Journal of Textile Research ›› 2020, Vol. 41 ›› Issue (12): 124-129.doi: 10.13475/j.fzxb.20200505006

• Apparel Engineering • Previous Articles     Next Articles

Automatic measurement of key dimensions for Han-style costumes based on use of convolutional neural network

WANG Yiwen1, LUO Ronglei2,3(), KANG Yuzhe4   

  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
    3. Silk and Fashion Culture Center, Hangzhou, Zhejiang 310018, China
    4. School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou, Zhejiang 310018, China
  • Received:2020-05-26 Revised:2020-08-31 Online:2020-12-15 Published:2020-12-23
  • Contact: LUO Ronglei E-mail:luoronglei@163.com

RichHTML

14

PDF (PC)

264

Abstract:

In order to quickly and accurately obtain the key dimensions of the ancient Chinese Han-style costumes with scarce sample data, a clothing size measurement scheme based on the use of convolutional neural network was proposed in this paper. Firstly, a two-stage convolutional neural network model GlobalNet-RefineNet was built for detecting the key points. The accuracy of the key point recognition was improved through twice transfer learning and repeated iterative training. An algorithm was used to get the pixel distance between coordinate points. Combined with the tiles of Han-style costume and at least one real measurement size given in the museum or excavation report, the size data of the whole garment were obtained through proportional mapping. This research used the top of a Han-style costume as an example for experiments. The research results show that after two times of transfer learning, the model has a high degree of convergence and good training effect. The relative error of costume top size measured by this scheme is between 0.59%-4.17%. This research provides new ideas for the restoration research of traditional clothing and the measurement of cultural relics.

Key words: dimensional measurement, key dimensions of costume, Han-style costume, convolutional neural network, transfer learning

CLC Number: 

  • TS941.79

Fig.1

GlobalNet pyramid network structure"

Fig.2

Two-stage convolutional neural network structure diagram"

Fig.3

Flow chart of migration training"

Tab.1

Key point detection results after first training"

类型 测量
对象
张数		 含不同个数关键点图片张数 单点
准确
率/%		 漏识
别率/
%		 识别
准确
率/%
13
个		 10~13
个		 10~7
个		 7个
以下
现代汉服 50 20 15 15 0 68 0 40
古代汉服 50 17 14 19 0 61 0 34

Tab.2

Key point detection results after second training"

类型 测量
对象
张数		 含不同个数关键点图片张数 单点
准确
率/%		 漏识
别率/
%		 识别
准确
率/%
13个 10~13
个		 7~10
个		 7个
以下
现代汉服 50 44 6 0 0 95 0 92
古代汉服 50 40 8 2 0 86 0 80

Fig.4

Detection effect of Han-style costumes after first training. (a) Modern Han-style costumes ; (b) Ancient Han-style costumes"

Fig.5

Detection effect of Han-style costumes after second training. (a) Modern Han-style costumes; (b) Ancient Han-style costumes"

Tab.3

Size measurement table of women's jacket with geometric patterns"

上衣
部位		 实际值/
mm		 像素距
离/像素		 测量
值/mm		 绝对误
差/mm		 相对误
差/%
领口宽 160 79 165 5 3.13
袖口宽 120 55 115 5 4.17
摆宽 1 120 530 1 108 9 0.80
衣长 725 344 719 6 0.83

Tab.4

Size measurement table of plain silk shirt"

上衣
部位		 实际值/
mm		 像素距
离/像素		 测量值/
mm		 绝对误
差/mm		 相对误
差/%
领口宽 100 55 97 3 3.00
袖宽 680 384 676 4 0.59
衣长 810 456 803 7 0.86

Tab.5

Size measurement table of blue crepe short women's Bufu with unicorn pattern"

