Fabric Texture Research Articles

Optimization Analysis of Clothing Fabric Design Based on Autoencoder and Generative Adversarial Neural Network

In order to meet the challenges of increasingly diverse clothing styles and consumer demands in the market, traditional fabric design methods have become difficult to meet the needs of rapid iteration and innovation due to their time-consuming, costly, and subjective preferences of designers. In view of this, we propose an innovative fabric design optimization simulation strategy aimed at breaking through these bottlenecks through technological means. This strategy cleverly integrates advanced technologies of variational autoencoder (VAE) and generative adversarial network (GAN). First, VAE is used to capture and learn the complex distribution characteristics of existing clothing fabric designs, which include key information such as fabric texture, color matching, and structural details. Subsequently, GAN uses the hidden vectors obtained from VAE as input to generate brand new fabric design samples. During the training phase, GAN continuously iterates and optimizes, engaging in intense “adversarial” interactions between its generator and discriminator. The generator is dedicated to creating new samples that are as close to real fabric designs as possible, while the discriminator is responsible for identifying the authenticity of these samples. This process is implemented through backpropagation (BP) loss function, ensuring that the generated fabric design can visually simulate real fabrics. Experimental verification shows that this method can not only effectively generate high-quality and realistic clothing fabric designs, but also greatly shorten the design cycle and reduce costs.

A Novel-Potential Wave-Bump Yarn of Plain Weave Fabric for Fog Harvesting.

With the variety of fibers and fabrics, the studies of the surface structure of the textile yarns, the weave fabric, and their surface wettability are still potential factors to improve and optimize the fog harvesting efficiency. In this work, inspired by the fog harvesting behavior of the desert beetle dorsal surface, a wavy-bumpy structure of post-weave yarn (obtained from woven fabric) was reported to improve large droplet growth (converge) efficiency. In which, this study used tetrabutyl titanate (Ti(OC4H9)4) to waterproof, increase hydrophobicity, and stabilize the surface of yarns and fabric (inspired by the feather structure and lotus leaf surface). Moreover, PDMS oil was used (lubricated) to increase hydrophobicity and droplet shedding on the yarns (inspired by the slippery surface of the pitcher plant) and at the same time, enhance the fog harvesting efficiency of the warp yarn woven fabric (Warp@fabric). In addition, a three-dimensional adjacent yarn structure was arranged by two non-parallel fabric layers. The yarns of the inner and outer layers were intersected at an angle decreasing to zero (mimicking the water transport behavior of Shorebird's beaks). This method helped large droplets quickly form and shed down easily. More than expected, the changes in fabric texture and fiber surface yielded an excellent result. The OBLWB-Warp@fabric's water harvesting rate was about 700% higher than that of the original plain weave fabric (Original@fabric). OBLWB-Warp@fabric's water harvesting rate was about 160% higher than that of Original-Warp@fabric. This shows the great practical application potential of woven fabrics with a low cost and large scale, or you can make use of textile wastes to collect fog, suitable for the current circular economy model. This study hopes to further enrich the materials used for fog harvesting.

Research on design thinking of pleated clothing inspired by fractal geometry

PurposeThis paper summarizes the design thinking of pleated clothing, uses virtual software to simulate the pleated clothing process and clothing effect and applies the fractal geometry principle to conduct relevant experiments, which verifies the possibility of simulation software assisting pleated clothing design, improves research and development efficiency, lays a foundation for future process parameter collection to upgrade the pleated clothing database and realizes the diversity of digital innovative design of pleated clothing.Design/methodology/approachAccording to the principle of self-similarity and fractal dimension in fractal geometry, the design thinking of pleated clothing is put forward and the simulation folding of 3D digital technology is expanded. The design method takes traditional craft and digital technology as an example. The two-dimensional design thinking of pleated clothing is geometric crease thinking of fabric swatch and geometric expansion of basic clothing pattern. Three-dimensional modeling design thinking, such as based on the human body shape, geometric shape and folding mode. As well as digital design thinking, such as the angle of the fabric element and the perspective of garment pattern change.FindingsThis paper analyzes the Y structure of the similar shape “binary tree” in origami art, the principle of infinite folding with 45? As the unit and the point set capacity theory of Cantor concentric circles in the fractal dimension principle expands the design thinking of pleated clothing, applies a large number of experimental cases of design thinking inspired by the fractal geometry of pleated clothing and validates the possibility of simulation software assisting pleated clothing design.Research limitations/implicationsWe argue here that digital technology should continue to be used in a similar way for research and development in other clothing design-related processes in the future. When it comes to the possibilities for digital innovation in relation to pleated clothing, there is a lack of data for modeling the characteristics of such clothing and establishing the processing requirements. Thus, there is scope here for innovation breakthroughs in terms of both the art and the technology associated with clothing design, not to mention how the two are integrated. This paper has laid some potential foundations for this through its elaboration of fractal-inspired design in the context of pleated garments.Practical implicationsNumerous experiments were undertaken and digital simulation software was used to explore various potential designs in the paper. This confirmed the possibility of using simulation software to assist in the design of pleated clothing, which offers the scope for improved research and more efficient development. However, if the processing parameters for changes in the pleated fabric are not set in the software, it is impossible to automatically generate auxiliary clothes. The collection of processing parameters and the creation of a more comprehensive pleated clothing database should therefore be the focus of future research.Social implicationsThe paper is oriented toward future clothing design. There is some impact on research, practice and/or society. This opens up the possibility of using fractal geometry in the design of the folds and dimensional energy in pleated clothing. On the whole, the innovation point is clear and the idea is novel. By seeking the principles of science and technology, sensing the infinite possibilities, improving the design efficiency and inspiring innovative design thinking methods, in order to meet the diversity and personality needs of the external shape of the human body.Originality/valueThe concave and convex texture of pleated fabric is geometric and decorative. It has a direct reference value for pleated clothing design by using the concepts of partial and whole in fractal geometry and the quantity and energy conversion in fractal dimension space. Due to the influence of clothing intelligent manufacturing policy, there is a lack of data sorting for the modeling characteristics and process requirements of pleated clothing, and there is innovative design thinking that needs to be integrated with technology and art, which lays a foundation for the future collection of process parameters to upgrade the pleated clothing database.

