基于深度学习的女衬衫图案样式识别分类  

Pattern recognition and classification of women's shirts based on deep learning

作　　者：李青 冀艳波[1] 郭濠奇 刘凯旋 Li Qing;Ji Yanbo;Guo Haoqi;Liu Kaixuan(School of Fashion and Art Design,Xi'an Polytechnic University,Xi'an 710048,China;School of Electrical Engineering and Automation,Jiangxi University of Science andTechnology,Ganzhou 341000,China)

机构地区：[1]西安工程大学服装与艺术设计学院,西安710048 [2]江西理工大学电气工程与自动化学院,江西赣州341000

出　　处：《现代纺织技术》2022年第4期207-213,共7页Advanced Textile Technology

基　　金：陕西省教育厅自然专项基金项目(18JK0352)。

摘　　要：针对服装图案分类效率低的问题,设计一种基于Inception v3算法与迁移学习技术对女衬衫图案进行分类的方法。在Inception v3基础上,拓展训练网络架构,对8121张8类女衬衫图片进行训练,并与GoogLeNet等典型算法模型进行准确率与损失值对比。结果表明:在相同的识别精度上Inception v3具有较好的收敛速率;并且将迁移学习应用到Inception v3优化算法中,在保持初始模型识别速度情况下,可使模型识别平均精度提高6%,达到98%,同时参与训练的参数量减少了约91%。研究结果可有效解决服装图案分类困难问题,并为服装图案可视化分类研究提供技术参考。In order to solve the problem of low efficiency of clothing pattern classification,a method based on Inception v3 algorithm and transfer learning technology to classify blouse patterns is designed.On the basis of Inception v3,this paper expand the training network architecture.8121 pictures of blouses are divided into 8 categories to train,and the accuracy and loss value were compared with typical algorithm models such as GoogLeNet.The results show that:Inception v3 has a better convergence rate at the same recognition accuracy;and transfer learning is applied to the Inception v3 optimization algorithm,while maintaining the initial model recognition speed,the average accuracy of model recognition is increased to 98%,and the amount of parameters involved in training has been reduced by 91%.The results of this research can effectively solve the difficult problem of clothing pattern classification and provide a technical reference for its visual classification research.

关 键 词：Inception v3 迁移学习 衬衫图案 卷积神经网络 

分 类 号：TB383[一般工业技术—材料科学与工程]