Fabric Image Research Articles

딥러닝을 이용한 의류 이미지의 텍스타일 소재 분류

As online transactions increase, the image of clothing has a great influence on consumer purchasing decisions. The importance of image information for clothing materials has been emphasized, and it is important for the fashion industry to analyze clothing images and grasp the materials used. Textile materials used for clothing are difficult to identify with the naked eye, and much time and cost are consumed in sorting. This study aims to classify the materials of textiles from clothing images based on deep learning algorithms. Classifying materials can help reduce clothing production costs, increase the efficiency of the manufacturing process, and contribute to the service of recommending products of specific materials to consumers. We used machine vision-based deep learning algorithms ResNet and Vision Transformer to classify clothing images. A total of 760,949 images were collected and preprocessed to detect abnormal images. Finally, a total of 167,299 clothing images, 19 textile labels and 20 fabric labels were used. We used ResNet and Vision Transformer to classify clothing materials and compared the performance of the algorithms with the Top-k Accuracy Score metric. As a result of comparing the performance, the Vision Transformer algorithm outperforms ResNet.

Defect detection of color-patterned fabric based on DenoisingGAN

PurposeThe defect detection problem of color-patterned fabric is still a huge challenge due to the lack of manual defect labeling samples. Recently, many fabric defect detection algorithms based on feature engineering and deep learning have been proposed, but these methods have overdetection or miss-detection problems because they cannot adapt to the complex patterns of color-patterned fabrics. The purpose of this paper is to propose a defect detection framework based on unsupervised adversarial learning for image reconstruction to solve the above problems.Design/methodology/approachThe proposed framework consists of three parts: a generator, a discriminator and an image postprocessing module. The generator is able to extract the features of the image and then reconstruct the image. The discriminator can supervise the generator to repair defects in the samples to improve the quality of image reconstruction. The multidifference image postprocessing module is used to obtain the final detection results of color-patterned fabric defects.FindingsThe proposed framework is compared with state-of-the-art methods on the public dataset YDFID-1(Yarn-Dyed Fabric Image Dataset-version1). The proposed framework is also validated on several classes in the MvTec AD dataset. The experimental results of various patterns/classes on YDFID-1 and MvTecAD demonstrate the effectiveness and superiority of this method in fabric defect detection.Originality/valueIt provides an automatic defect detection solution that is convenient for engineering applications for the inspection process of the color-patterned fabric manufacturing industry. A public dataset is provided for academia.

Digital Color Images as a Tool for the Sustainable Use of Embroidery Elements from Folk Costumes

The aim of the research is to improve the public’s assessment and understanding of the cultural values and history of Bulgaria. The main issues related to the sustainable use of elements of the cultural heritage are defined, and the accessible literary sources related to the digitization of the folklore heritage are reviewed. Shape indices, color, and textural characteristics were obtained from digital color images of the elements of Bulgarian folk costumes. The most informative indices of these features were selected. A kernel variant of the principal component analysis (kPCA) method was used to reduce the data volume of the feature vector. A Naïve Bayes classifier, discriminant analysis, and the support vector method (SVM) were used for classification. The classification accuracy was assessed. In the analysis of the decorative elements of Bulgarian costumes, it was found that the accuracy of classification depended both on the method for reducing the volume of data and on the separability of the classes of data, depending on the classifier used. In the analysis of microscopic images of textile fabrics from Bulgarian costumes, it was found that the accuracy of classification for the studied objects depended both on the method for reducing the volume of data and on the used classifier. In the considered cases, a classification error below 10% was obtained using a non-linear kPCA kernel and SVM with a non-linear partition function. It was proven that the results of this development can be used in the creation of modern cross-stitch patterns, textile patterns, and clothing. The practical application of these research findings has the potential to benefit various stakeholders, including cultural heritage institutions, researchers, artisans, designers, and the general public, promoting a deeper appreciation and sustainable use of costume embroidery elements. Research can continue in the direction of the sustainable use and preservation of embroidery elements of Bulgarian costumes, enriching the understanding of cultural heritage and promoting appreciation for it in future generations.

