Journal of Textile Research ›› 2019, Vol. 40 ›› Issue (10): 191-195.doi: 10.13475/j.fzxb.20181006705

• Management & Information • Previous Articles    

Garment grain balance evaluation system based on deep learning

XU Qian1, CHEN Minzhi1,2()   

  1. 1. School of Fashion Design & Engineering, Zhejiang Sci-Tech University, Hangzhou, Zhejiang 310018, China;
    2. School of International Education, Zhejiang Sci-Tech University, Hangzhou, Zhejiang 310018, China
  • Received:2018-10-31 Revised:2019-06-28 Online:2019-10-15 Published:2019-10-23
  • Contact: CHEN Minzhi E-mail:cmz_m@163.com

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Abstract:

In order to solve the problem on automatic evaluation of the garment grain balance in virtual fitting, the paper proposed a method based on the superiority of deep learning in automatic image recognition, created the topological structure of convolutional neural network according to the characteristics of garment grain balance, and by the grade classification training and learning training of the garment grain plaques with different balance states, the recognition accuracy of the neural network model of the training reached 93.589%, thus an automatic evaluation system was established for the grain worm balance of all key parts of the garment. The results show that the application of the garment grain balance evaluation system based on deep learning can identify and classify the garment grain plaques in each key part of the garment in the virtual environment, which can shorten the time of balance detection and improve the detection efficiency. The unbalanced position of the garment grain can be quickly acquired to facilitate the modification of the garment.

Key words: virtual fitting, deep learning, garment grain, image recognition, Alexnet model

CLC Number: 

  • TS941.2

Fig.1

Convolutional neural network structure of garmentgrain balance evaluation"

Tab.1

Network parameters of each layer"

层数 每层类型 核数 核的大小/像素 特征图的数量 特征图的大小/像素 步长/像素
1 卷积层1 96 11×11 96 55×55 4
2 池化层1 1 3×3 96 27×27 2
3 卷积层2 256 5×5 256 27×27 1
4 池化层2 1 3×3 256 13×13 2
5 卷积层3 384 3×3 384 13×13 1
6 卷积层4 384 3×3 384 13×13 1
7 卷积层5 256 3×3 256 13×13 1
8 池化层5 1 3×3 256 6×6 2

Tab.2

Network parameters of full connection layer"

序号 层数 核的大小/
像素		 维向量
的大小		 步长/
像素
1 全连接层6 1×1 4 096 1
2 全连接层7 1×1 4 096 1
3 全连接层8 1×1 4 1

Tab.3

Garment grain balancelevel reference table"

服装丝缕
平衡等级		 服装丝缕平衡程度
1 平衡,白色样布条贴合合体西服表面,标识线与水平基准线保持平衡
2 较平衡,白色样布条基本贴合合体西服表面,标识线与水平基准线基本保持平衡
3 较不平衡,白色样布条不太贴合合体西服表面,标识线与水平基准线不太保持平衡
4 不平衡,白色样布条不贴合合体西服表面,标识线与水平基准线未保持平衡

Tab.4

Convolutional neural network model learning training results forgarment grain balance evaluation"

各等级训练
样本状态		 训练结果/组
平衡状态 较平衡 较不平衡 不平衡
平衡 89 3 2 1
较平衡 0 79 3 2
较不平衡 1 5 82 1
不平衡 0 3 2 86
准确率/% 98.89 87.78 92.14 95.56

Tab.5

Convolutional neural network model test results for garment grain balance evaluation"

各等级训练
样本状态		 训练结果/组
平衡状态 较平衡 较不平衡 不平衡
平衡 15 0 0 1
较平衡 3 20 0 4
较不平衡 2 5 25 0
不平衡 5 0 0 20
准确率/% 60.00 80.00 100.00 80.00

Tab.6

Net size of human body obtained by three-dimensional scanningcm"

