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。

International Journal of Clothing Science and Technology

Purpose This paper aims to propose a new method to solve the two problems in fabric defect detection. Current state-of-the-art industrial products defect detectors are deep learning-based, which incurs some additional problems: (1) The model is difficult to train due to too few fabric datasets for the difficulty of collecting pictures; (2) The detection accuracy of existing methods is insufficient to implement in the industrial field. This study intends to propose a new method which can be applied to fabric defect detection in the industrial field. Design/methodology/approach To cope with exist fabric defect detection problems, the article proposes a novel fabric defect detection method based on multi-source feature fusion. In the training process, both layer features and source model information are fused to enhance robustness and accuracy. Additionally, a novel training model called multi-source feature fusion (MSFF) is proposed to tackle the limited samples and demand to obtain fleet and precise quantification automatically. Findings The paper provides a novel fabric defect detection method, experimental results demonstrate that the proposed method achieves an AP of 93.9 and 98.8% when applied to the TILDA(a public dataset) and ZYFD datasets (a real-shot dataset), respectively, and outperforms 5.9% than fine-tuned SSD (single shot multi-box detector). Research limitations/implications Our proposed algorithm can provide a promising tool for fabric defect detection. Practical implications The paper includes implications for the development of a powerful brand image, the development of “brand ambassadors” and for managing the balance between stability and change. Social implications This work provides technical support for real-time detection on industrial sites, advances the process of intelligent manual detection of fabric defects and provides a technical reference for object detection on other industrial Originality/value Therefore, our proposed algorithm can provide a promising tool for fabric defect detection.

Zhoufeng Liu

Shanliang Liu

Chunlei Li

Bicao Li

Fabric defect detection based on multi-source feature fusion

基于多源特征融合的织物缺陷检测

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

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

基于纺织面料图像特征的智能检测与评估方法

Purpose The purpose of this paper is to gain an in-depth understanding of the research progress, hotspots and future trends in the field of functional clothing. Design/methodology/approach The records of 4,153 pieces of literature related to functional clothing were retrieved from Web of Science by using a comprehensive retrieval strategy. A piece of software, CiteSpace was used as a tool to visualize the results of specific terms, such as author, institution and keyword. By analyzing the knowledge maps with several indicators, the intellectual basis and research fronts for the functional clothing domain could then be demonstrated. Findings The result indicated that functional clothing was a popular research field, with approximately 500 papers published worldwide in 2020. Its main research area was material science and involved public environmental and occupational health, engineering, etc. showing the characteristic of multi-interdisciplinary. Textile Research Journal and International Journal of Clothing Science and Technology were the top two journals in this field. The USA, China, Australia, England and Germany have been active and frequently cooperating with each other. Donghua University, the Hong Kong Polytechnic University and NASA, with the largest number of publications, were identified as the main research drivers. According to the co-citation analysis, thermal stress, nanogenerator and electrospinning were the topics of most cited articles during the past 20 years. Practical implications The findings identified smart clothing and protective clothing to be the research frontiers in the field of functional clothing, which deserved further study in the future. Originality/value The outcomes offered an overview of the research status and future trends of the functional clothing field. It could not only provide scholars with convenience in identifying research hotspots and building potential cooperation in the follow-up research, but also assist beginners in searching core scholars and literature of great significance.

Shuyang Li

Shu Jiang

Miao Tian

Yun Su

Jun Li

Mapping the research status and dynamic frontiers of functional clothing: a review via bibliometric and knowledge visualization