Self-adaptive Clothing Mapping based Virtual Try-on

Virtual Try-ON (VTON), as an innovative visual application in e-commerce scenarios with great commercial value, has been widely studied in recent years. Due to its better robustness and realistic effect, deformation-synthesize based VTON has become the dominant approach in this field. Existing clothing deformation techniques optimize the mapping relations between the original clothing image and the ground truth image of the worn clothing. However, there are color differences between the original and ground truth clothing images caused by lighting, warping and occlusion. The color differences may lead to misaligned clothing mapping by only minimizing the cost of pixel value difference. Another drawback is that taking the parsing prediction as ground truth will bring alignment remnant, rooting in the processing order of parsing and deformation. Aiming above two drawbacks, we put forward the Self-Adaptive clothing Mapping basEd Virtual Try-ON (SAME-VTON) for achieving realistic virtual try-on results as the same as real. The core of SAME-VTON is the self-adaptive clothing mapping technique, composed of two parts: a color-adaptive clothing mapping module and a parsing-adaptive prediction process. In the color-adaptive clothing mapping module, we map each pixel of the target clothing with a combination of multiple pixel values from the original clothing image, which considers both the position and color changes. Furthermore, different combination weights are learned to increase the diversity of color mapping. In the parsing-adaptive prediction process, the color-adaptive clothing mapping module is adopted to deform clothing firstly, then human parsing result is predicted under the reference of the deformed clothing, which can avoid alignment remnant. Extensive experiments demonstrate that the proposed SAME-VTON with self-adaptive clothing mapping technique can achieve optimal mapping in the case of large color differences and obtain superior results compared with existing deformation-synthesize based VTON.

The Metaverse as an Opportunity for the Development of Fashion Design

This paper refers to the relationship between the fashion system and digital transformation, focusing on fashion streetwear and the Metaverse’s digital worlds. The analysis goes briefly tracing the main stages of the evolution of the digital worlds in recent years, highlighting the points of contact and the contaminations with the material world, which are outlining new connections and unprecedented developments. The image and language of fashion clothing, generally material, maintain its strong communicative value even in the digital world, through the figure of the avatar. The Metaverse is able to create new opportunities for fashion and suggesting future developments that have a strong impact on the lives of young people, increasingly blurring the boundaries between the real and virtual worlds.

“Sowe ye yt together as ye may”: Textile Images as Meditational Stimuli in Fifteenth-Century English Bridgettine Writings

ABSTRACT This article aligns St. Bridget of Sweden’s Liber celestis revelacionum with the genre of gospel meditations popularized in late-medieval England by Nicholas Love’s Mirror of the Blessed Life of Jesus Christ. It considers how descriptions of specific fabrics, with textures that would be known intimately by a fifteenth-century reader, engage and stimulate the devout imagination. The article focuses on the role of textiles and textile imagery within the Bridgettine Syon Abbey, arguing for a particular sensitivity to texture within the Bridgettines’ daily experience, which facilitates their embodied relationship with the Virgin Mary. It concludes with a close reading of Oxford, Bodleian Library, MS Rawlinson C.41, exploring the interrelation between its fifteenth-century Bridgettine treatises, which emphasize the symbolic and spiritual value of textile imagery within late-medieval Marian devotion.

Cross-domain fashion cloth retrieval via novel attention-guided cascade neural network and clothing parsing

Cross-domain fashion retrieval is a feasible method for finding the required clothing among a large number of products. Computer vision-based retrieval algorithms have been developed by many researchers. However, the precise retrieval process still faces challenges such as corrupted query images, ambiguous patterns, and an array of attributes. In this work, a novel attention-guided cascaded network has been proposed for efficient cloth retrieval by overcoming the aforementioned challenges. Initially, the input images are pre-processed with Wavelet integrated Retinex algorithm (WRA) to enhance the query cloth image without diminishing the important information. The attention-guided cascaded network is designed with global feature descriptor (GFD) and local feature descriptor (LFD) modules for extracting the domain-enriched features. These two modules are integrated with an attention block to reduce complexity and improve performance using selected features. In the LFD module, the Clothing Parsing Encoder-Decoder network (CPED-Net) is used to separate the bottom and top regions and sends them into the respective network for fine-grained feature analysis. Finally, global and local features from the modules are combined in similarity analysis for cloth retrieval. The proposed model achieves an overall accuracy of 98.54% based on the fashion retrieval benchmarks dataset. From the experimental results, the proposed model is superior to the state-of-the-art models.

