Purpose The quality of produced textile fibers plays a very important role in the textile industry, and detection and assessment schemes are the key problems. Therefore, the purpose of this paper is to propose a relatively simple and effective technique to detect and assess the quality of produced textile fibers. Design/methodology/approach In order to achieve automatic visual inspection of fabric defects, first, images of the textile fabric are pre-processed by using Block-Matching and 3-D (BM3D) filtering. And then, features of textile fibers image are respectively extracted, including color, texture and frequency spectrum features. The color features are extracted by using hue–saturation–intensity model, which is more consistent with the human vision perception model; texture features are extracted by using scale-invariant feature transform scheme, which is a quite good method to detect and describe the local image features, and the obtained features are robust to local geometric distortion; frequency spectrum features of textiles are less sensitive to noise and intensity variations than spatial features. Finally, for evaluating the quality of the fabric in real time, two quantitatively metric parameters, peak signal-to-noise ratio and structural similarity, are used to objectively assess the quality of textile fabric image. Findings Compared to the quality between production and pre-processing of textile fiber images, the BM3D filtering method is a very efficient technology to improve the quality of textile fiber images. Compared to the different features of textile fibers, like color, texture and frequency spectrum, the proposed detection and assessment method based on textile fabric image feature can easily detect and assess the quality of textiles. Moreover, the objective metrics can further improve the intelligence and performance of detection and assessment schemes, and it is very simple to detect and assess the quality of textiles in the textile industry. Originality/value An intelligent detection and assessment method based on textile fabric image feature is proposed, which can efficiently detect and assess the quality of textiles, thereby improving the efficiency of textile production lines.

Xueqing Zhao

Xin Shi

Kaixuan Liu

Yongmei Deng

International Journal of Clothing Science and Technology

涵盖服装客观测量技术、纤维和织物控制、CAD系统、产品测试、缝纫、机织和针织、检验系统、悬垂和整理等科学技术的各个方面。学术和工业研究成果发表后进行了严格审查。

This journal covers various aspects of clothing science and technology, including objective measurement techniques, fiber and fabric control, computer-aided design (CAD) systems, product testing, sewing, woven and knitted fabrics, inspection systems, draping, and finishing. All academic and industrial research findings undergo rigorous review before publication.

本期刊涵盖了服装科学与技术的各个方面，包括客观测量技术、纤维和织物控制、计算机辅助设计（CAD）系统、产品测试、缝纫、机织和针织、检验系统、悬垂和整理等。所有学术和工业研究成果在发表前均经过严格的审查。

An intelligent detection and assessment method based on textile fabric image feature

An intelligent detection and assessment method based on textile fabric image feature is proposed, which can efficiently detect and assess the quality of textiles, thereby improving the efficiency of textile production lines.

Category	Quartile
工程技术	4
工程技术, 材料科学纺织	3

An intelligent detection and assessment method based on textile fabric image feature

ivySCI AI Smartly Parses PDF, Answers Researchers' Questions, and Helps You Understand Papers in Seconds

Journal Info

Category	Quartile
MATERIALS SCIENCE, TEXTILES	3