基于图像处理的纺织品耐摩擦色牢度评级

doi:10.13475/j.fzxb.20210702407

纺织学报 ›› 2022, Vol. 43 ›› Issue (12): 131-137.doi: 10.13475/j.fzxb.20210702407

基于图像处理的纺织品耐摩擦色牢度评级

安亦锦¹^,², 薛文良¹^,²(), 丁亦¹^,², 张顺连³

1.东华大学纺织面料技术教育部重点实验室, 上海 201620
2.东华大学纺织学院, 上海 201620
3.广州检验检测认证集团有限公司, 广东广州 511447

收稿日期:2021-07-07 修回日期:2022-09-16 出版日期:2022-12-15 发布日期:2023-01-06
通讯作者: 薛文良
作者简介:安亦锦(1997—),女,硕士生。主要研究方向为人工智能在纺织行业的应用。
基金资助:
国家自然科学基金项目(11804049)

Evaluation of textile color rubbing fastness based on image processing

AN Yijin¹^,², XUE Wenliang¹^,²(), DING Yi¹^,², ZHANG Shunlian³

1. Key Laboratory of Textile Science & Technology, Ministry of Education, Donghua University, Shanghai 201620, China
2. College of Textiles, Donghua University, Shanghai 201620, China
3. Guangzhou Inspection Testing and Certification Group Co., Ltd., Guangzhou, Guangdong 511447, China

Received:2021-07-07 Revised:2022-09-16 Published:2022-12-15 Online:2023-01-06
Contact: XUE Wenliang

摘要/Abstract

摘要：

为解决目前纺织品色牢度人工评级方式的主观性和繁重工作量,结合深度学习与传统纺织检测,以纺织品检测中的纺织品色牢度评级为对象,研究基于图像处理和深度学习的智能评级创新方法。针对场景与问题,选择利用图像处理技术进行采样图像的预处理和分割,在小样本、多分类的实际条件下搭建数据库,利用深度学习完成对摩擦沾色试样色牢度的迅速评级。结果表明,所选择的图像处理技术对图像的处理效果良好,对后续深度学习准确率的提高有辅助效果;深度学习对耐摩擦沾色试样色牢度的评级准确率达到87.5%,高效、客观且准确率高,实现评级操作简易化,利用神经网络达到代替人眼评级过程,提高准确度和改善目前方法的不足。

关键词: 图像处理, 多分类, 深度学习, 纺织品色牢度评级, 耐摩擦沾色色牢度

Abstract:

In order to objectively evaluate textile color fastness and eliminate the heavy workload in manual color evaluation, this paper reports on research into evaluation of textile color rubbing fastness using image processing technology. In view of the scenes and problems, image processing technology was used to treat and segment the sampled images. A database was built for small samples from multiple categories, and deep learning was used to achieve the rapid rating of the color fastness of rubbed samples which was necessary for stain removal. The results show that the selected image processing technique works well and has an auxiliary effect on the subsequent improvement of the accuracy of deep learning. The rating accuracy reaches 87.5%, which is efficient, objective and accurate. The rating operation was simplified, and the neural network was used to replace the human eye rating process, improving the accuracy and overcoming the shortcomings of the current method.

Key words: image processing, multi-category, deep learning, textile color fastness rating, color rubbing fastness

中图分类号:

TS197

安亦锦, 薛文良, 丁亦, 张顺连. 基于图像处理的纺织品耐摩擦色牢度评级[J]. 纺织学报, 2022, 43(12): 131-137.

AN Yijin, XUE Wenliang, DING Yi, ZHANG Shunlian. Evaluation of textile color rubbing fastness based on image processing[J]. Journal of Textile Research, 2022, 43(12): 131-137.

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基于图像处理的纺织品耐摩擦色牢度评级

Evaluation of textile color rubbing fastness based on image processing

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