Automatic Fabric Defect Detection Method Using AC-YOLOv5

In the face of detection problems posed by complex textile texture backgrounds, different sizes, and different types of defects, commonly used object detection networks have limitations in handling target sizes. Furthermore, their stability and anti-jamming capabilities are relatively weak. Therefore, when the target types are more diverse, false detections or missed detections are likely to occur. In order to meet the stringent requirements of textile defect detection, we propose a novel AC-YOLOv5-based textile defect detection method. This method fully considers the optical properties, texture distribution, imaging properties, and detection requirements specific to textiles. First, the Atrous Spatial Pyramid Pooling (ASPP) module is introduced into the YOLOv5 backbone network, and the feature map is pooled using convolution cores with different expansion rates. Multiscale feature information is obtained from feature maps of different receptive fields, which improves the detection of defects of different sizes without changing the resolution of the input image. Secondly, a convolution squeeze-and-excitation (CSE) channel attention module is proposed, and the CSE module is introduced into the YOLOv5 backbone network. The weights of each feature channel are obtained through self-learning to further improve the defect detection and anti-jamming capability. Finally, a large number of fabric images were collected using an inspection system built on a circular knitting machine at an industrial site, and a large number of experiments were conducted using a self-built fabric defect dataset. The experimental results showed that AC-YOLOv5 can achieve an overall detection accuracy of 99.1% for fabric defect datasets, satisfying the requirements for applications in industrial areas.

Efficient Fine Tuning for Fashion Object Detection.

Pre-trained models have achieved success in object detection. However, challenges remain due to dataset noise and lack of domain-specific data, resulting in weaker zero-shot capabilities in specialized fields such as fashion imaging. We addressed this by constructing a novel clothing object detection benchmark, Garment40K, which includes more than 140,000 human images with bounding boxes and over 40,000 clothing images. Each clothing item within this dataset is accompanied by its corresponding category and textual description. The dataset covers 2 major categories, pants and tops, which are further divided into 15 fine-grained subclasses, providing a rich and high-quality clothing resource. Leveraging this dataset, we propose an efficient fine-tuning method based on the Grounding DINO framework to tackle the issue of missed and false detections of clothing targets. This method incorporates additional similarity loss constraints and adapter modules, leading to a significantly enhanced model named Improved Grounding DINO. By fine-tuning only a small number of additional adapter module parameters, we considerably reduced computational costs while achieving performance comparable to full parameter fine tuning. This allows our model to be conveniently deployed on a variety of low-cost visual sensors. Our Improved Grounding DINO demonstrates considerable performance improvements in computer vision applications in the clothing domain.

Fabric defect detection using ACS-based thresholding and GA-based optimal Gabor filter

Fabric defect detection is closely related to quality control. Therefore, it is an important topic in the modern textile field. In this article, a new fabric defect detection method using ACS-based thresholding and GA-based optimal Gabor filter is proposed. The multi-level gray-scale image thresholding is improved by using Adaptive cuckoo search (ACS) to calculate the best threshold. In order to suppress texture interference, the optimal Gabor filter calculated by genetic algorithm (GA) is used to achieve the optimal feature extraction of fabric defects. A specific image acquisition system is set up to obtain clear defective fabric images and corresponding defect-free images which are used to build the database. The performance of the method is evaluated by extensive experiments on various types of fabric defects. Experimental results show that the proposed method, effective and robust, is superior than the other six methods.