上衣
部位		 实际值/
mm		 像素距
离/像素		 测量值/
mm		 绝对误
差/mm		 相对误
差/%
袖宽 360 203 366 6 1.67
腰宽 590 331 597 7 1.19
衣长 630 344 621 9 1.43
[1] 曹丽, 汪亚明, 包晓敏. 机器视觉在服装尺寸自动测量中的应用[J]. 纺织学报, 2003,24(1):19-21.
CAO Li, WANG Yaming, BAO Xiaomin. Application of machine vision in automatic measurement of clothing size[J]. Journal of Textile Research, 2003,24(1):19-21.
[2] 王生伟, 张凯健. 基于角点检测的服装尺寸在线测量技术[J]. 信息技术与信息化, 2018,12:73-75.
WANG Shengwei, ZHANG Kaijian. Online measurement technology for garment size based on image process-ing[J]. Information Technology and Informatization, 2018,12:73-75.
[3] 董建明, 胡觉亮. 一种有效的服装尺寸自动测量方法[J]. 纺织学报, 2008,29(5):98-101.
DONG Jianming, HU Jueliang. An efficient method for automatic measurement of garment dimensions[J]. Journal of Textile Research, 2008,29(5):98-101.
[4] 肖祎. 基于拍照的服装和人体尺寸测量系统设计与研发[D]. 杭州:浙江大学, 2019: 25-37.
XIAO Yi. Design and development of a garment and human body measurement system based on photo-graphy[D]. Hangzhou:Zhejiang University, 2019: 25-37.
[5] HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]// Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE Computer Society, 2016: 770-778.
[6] LIN T Y, DOLLA P, GIRSHICK R, et al. Feature pyramid networks for object detection[C]// Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. State of California: IEEE Computer Society, 2017: 106.
[7] GROOS D, RAMAMPIARO H, IHLEN E. Efficientpose: scalable single-person pose estima-tion[C]// Proceedings of the Association for the Advance of Artificial Intelligence. New York: Applied Intelligence, 2020: 1-14.
[8] ZHANG Q, LU Z, ZHOU X, et al. Automatic removal of false image stars in disk-resolved images of the cassini imaging science subsystem[J]. Research in Astronomy and Astrophysics, 2020,20(7):95-104.
doi: 10.1088/1674-4527/20/6/95
[9] 段萌. 基于卷积神经网络的图像识别方法研究[D]. 郑州:郑州大学, 2017: 42.
DUAN Meng. The research on the mothed of image recognition based on convolutional neural networks[D]. Zhengzhou:Zhengzhou University, 2017: 42.
[10] Fashion AI赛题与数据[DB/OL]. 阿里云天池[2020-04-25]. https://tianchi.aliyun.com/competition/entrance/231648/introduction.
Fashion AI competition questions and data[DB/OL]. Alibaba Tianchi[2020-04-25]. https://tianchi.aliyun.com/competition/entrance/231648/introduction.
[11] LIU Ziwei, LUO Ping, QIU Shi, et al. Large-scale fashion (deepfashion) database[DB/OL]. The Chinese University of Hong Kong [2020-04-25]. http://mmlab.ie.cuhk.edu.hk/projects/DeepFashion.html.
[12] XU Luohongshang. FashionAI全球挑战赛-服饰属性识别赛后技术分享[EB/OL]. [2020-04-25]. https://blog.csdn.net/xuluohongshang/article/details/80616331.
XU Luohongshang. Fashion AI global challenge-apparel attribute recognition technology sharing after the game [EB/OL]. [2020-04-25]. https://blog.csdn.net/xuluohongshang/article/details/80616331.
[13] 王淑娟, 李一泉, 蒋玉秋, 等. 钱家衣橱无锡七房桥明墓出土服饰保护修复展[M]. 杭州:中国丝绸博物馆, 2017: 14-16.
WANG Shujuan, LI Yiquan, JIANG Yuqiu, et al. Qian family's wardrobe costume found in the tomb of Qian Zhang(1486-1505) and his wife[M]. Hangzhou: China National Silk Museum, 2017: 14-16.
[14] 泰州市博物馆. 江苏泰州森森庄明墓发掘简报[J]. 文物, 2013,11:36-49.
Taizhou Museum. The excavation of a tomb of the Ming dynasty at Sensenzhuang in Taizhou city, Jiangsu[J]. Cultural Relics, 2013,11:36-49.
[15] 山东博物馆. 斯文在兹孔府旧藏服饰[M]. 济南:山东博物馆, 2012: 64.
Shandong Museum. Clothing from the collections of Kong family mansion[M]. Ji'nan:Shandong Museum, 2012: 64.
[1] SHAO Jinxin, ZHANG Baochang, CAO Jipeng. Fiber detection and recognition technology in cotton fiber carding process based on image processing and deep learning [J]. Journal of Textile Research, 2020, 41(07): 40-46.
[2] XIA Haibang, HUANG Hongyun, DING Zuohua. Clothing comfort evaluation based on transfer learning and support vector machine [J]. Journal of Textile Research, 2020, 41(06): 125-131.
[3] WANG Zexia, CHEN Ge, CHEN Zhenzhong. Surface defect recognition of chemical fiber yarn packages based on improved convolutional neural network [J]. Journal of Textile Research, 2020, 41(04): 39-44.
[4] WANG Xiaohua, YAO Weiming, WANG Wenjie, ZHANG Lei, LI Pengfei. Sewing gesture recognition based on improved YOLO deep convolutional neural network [J]. Journal of Textile Research, 2020, 41(04): 142-148.
[5] JIA Xiaojun, YE Lihua, DENG Hongtao, LIU Zihao, LU Fengjie. Elements classification of vein patterns using convolutional neural networks for blue calico [J]. Journal of Textile Research, 2020, 41(01): 110-117.
[6] SUN Jie, DING Xiaojun, DU Lei, LI Qinman, ZOU Fengyuan. Research progress of fabric image feature extraction and retrieval based on convolutional neural network [J]. Journal of Textile Research, 2019, 40(12): 146-151.
[7] . Yarn-dyed fabric defect detection based on deep-convolutional neural network [J]. JOURNAL OF TEXTILE RESEARCH, 2017, 38(02): 68-74.
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