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"

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