功能性服装研究现状与动态前沿的映射：基于文献计量与知识可视化的综述

Purpose As an ancient art form, embroidery has strong practicality and artistic value. However, current embroidery style migration models produce images with unclear textures and a lack of stitch detail. So, in this paper, we propose a cyclic consistent embroidery style migration network with texture constraints, which is called Texture Cycle GAN (TCGAN). Design/methodology/approach The model is based on the existing Cycle GAN network with an additional texture module. This texture module is implemented using a pre-trained Markovian adversarial network to synthesize embroidery texture features. The overall algorithm consists of two generative adversarial networks (for style migration) and the Markovian adversarial network (for texture synthesis). Findings Qualitative and quantitative experiments show that, compared with the existing convolutional neural network style transfer algorithm, the introduction of the texture-constrained embroidery style transfer model TCGAN can effectively learn the characteristics of style images, generate digital embroidery works with clear texture and natural stitches and achieve more realistic embroidery simulation effects. Originality/value By improving the algorithm for image style migration and designing a reasonable loss function, the generated embroidery patterns are made more detailed, which shows that the model can improve the realism of embroidery style simulation and help to improve the standard of embroidery craftsmanship, thus promoting the development of the embroidery industry.

Chengxia Liu

Jiawen Gu

Lan Yao

Ying Zhang

Research on embroidery style migration model based on texture cycle GAN

基于纹理循环GAN的刺绣风格迁移模型研究

Purpose The attraction of online shopping has raised the demand for customized image searches, mainly in the fashion industry. Daily updates in this industry increase the size of the clothing database at a rapid rate. Hence, it is crucial to design an efficient and fast image retrieval system owing to the short-listing of images depending upon various parameters such as color, pattern, material used, style, etc. Design/methodology/approach This manuscript introduces an improved algorithm for the retrieval of images. The inherited quality of images is first enhanced through intensity modification and morphological operations achieved with the help of a light adjustment algorithm, followed by the speeded up robust feature (SURF) extraction and convolutional neural networks (CNN). Findings The results are validated under three performance parameters (precision, recall and accuracy) on a DeepFashion dataset. The proposed approach helps to extract the most relevant images from a larger dataset based on scores conferred by multiple cloth features to meet the demands of real-world applications. The efficiency of the proposed work is deduced from its effectiveness in comparison to existing works, as measured by performance parameters including precision, recall and F1 score. Further, it is also evaluated against other recent techniques on the basis of performance metrics. Originality/value The presented work is particularly advantageous in the fashion industry for creating precise categorization and retrieving visually appealing photographs from a diverse library based on different designs, patterns and fashion trends. The proposed approach is quite better than the other existing ML/DL-based approaches for image retrieval and classification. This further reflects a significant improvement in customized image retrieval in the field of the fashion industry.

Navneet Kaur

Shreelekha Pandey

Nidhi Kalra

Fashion cloth image categorization and retrieval with enhanced intensity using SURF and CNN approach

基于SURF和CNN方法增强强度的时尚服装图像分类与检索

Purpose Materials with negative Poisson’s ratio are known show improved performance properties. By designing woven fabric structures with auxetic properties, it would be possible to add many functional features to the structure in a single step compared to conventional fabric structures. This study investigated the weave designs for forming auxetic woven fabric structures and also the effects of using elastane yarns on Poisson’s ratio and possible auxetic performances. Design/methodology/approach Weave pattern designs consisting of re-entrant honeycomb and re-entrant zigzag forms, created by long floats and one-to-one intersections of yarns, were included in fabric structure. In addition, fabrics were woven by inserting weft yarns with and without elastane component to evaluate the effect of using elastane component on the auxetic performance. For this purpose, fabrics were woven with 100% polyester warp and weft yarns. Measurement of air permeability and % wetting area of fabrics under different elongations were carried out. Findings Results showed that NPR could be obtained from the designed patterns creating re-entrant honeycomb and re-entrant zigzag geometrical forms on the fabric surface. Also, it was found that the use of elastane yarn had an improving effect on auxetic performance of the woven fabrics. Originality/value By designing auxetic structured woven fabrics could be preferred in areas of use where clothing comfort was desired, due to the transverse expansion behavior as a result of the auxetic effect due to tension and the resulting pore opening effect.

