基于关键点检测的服装廓形识别  

Clothing silhouette recognition based on detection of key points

作　　者：陶金之 夏明[1,2,3] 王伟 TAO Jinzhi;XIA Ming;WANG Wei(College of Fashion and Design,Donghua University,Shanghai 200051,China;Shanghai Key Laboratory of Spacecraft Mechanism,Shanghai 201108,China;Key Laboratory of Clothing Design and Technology,Ministry of Education,Donghua University,Shanghai 200051,China;Jiangyin Zhuri Information Technology Co.,Ltd.,Wuxi,Jiangsu 214434,China)

机构地区：[1]东华大学服装与艺术设计学院,上海200051 [2]上海市空间飞行器机构重点实验室,上海201108 [3]东华大学现代服装设计与技术教育部重点实验室,上海200051 [4]江阴逐日信息科技有限公司,江苏无锡214434

出　　处：《纺织学报》2024年第6期142-148,共7页Journal of Textile Research

基　　金：上海市科技计划资助项目(23DZ2229032);国家自然科学基金资助项目(12172229)。

摘　　要：为精准且快速地实现对服装廓形的判断,以秀场连衣裙为研究对象,提出了基于关键点检测的服装廓形分类算法。使用YOLO v8-Pose模型对秀场连衣裙进行关键点检测,提取服装的肩部、胸部、腰部、臀部和底摆两侧共10个关键点,并生成服装廓形图。通过加入Sobel边缘提取算法改进的DenseNet网络深度提取服装廓形特征,采用余弦相似度算法将其与标准的廓形库中提取的特征相比较,最终实现服装廓形的判别与分类。结果表明,该方法能够快速且准确地实现服装廓形的分类,廓形分类准确率达到了95.9%。Objective The clothing silhouette serves as an important feature for distinguishing and describing garments,and it holds significant relevance in various aspects such as consumer guidance in purchasing,personalized recommendations and customization services,design and production optimization,as well as market trend analysis and research.Previous research generally relied on manually defined key areas and designed complex algorithms to extract key point dimensions,resulting in low efficiency in discrimination.In order to achieve accurate and rapid clothing silhouette classification,this research focuses on runway dresses and proposes a clothing silhouette classification algorithm based on key point detection.Method A convolutional neural network was used in this research to predict ten key points including shoulders,chest,waist,hips,and bottom hem.These key points allow for the extraction of the clothing's silhouette from complex backgrounds,resulting in a silhouette image composed of lines.To extract simple clothing silhouette features,the DenseNet network was enhanced by incorporating the Sobel edge detection algorithm.The extracted features were then compared with the features extracted from a relative standard silhouette database using the cosine similarity algorithm.This approach ultimately enables the discrimination and classification of clothing silhouettes.Results The average error rates for each key point ranged from 0.046 to 0.205.The key points on the sides of the bottom hem had relatively larger average error rates of 0.205 and 0.204.This is mainly due to the deformation of the bottom hem caused by the model's walking movements,making it challenging to discern the key points of the dresses with trailing hemlines caused by stage lighting reflections.The shoulder key points had the lowest error rates,with values of 0.046 and 0.053.This is because the clothing texture stands out more compared to the surface of the human body,resulting in higher accuracy in key point localization.The waist key points had s

关 键 词：服装 廓形分类 YOLO v8-Pose 关键点检测 DenseNet网络 相似度算法 连衣裙 

分 类 号：TS941.17[轻工技术与工程—服装设计与工程]