Texture-based Clustering of Archaeological Textile Images

Texture-based Clustering of Archaeological Textile Images

The improved method in fabric image classification using convolutional neural network

The improved method in fabric image classification using convolutional neural network

Virtual Dressing Room System using Deep Neural Networks

Abstract: In earlier twenty first century, fashion trends has evolved into a way of life. Depending on skin tone, physical stature, gender, as well as social and geographic factors, the amount and style of clothes which we worn might vary significantly. For the huge chunk of population, the in-store buying experience ( buying in the shops ) is still what comes to mind when they think of shopping. The customers can try on clothing in real time, but this process takes too long when there will not be enough trial rooms in the dressing store. The goal here is to create an engaging, interactive, involving and incredibly realistic virtual system that allow the users or customers to select from a wide variety of clothing designs before simulating those outfits on the people or customers virtually. In this study, we have proposed a system that aids in the synchronization of daily attire of the people. The Virtual styling room using live video feed may alter how a person will shop for and tries on new clothing. Customers or people can try on a wide range of clothing items without a need to actually wear them by making use of the idea of “Virtual Reality”. The benefit of doing things in this way is that it would take less time and effort of the people or customers to physically try the garments on. The project we did also aids in market management, reducing the requirement for customers to try on each and every article of clothing in clothing store. Additionally, retailers can save time, money, and space by not keeping a large inventory on hand (in store). This project aims at transferring a target clothing image onto a reference person. Generative adversarial network involves three modules semantic generation, cloth wrapping and try on module

TBINet: fabric defect detection based on a top-down and bottom-up inference network

A key aspect of textile production is the use of automatic defect detection methods for quality control, which is a critical part of the current fabric defect detection system. Benefiting from the explosive development of convolutional neural networks, it has been proved to be feasible to apply it to salient object detection model in fabric defect detection, but how to learn more robust features and better feature fusion for saliency are still complicated tasks. This article presents a novel saliency detection method for fabric defect detection based on both top-down and bottom-up saliency inference combined with two-way information. The top-down process is to infer high-level saliency by gradually using higher-level and richer semantic features on the basis of the backbone. Then a self-fusion enhanced representation module is proposed, which simulates the parallel processing mechanism with residual connection in a top-down manner to generate robust features and effectively characterize the texture features of complex fabrics. In addition, in the down-top process, the interactive feature fusion module is designed, which is used for coarse-to-fine saliency estimation, where the high-level saliency is gradually integrated with finer lower-level features to obtain a fine-grained result. Finally, fabric defect detection is localized by segmenting the generated saliency map. Extensive experimental results on two kinds of fabric image datasets have demonstrated that the proposed TBINet can localize the defect region with high accuracy, and perform favorably against seven state-of-the-art methods on eight widely tested metrics.

Biosynthesis of Silver Nanoparticles Enhanced Antibacterial Silk Face Covering

ABSTRACT Biosynthesis of silver nanoparticles (AgNPs) using plant extracts as reducing agents has recently attracted interest as an environmentally friendly and cost-effective approach. In this study, biosynthesized silver nanoparticles were coated on cotton fabric as an antimicrobial outer middle layer in a silk face mask. The AgNPs were synthesized and characterized by scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX). A solution of 100 ppm AgNPs was coated on cotton fabric by the exhaustion process and quantified according to the procedures of AATCC 100–1999. Top performance characteristics of the barrier face covering were determined according to ASTM F3502–2021. The AgNPs were spherical with particle size ranging 35–88 nm. SEM images of AgNP-coated cotton fabrics showed good particle dispersion that decreased after washing, with 99% reduction in viable E. coli and S. aureus after 30 washing cycles. The developed silk face covering also offered sufficient sub-micron particle filtration efficiency and airflow resistance as level II performance under ASTM classification.

An improved image processing algorithm for visual characteristics in graphic design.

