors contribution on residual bagged fabric behavior[J]. Journal of The Textile Institute, 2013,104(10):1132-1140.
doi: 10.1080/00405000.2013.778000
[7] YEUNG K W, LI Y, ZHANG X, et al. Evaluating and predicting fabric bagging with image processing[J]. Textile Research Journal, 2002,72(8):693-700.
doi: 10.1177/004051750207200808
[8] 余芳. 基于小波分析的毛料服装穿着起拱性客观评价[J]. 毛纺科技, 2018,46(4):67-71.
YU Fang. Objective evaluation of wearing bagging behavior for wool pants based on wavelet analysis[J]. Wool Textile Journal, 2018,46(4):67-71.
[9] HAMID Movahed, HOSSEIN Hasani. Analytical studies on woven fabrics' bagging performance affected by the material, yarn, and fabric parameters[J]. Journal of The Textile Institute, 2017,108(5):703-711.
doi: 10.1080/00405000.2016.1180736
[10] 张宁, 潘如如, 高卫东. 采用图像处理的织物缝纫平整度自动评估[J]. 纺织学报, 2017,38(4):145-150.
ZHANG Ning, PAN Ruru, GAO Weidong. Automatic seam-puckering evaluation using image processing[J]. Journal of Textile Research, 2017,38(4):145-150.
[1] WANG Hui, SUN Jie, DING Xiaojun, LONG Ying, ZOU Fengyuan. Comparison of feature extracting and matching methods for fabric patterns [J]. Journal of Textile Research, 2020, 41(04): 45-50.
[2] SHI Kangjun, WANG Jing'an, GAO Weidong. Objective smoothness evaluation of fabric based on Fourier spectral features [J]. Journal of Textile Research, 2019, 40(11): 50-56.
[3] . Fabric defect inspection based on modified discriminant complete local binary pattern and lattice segmentation [J]. Journal of Textile Research, 2018, 39(09): 57-64.
[4] . Objective evaluation of seaming smoothness for worsted wool garments [J]. JOURNAL OF TEXTILE RESEARCH, 2018, 39(03): 120-125.
[5] . Visual saliency detection of clothing and accessory design [J]. JOURNAL OF TEXTILE RESEARCH, 2018, 39(03): 126-131.
[6] . Pilling objective evaluation based Gaussian filtering in wavelet domain [J]. JOURNAL OF TEXTILE RESEARCH, 2017, 38(09): 155-161.
[7] . 3-D reconstruction of carbon-fiber fabric surface from Laser scanning points cloud [J]. Journal of Textile Research, 2016, 37(4): 54-59.
[8] . Fast fabric defect detection algorithm based on integral image [J]. JOURNAL OF TEXTILE RESEARCH, 2016, 37(11): 141-147.
[9] . Lace retrieval method based on improved texture feature [J]. JOURNAL OF TEXTILE RESEARCH, 2016, 37(06): 142-154.
[10] . Automatic recognition and positioning method of fabric slitting zone using one-dimensional Fourier transform [J]. JOURNAL OF TEXTILE RESEARCH, 2016, 37(01): 147-151.
[11] . Evaluation on fabric singeing effect based on change of fabric hairiness characteristics [J]. Journal of Textile Research, 2015, 36(04): 102-106.
[12] . Algorithm for pattern detection of cotton foreign fibers based on cluster statistic analysis [J]. Journal of Textile Research, 2015, 36(03): 135-139.
[13] . Defect detection of plain weave based on visual saliency mechanism [J]. JOURNAL OF TEXTILE RESEARCH, 2014, 35(4): 56-0.
[14] . Research on objective evaluation of fabric wettability based on image processing [J]. JOURNAL OF TEXTILE RESEARCH, 2013, 34(9): 44-0.
[15] . Fabric defect detection of statistic aberration feature based on GMRF model [J]. JOURNAL OF TEXTILE RESEARCH, 2013, 34(4): 137-142.
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