Mine Akgun

Recep Eren

Fatih Suvari

Tuğba Yurdakul

Investigation of the effect of weave and elastane yarn use on auxetic woven fabric formation and various physical performance parameters

织物结构与弹性纱线对负泊松比织物形成及各项物理性能参数影响的研究

Purpose On the basis of demand survey feedback from individuals with disabilities and caregivers, this study designed two sets of functional garments for long-term bedridden patients, with the primary objective of increasing convenience and reducing the physical workload of caregivers. Design/methodology/approach Wear trials were conducted by employing 24 subjects to perform 11 different tasks to compare the performance of the two newly developed garments with that of conventional hospital patient apparel. Task operation time, heart rate (HR), electromyography (EMG) signals, and subjective perceptions were evaluated. Findings The new functional garments reduced the time required to perform tasks by 29–79%, maintained the average HR of caregivers at approximately the resting threshold and resulted in a 37–74% reduction in the root mean square (RMS) of the EMG at the arm muscles in the private and thigh nursing tasks. All the subjective and objective evaluation results of the caregivers demonstrated varying degrees of correlation. Practical implications This study has practical implications for the design of functional clothing for long-term bedridden patients and provides guidance for evaluating the ergonomics of garments that can be utilized only with caregiver support. Originality/value In contrast to previous studies that focused primarily on individuals with disabilities while overlooking the indispensable role of caregivers in the nursing process, this study shifted its emphasis to long-term bedridden patients who relied exclusively on caregivers for daily activities. Additionally, this study attempted to analyze the correlations between the evaluation parameters to explore the relationships between the evaluation methods.

Qingqing Zhang

Jiazhen He

Lili Dai

Zhongwei Chen

Jinping Guan

Yan Chen

Aifang He

Design and ergonomic evaluation of garments for long-term bedridden patients

长期卧床患者服装的设计与人体工效学评价

Purpose The purpose of this paper is to analyze research trends on “Wearable devices in textile filed” from 2010 to 2023 and determine the important keywords, nations and journals associated with this topic. Design/methodology/approach Utilizing journal literature on wearable devices from the 2010 to 2023 Web of Science (WoS) database, this study employs VOSviewer, biblioshiny of the R software package and the specialized software CiteSpace to generate knowledge graphs for measuring retrieval outcomes. Findings The research on wearable devices integrated into textiles between 2010 and 2023 can be divided into three stages: initial slow progress, subsequent rapid advancement and final slow progress, indicating a general rise in the quantity of published material. The terms “fabrication,” “wearable electronics” and “design” are closely linked in this field. China is the most globally networked country in this field, according to the World Collaboration Map. NANO ENERGY ranked first in the number of articles published in journals, with 18 articles and 1,151 citations. Originality/value The study identified the present state and research trends in the field of “Wearable devices in textile filed,” offering valuable information for researchers to enhance their understanding of the field’s progress.

Tianqi Xu

Min Wang

Research progress of wearable devices in textile field: a bibliometric analysis using VOSviewer, RStudio bibliometrix and CiteSpace software tools

纺织领域可穿戴设备研究进展：基于VOSviewer、RStudio bibliometrix和CiteSpace软件工具的文献计量分析

Abbreviation	IJCST
Journal Impact	1.18
Quartiles(Global)	MATERIALS SCIENCE, TEXTILES(Q3)

ISSN	0955-6222, 1758-5953
h-index	42

Publication Information	Publisher: Emerald Group Publishing Ltd.，Publishing cycle: Bimonthly，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 1989，Proportion of original research papers: 96.43%，Self Citation Rate:10.00%， Gold OA Rate: 1.91%
Average review cycle	网友分享经验：平均12.0个月
Average recruitment ratio	网友分享经验：容易

International Journal of Clothing Science and Technology

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Fabric defect detection based on multi-source feature fusion

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

Mapping the research status and dynamic frontiers of functional clothing: a review via bibliometric and knowledge visualization

Research on embroidery style migration model based on texture cycle GAN

Fashion cloth image categorization and retrieval with enhanced intensity using SURF and CNN approach

Research on embroidery style migration model based on texture cycle GAN

Investigation of the effect of weave and elastane yarn use on auxetic woven fabric formation and various physical performance parameters

Fashion cloth image categorization and retrieval with enhanced intensity using SURF and CNN approach

Design and ergonomic evaluation of garments for long-term bedridden patients

Research progress of wearable devices in textile field: a bibliometric analysis using VOSviewer, RStudio bibliometrix and CiteSpace software tools