名称 净尺寸 名称 净尺寸
颈围 37.0 胸围 91.7
肩宽 43.0 肚围 83.5
背宽 38.5 臀围 92.7
前胸 38.5 袖头 30.7
侧颈到胸围 23.5 袖管 28.5
后背高 22.0 袖口 18.5

Fig.2

Schematic diagram of 10 key parts for judging balance and evaluation results of garment grain"

[1] 魏静, 金晨怡, 韩阳. 裁剪适用性解析[J]. 纺织学报, 2010,31(12):108-111.
WEI Jing, JIN Chenyi, HAN Yang. Analysis onapplicability of draping[J]. Journal of Textile Research, 2010,31(12):108-111.
[2] STEPHEN Gray. IT reaches clothing production[J]. Textile Asia, 1995,26(8):103-108.
[3] 陆永良, 李汝勤, 胡金莲. 虚拟服装的发展历史和现状[J]. 纺织学报, 2005,26(1):132-134.
LU Yongliang, LI Ruqin, HU Jinlian. History and current situation for virtual[J]. Journal of Textile Research, 2005,26(1):132-134.
[4] XU B, HUANG Y, YU W. Scanning and modeling for custom fit garments[J]. Journal of Textile & Apparel Technology & Management, 2002,2(2):1-9.
[5] 张瑞云, 李汝勤. 服装CAD中三维数据的拾取与服装造型点的生成[J]. 中国纺织大学学报, 2000,26(3):48-50.
ZHANG Ruiyun, LI Ruqin. 3D data's picking up and garment key points getting in garment CAD[J]. Journal of Donghua University, 2000,26(3):48-50.
[6] 周飞燕, 金林鹏, 董军. 卷积神经网络研究综述[J]. 计算机学报, 2017,40(6):1229-1251.
ZHOU Feiyan, JIN Linpeng, DONG Jun. Review of convolutional neural network[J]. Chinese Journal of Computers, 2017,40(6):1229-1251
[7] 胡正平, 陈俊岭, 王蒙, 等. 卷积神经网络分类模型在模式识别中的新进展[J]. 燕山大学学报, 2015,39(4):283-291.
HU Zhengping, CHEN Junling, WANG Meng, et al. Recent progress on convolutional neural network in pattern recognition[J]. Journal of Yanshang University, 2015,39(4):283-291.
[8] 景军峰, 范晓婷, 李鹏飞, 等. 应用深度卷积神经网络的色织物缺陷检测[J]. 纺织学报, 2017,38(2):68-74.
JING Junfeng, FAN Xiaoting, LI Pengfei, et al. Yarn-dyed fabric defect detection based on deep-convolutional neural network.[J]. Journal of Textile Research, 2017,38(2):68-74.
[9] YANN Lecun. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998,86(11):2278-2324.
doi: 10.1109/5.726791
[10] 尹宝才, 王文通, 王立春. 深度学习研究综述[J]. 北京工业大学学报, 2015,41(1):48-59.
YIN Baocai, WANG Wentong, WANG Lichun. Overview of deep learning[J]. Journal of Beijing University of Technology, 2015,41(1):48-59.
[11] 孙炜晨. 基于深度学习的图像分类[D]. 北京:北京邮电大学, 2016: 4-6.
SUN Weichen. Deep learning in image classification[D]. Beijing: Beijing University of Posts and Telecommunications, 2016: 4-6.
[12] 高震宇. 基于卷积神经网络的图像分类方法及应用[D]. 合肥:中国科技技术大学, 2018: 9-13.
GAO Zhenyu. Research and application of image classification method based on deep convolutional neural network[D]. Hefei:University of Science and Technology of China, 2018: 9-13.
[13] 陈雯柏. 人工神经网络与实践[M]. 西安: 西安电子科技大学出版社, 2016: 179-180.
CHEN Wenbai. Artificial Neural Network and Practice [M]. Xi'an: Xi'an University of Electronic Science and Technology Press, 2016: 179-180.
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