[1]王 蕾,顾先雯,张鹏飞,等.基于图像处理的男衬衫外观平整度自动评估[J].服装学报,2025,10(03):268-274.
 WANG Lei,GU Xianwen,ZHANG Pengfei,et al.Automatic Evaluation of Men’s Shirt Appearance Smoothness Based on Image Processing[J].Journal of Clothing Research,2025,10(03):268-274.
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基于图像处理的男衬衫外观平整度自动评估()

《服装学报》[ISSN:2096-1928/CN:32-1864/TS]

卷:
第10卷
期数:
2025年03期
页码:
268-274
栏目:
图像处理及生成技术专题
出版日期:
2025-07-01

文章信息/Info

Title:
Automatic Evaluation of Men’s Shirt Appearance Smoothness Based on Image Processing
作者:
王 蕾;  顾先雯;  张鹏飞;  潘如如;  高卫东
江南大学 特种防护纺织品教育部重点实验室,江苏 无锡 214122
Author(s):
WANG Lei;  GU Xianwen;  ZHANG Pengfei;  PAN Ruru;  GAO Weidong
Key Laboratory of Special Protective Textiles, Ministry of Education, Jiangnan University, Wuxi 214122, China
分类号:
TP 391.41; TS 941.713
文献标志码:
A
摘要:
为解决人工检验对服装平整度评价易产生主观性强、再现性差、效率低等问题,以GB/T 19980—2005中的评分标准为基础,构建男衬衫平整度自动评估算法。该算法基于灰度共生矩阵的方法对衬衫侧缝位置进行分部位特征提取、等级区间划分以及自动评级; 同时,自动赋予衬衫特征部位客观权重比,并参考标准中的评分方法进行加权计算,构建外观平整度整体评分计算模型,实现对男衬衫外观平整度的自动评估。研究表明:相比人工评价方法,自动评估算法具有更好的再现性,且与主观评价结果对比一致性可达0.970,证明该方法可有效解决人工评价方法存在的问题。
Abstract:
To address the limitations of manual garment smoothness evaluation—including strong subjectivity, poor reproducibility and low efficiency this study develops an automated assessment algorithm for men’s shirt smoothness based on the scoring principles of China’s GB/T 19980-2005 standard. The algorithm utilizes Gray-Level Co-occurrence Matrix(GLCM)for region-specific feature extraction at side seams, grade interval division, and automatic rating. It automatically assigns objective weight ratios to key garment features and establishes a comprehensive smoothness scoring model through weighted calculation according to the standard’s scoring method, enabling automated evaluation of men’s shirt appearance smoothness. Experimental results demonstrate that compared with manual evaluation, the proposed algorithm exhibits superior reproducibility and achieves a 0.970 consistency with subjective assessments, proving its effectiveness in overcoming the shortcomings of human-dependent evaluation methods.

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更新日期/Last Update: 2025-06-30