摘要
为提升缝纫平整度客观评级精准性和普适性,分别从织物和服装2方面对方法标准和评级技术进行梳理和评述。系统阐述国内外织物洗后接缝外观平整度、服装缝纫平整度主观评级方法标准,对比当前服装产品的测试方法、测试部位和技术要求,梳理接触式测量法、图像分析法、三维形态分析法等客观评级方法。着重探讨缝纫平整度客观评级中存在的若干关键问题,需要结合专家评级机制,提高主客观一致性、检测速度、评级精度,降低检测设备成本,以期助推客观、准确、快捷的商用检测设备的研发,为服装缝纫平整度相应客观标准的制定和实施提供参考依据。
In order to improve the accuracy and universality of objective evaluation of sewing flatness,the method standards and assessing technologies were reviewed and commented. The standards of subjective rating methods of seams appearance flatness of fabrics after cleansing and garment sewing flatness were systematically expounded. The testing methods, testing parts and technical requirements of the clothing products were compared, and the objective rating methods such as contact measurement, image analysis and three-dimensional morphological analysis were combed out. The existing key problems in current objective grading were discussed. It is necessary to integrate the expert assess mechanism, improve the evaluate speed, accuracy and subjective-objective consistency, and reduce the cost of detection equipment in future research, with the aim to promote the development of objective, accurate and fast commercial detection equipment, provide reference for formulation and implementation of corresponding objective standards for garment sewing flatness.
作者
袁志磊
徐平华
丁雪梅
吴雄英
徐明慧
YUAN Zhilei;XU Pinghua;DING Xuemei;WU Xiongying;XU Minghui(Shanghai Customs District,Shanghai 200135,China;School of Fashion Design&Engineering,Zhejiang Sci-Tech University,Hangzhou 310018,China;Clothing Engineering Research Center of Zhejiang Province,Hangzhou 310018,China;College of Fashion and Design,Shanghai 200051,China;Key Laboratory of Clothing Design and Technology(Donghua University),Ministry of Education,Shanghai 200051,China)
出处
《印染助剂》
CAS
2022年第1期59-64,共6页
Textile Auxiliaries
基金
上海市技术标准项目(19DZ2200200)
国家自然科学基金青年基金项目(61702460)
浙江理工大学科研业务费专项资金资助项目(2021Q057)
浙江理工大学优秀研究生学位论文培育基金(LWYP2020055,LW-YP2021053)
服装设计国家级虚拟仿真实验教学中心项目(zx20212004)。
关键词
缝纫平整度
特征提取
机器视觉
模式识别
综合评价
sewing flatness
feature extraction
machine vision
pattern recognition
comprehensive evaluation