Drawing the clothing plan is an essential part of the clothing industry. However, the irregular shape of clothing, strong deformability and sensitivity to light make the fast and accurate realization of clothing image retrieval a very challenging problem. The successful application of the Transformer in image recognition shows the application potential of the Transformer in the image field. This article proposes an efficient and improved clothing plan based on ResNet-50. Firstly, in the feature extraction section, the ResNet-50 network structure embedded in the Transformer module is used to improve the network's receptive field range and feature extraction ability. Secondly, dense jump connections are added to the ResNet-50 upsampling process, making full use of feature extraction information at each stage, further improving the quality of the generated image. The network consists of three steps: the sketch stage, which aims to predict the color distribution of clothing and obtain watercolor images without gradients and shadows. The second is the thinning stage, which refines the watercolor image into a clothing image with light and shadow effect; The third is the optimization stage, which combines the outputs of the first two stages to optimize the generation quality further. The experimental results show that the improved network's IS and first input delay (FID) scores are 4.592 and 1.506, respectively. High-quality clothing images can be generated only by inputting line drawings and a few color points. Compared with the existing methods, the image generated by this network has excellent advantages in realism and accuracy. This method can combine various feature information of images, improve retrieval accuracy, has strong robustness and practicability, and can provide a reference for the daily work of fashion designers.

FashionSegNet: a model for high-precision semantic segmentation of clothing images

FashionSegNet: a model for high-precision semantic segmentation of clothing images

A spatial–spectral adaptive learning model for textile defect images recognition with few labeled data

Textile defect recognition is a significant technique in the production processes of the textile industry. However, in the practical processes, it is hard to acquire large amounts of textile defect samples. Meanwhile, the textile samples with correct defect labels are rare. To address these two limitations, in this paper, we propose a novel semi-supervised graph convolutional network for few labeled textile defect recognition. First, we construct the graph convolutional network and convolution neural network to extract spectral features and spatial features. Second, the adaptive convolution structure is proposed to generate adaptive kernels according to their dynamically learned features. Finally, the spatial–spectral adaptive unified learning network (SSA-ULNet) is built for limited labeled defective samples, and graph-based semi-supervised learning is constructed. The textile defect recognition model can extract the textile image features through the image descriptors, enabling the whole network to be end-to-end trainable. To evaluate the proposed method, one public dataset and two unique self-built textile defect datasets are used to textile defect recognition. The evaluation results demonstrate that the proposed SSA-ULNet obviously outperforms existing state-of-the-art deep learning methods.

Particle fabric imaging for understanding the monotonic and cyclic shear response of silts

The findings from geotechnical laboratory testing has shown that, in addition to the well understood influence of void ratio and effective confining stress, the particle fabric has a significant effect on the monotonic and cyclic shear behaviour of silts. There is a need to advance our understanding of these effects through systematic quantification of the particle fabric in a given silt matrix in terms of individual particle parameters (e.g., dimensional sizes, volumes, shapes, orientations) as well as inter-particle contact arrangements. In addressing this need, new methodologies were developed for X-ray micro-computed tomography (μ-CT) imaging of silts with specific attention paid to: sampling and preparing of silt specimens, scanning parameters to obtain the needed image resolutions, and digital processing of images to capture individual particle data. The ability of μ-CT imaging to effectively image and capture individual particle parameters and three-dimensional fabric of silt is demonstrated. The particle fabric in terms of orientation in reconstituted silt specimens is illustrated using the μ-CT images produced using standard-size silica particles. The fabric(s) quantified from the imaging of a natural low plastic silt is also presented, and the findings are in accord with those inferred from the mechanical laboratory element testing of the same silt. The work contributes to systematic accounting for fabric in understanding the macroscopic shear behaviour of natural silts.

Fashion Recommender System (FRS): Image Based Engine for Personalized Outfit

The methods used to predict how highly a person will evaluate a product or a group of people are known as recommendation systems. Books, movies, restaurants, and other products can be among the things commonly recommended. Objects where people differ in their preferences matter a lot. For predicting the preferences 2 methods are used, one a content based approach that considers the characteristic of an item, and the other is a collaborative method that evaluates choices by taking into account previous user behaviour. In this paper, a system for recommending fashion items is proposed, one that will base its recommendations on the provided clothing images' styles. The upper body and lower body clothing images, as well as those of a human model, are the main focus of this work. We have used the fashion image dataset from Kaggle website. This paper presents an idea to develop a content based recommendation system that uses convolutional neural network model, ResNet-50.