基于标签优化的裤子廓形智能识别研究

IF 2.2 4区 工程技术 Q1 MATERIALS SCIENCE, TEXTILES
Xuewei Jiang, Ziling Chen, Cheng Chi, Sha Sha, Jun Zhang
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引用次数: 0

摘要

随着网络购物平台的发展,消费者和设计师在购物或设计时需要从大量的服装中进行选择。服装产品的快速识别可以有效地提高设计师和消费者的体验效率。因此,本文采用DeepLabV3+结合深度可分离卷积来提高网络的计算速度。针对语义分割中h形轮廓识别率低的问题,对模糊裤子轮廓样本进行了进一步分析。根据裤子的特征重新定义裤子轮廓,采用裤子轮廓分类方法对数据集标签进行优化。结果表明,该方法显著提高了裤子轮廓识别的准确性和效率。H廓形的召回率、IoU和PA指标分别提高了6%、5%和1%。经过标签优化后,剪影V的分类预测准确率为100%,剪影V的召回率为97%,剪影O的召回率为96%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on intelligent recognition of trouser silhouettes based on label optimization
With the development of online shopping platforms, consumers and designers need to choose from a large number of garments when shopping or designing. Quick identification of clothing products can effectively improve the efficiency of designers’ and consumers’ experience. Therefore, this paper used DeepLabV3+ combined with deep separable convolution to improve the network computation speed. To address the problem of low recognition rate of H-shaped silhouette in semantic segmentation, the fuzzy trouser silhouette samples are further analyzed. The trouser silhouette was redefined according to the characteristics of pants, and the dataset labels were optimized with a trouser silhouette classification method. It was found that the accuracy and efficiency of trouser silhouette recognition were significantly improved. The indicators of recall rate, IoU and PA of H silhouette is improved by 6%, 5%, and 1% respectively. After label optimization, the classification prediction accuracy of silhouette V is 100%, the recall of silhouette V is 97%, and the recall of silhouette O is 96%.
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来源期刊
Journal of Engineered Fibers and Fabrics
Journal of Engineered Fibers and Fabrics 工程技术-材料科学:纺织
CiteScore
5.00
自引率
6.90%
发文量
41
审稿时长
4 months
期刊介绍: Journal of Engineered Fibers and Fabrics is a peer-reviewed, open access journal which aims to facilitate the rapid and wide dissemination of research in the engineering of textiles, clothing and fiber based structures.
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