摘要
为探索传统服饰文化的智能化保护手段和提高传统服饰文化的传播效率,以加权投票法集成多注意力机制的传统服饰识别算法为核心,构建了一个典型传统服饰图像的在线识别系统。首先,通过书籍扫描和线下拍摄等手段收集传统服饰图像数据,再联合多背景替换和几何变换混合增强服饰图像数据,完成传统服饰图像数据集的构建。随后,采用迁移学习技术在DenseNet169网络上分别引入了通道注意力、卷积注意力和位置注意力3种机制来构建模型,并对3种模型的识别结果进行加权投票判决,实现对传统服饰图像的高精度识别。在此基础上,通过对未知待测图像进行在线裁剪和自适应等规范化预处理,提高了识别系统的泛化适应性。最后,采用Web和云计算技术实现了系统的在线识别、交互、显示和账号管理等功能集成。测试结果表明,本文实现的传统服饰识别算法在验证集上的识别准确率达到了93.5%,构建的系统能够有效地在线识别15类传统服饰图像,对传统服饰文化的传播和保护具有一定的促进作用。
Objective Traditional costume culture is in danger of disappearing gradually and needs effective conservation methods.Currently,most of conservation methods rely on human resources,such as recording traditional costume by taking photos and scanning,causing that the conservation efficiency and culture exchanging are low and an efficient method to preserve the culture is lacking.Therefore,a new deep learning algorithm was proposed for highly accurate recognition of traditional costumes,and an online web identification system was designed based on cloud computing technologies.The proposed research should serve as a new alternative way for conservation and recognization of traditional costume culture efficiently.Method Firstly,a traditional costume image dataset was constructed and enhanced by the combination of multiple background replacement and geometric transformation.Then,three modified DenseNet169 network models were built by introducing the squee and excitation(SE),convolutional block attention module(CBAM)and coordinate attention(CA),respectively,and these models were later integrated together to form a high-performance algorithm.After that,based on cloud computation and web technology,an online recognition system for typical traditional costume images was constructed by combining image normalization pre-processing and the new algorithm.Results A traditional costume images dataset,which contains 92160 images of a total of 15 styles,such as costumes of Zang,Man,Mongolian,Miao,Yi,Gejia,Li,Qiang,Hui,Dai,Zhuang,Han,She,Bai and Korean nationlities,was set up.The comprehensive recognition accuracies for the three improved models(using attention mechanisms SE-Dense Net 169,CBAM-DenseNet169 and CA-DenseNet169,respectively)were 89.50%,89.83%and 90.17%,respectively(Tab.1).Although all their comprehensive recognition performances were good and similar,each model was limited by poor recognition accuracies for some specific different traditional costume categories.For example,the separated recognition accuracies of SE-DenseNet169 on Li and Zang costumes were only 77.5%and 80%,respectively.After weighting integration of the three models,the final algorithm obtained a high comprehensive recognition accuracy of 93.50%on the verification set.Compared with the previous best comprehensive recognition accuracy of CA-DenseNet169,an improvement of 3.33%was achieved.With the new algorithm,apart from relatively low separated recognition accuracy(about 87.50%)for Li costumes,the separated recognition accuracies for other traditional costume categories were all above 90.00%.Once the Korean costume image was input into the system,the most possible 3 prediction costume categories and the consumption time were displayed(Fig.4).600 Real scene traditional costume images from different costume categories were tested,only 15 images′corrected categories were not shown in the most possible 3 prediction costume categories,which indicated a high comprehensive recognition accuracy of 98.00%.The value would be decreased to 93.50%if only using the most possible 1 prediction costume category as the output result.Meanwhile,the average processing and recognizing time taken by the system(deployed on an Aliyun server with dual-cores intel i5 CPU and 4 GiB RAM)for an image of 1 MB was around 11-13 s,which should be acceptable.Conclusion In addition to the problems of lack of effective methods to protect traditional costume culture and limited recognization channels,the research built an online recognition system of typical traditional costume images.The system could efficiently identify 15 types of traditional costume images,and it is convenient to operate,recognize and share.Besides recording,protecting and spreading traditional costume culture efficiently,the system could also be used as a digital tool to promote tourism,culture and economy in various ethnic regions.The research would provide a new alternative solution for conserving and recognizing traditional costume culture.However,at present,the system still has some limitations,such as,only few recognizable traditional costume categories,low recognition accuracy of individual traditional costume categories and slightly slow recognition speed.In the future,the number of recognizable costume categories should be expanded,the algorithm should be improved,and the interface and operation process of the system should be optimized.
作者
颜丙义
侯金
黄启煜
杨汉城
田进
杨春勇
YAN Bingyi;HOU Jin;HUANG Qiyu;YANG Hancheng;TIAN Jin;YANG Chunyong(College of Electronics and Information Engineering,South-Central Minzu University,Wuhan,Hubei 430074,China;Hubei Key Laboratory of Intelligent Wireless Communications,Wuhan,Hubei 430074,China;Hubei Engineering Research Center of Intelligent Internet of Things Technology,Wuhan,Hubei 430074,China)
出处
《纺织学报》
EI
CAS
CSCD
北大核心
2023年第5期184-190,共7页
Journal of Textile Research
基金
国家自然科学基金项目(11504435、62171478)
中央高校基本科研业务费专项资金项目(CZY22011)。
关键词
传统服饰图像
数据集增强
迁移学习
加权投票判决
在线识别系统
traditional costume image
dataset enhancement
transfer learning
weighted voting verdict
online recognition system
作者简介
第一作者:颜丙义(1998-),男,硕士生。主要研究方向为计算机视觉;通信作者:侯金(1981-),男,教授,博士。主要研究方向为光电检测与图像处理。E-mail:houjin@mail.scuec.edu.cn。
