摘要
为提高服装制版效率,实现从服装款式图到样板的智能检索,提出一种基于服装结构特征识别的相似样板匹配技术。该技术将服装结构制版知识与深度学习算法结合,基于对女裤中的廓形、褶裥、腰头类型等18个细粒度特征的识别来匹配样板。其中,技术的实现主要包括分类标签设计和模型验证实验。对于分类标签设计:先根据女裤结构制图知识,对平面款式图中可作为样板相似性评价指标的服装结构特征进行定义,并根据定义设置多标签类别;然后将多标签分类转化为单标签多分类,建立平面款式图、结构特征和样板三者之间的联系;最后通过数据可视化等方法对标签之间的相关性进行研究,并设计了最终的18个分类标签。对于模型验证实验:首先建立以女裤平面款式图为样本的服装数据集,基于数据集的特点对经典AlexNet网络进行改进,其中包括简化网络结构、减少模型参数、防止过拟合,在每层卷积层后增加批归一化操作,以加快模型的收敛速度,提高模型的泛化能力。模型测试结果表明:改进后的模型在验证集上的准确率为83.4%,相比改进前的AlexNet模型其准确率提高了6.7%;与其它结构更复杂的网络模型相比,该模型的准确率更高,综合性能更好,可用于款式图的结构特征识别及相似样板匹配。
Objective From the perspective of intelligent pattern making,the closest pattern in the pattern library is matched by the identification of the garment style drawings and new patterns can be developed based on that pattern.This method of pattern making makes maximum use of existing pattern information and simplifies the structure drawing process of the pattern.In order to achieve similarity matching from garment style drawings to patterns,a pattern matching technique based on garment structural feature recognition is proposed.Method The implementation of this technique consists of two main parts.The first is category label design,where certain structural features in the flat style drawing are defined and multi-label categories are set according to the definition according to the knowledge of women′s trouser structural drawing.Then the multi-label categories were transformed into single-label multi-categories,and the link between the flat style diagram,structural features and the pattern was established.Finally,examining the correlation between the labels,the final labels for the experiment were designed.The second part is the model validation.In this part of work,the apparel dataset was established,which took the women′s trousers flat style drawing as the sample.Then the AlexNet network was improved in the experiment.These changes mainly include simplifying the network structure and adding batch normalization operations after each convolutional layer.Results In the process of label design,18 categories of women′s trousers were set through data visualization analysis and the study of correlations between labels.One of the results of the model testing shows the model converges faster after adding the batch normalization after the convolution layer(Fig.10 and Tab.4).The recognition accuracy is higher,with the highest recognition accuracy achieved by adding four layers of batch normalization.Second,it can be seen that the final training accuracy of the model tends to be stable around 99%(Fig.8).The accuracy of
作者
刘蓉
谢红
LIU Rong;XIE Hong(School of Textiles and Fashion,Shanghai University of Engineering Science,Shanghai201620,China)
出处
《纺织学报》
EI
CAS
CSCD
北大核心
2023年第10期134-142,共9页
Journal of Textile Research
基金
上海市科学技术委员会科技创新行动计划资助项目(18030501400)。
关键词
服装结构特征
样板匹配
多标签分类
服装数据集
AlexNet网络
服装制版
garment structure characteristic
pattern matching
multi-label classification
garment dataset
AlexNet neural network
garment pattern making