DF-YOLO: An attempt on enhancing generalization in fabric defect detection based on YOLO network

This article introduces Domain-fusion YOLO (DF-YOLO), a novel object detection network enhancing YOLOv5 series networks’ generalizability, particularly in fabric defect detection. DF-YOLO incorporates a unique feature extractor in its backbone, enhancing frequency domain feature perception alongside spatial information. Utilizing Fourier transform, the network better discerns fabric textures and anomalies, addressing traditional convolutional neural networks’ limited receptive field issue. Feature fusion and dimension reduction are applied for capturing latent features. In its neck, DF-YOLO integrates an improved BiFPN-based feature-fusion structure and CBAM attention mechanism, optimizing feature selection and fusion across scales. This mitigates information loss due to varying input resolutions and architecture depth. DF-YOLO marks a significant advancement in textile anomaly detection, improving generalization while maintaining efficiency. Its innovative approach benefits automated quality control, offering a sophisticated tool for industries requiring precise fabric inspection.

Biometric-Tuned E-Skin Sensor with Real Fingerprints Provides Insights on Tactile Perception: Rosa Parks Had Better Surface Vibrational Sensation than Richard Nixon.

The dense mechanoreceptors in human fingertips enable texture discrimination. Recent advances in flexible electronics have created tactile sensors that effectively replicate slowly adapting (SA) and rapidly adapting (RA) mechanoreceptors. However, the influence of dermatoglyphic structures on tactile signal transmission, such as the effect of fingerprint ridge filtering on friction-induced vibration frequencies, remains unexplored. A novel multi-layer flexible sensor with an artificially synthesized skin surface capable of replicating arbitrary fingerprints is developed. This sensor simultaneously detects pressure (SA response) and vibration (RA response), enabling texture recognition. Fingerprint ridge patterns from notable historical figures - Rosa Parks, Richard Nixon, Martin Luther King Jr., and Ronald Reagan - are fabricated on the sensor surface. Vibration frequency responses to assorted fabric textures are measured and compared between fingerprint replicas. Results demonstrate that fingerprint topography substantially impacts skin-surface vibrational transmission. Specifically, Parks' fingerprint structure conveyed higher frequencies more clearly than those of Nixon, King, or Reagan. This work suggests individual fingerprint ridge morphological variation influences tactile perception and can confer adaptive advantages for fine texture discrimination. The flexible bioinspired sensor provides new insights into human vibrotactile processing by modeling fingerprint-filtered mechanical signals at the finger-object interface.

A fully integrated electronic fabric-enabled multimodal flexible sensors for real-time wireless pressure-humidity-temperature monitoring

Real-time physiological information monitoring can predict and prevent disease, or improve treatment by early diagnosis. A comprehensive and continuous monitoring of human health requires highly integrated wearable and comfortable sensing devices. To address this need, we propose a low-cost electronic fabric-enabled multifunctional flexible sensing integration platform that includes a flexible pressure sensor for monitoring postural pressure, a humidity sensor for monitoring the humidity of the skin surface, and a flexible temperature sensor for visualizing the ambient temperature around the human body. Thanks to the unique rough surface texture, hierarchical structure, and robust electromechanical features of the MXene-modified nonwoven fabrics, the flexible pressure sensor can achieve a monitoring sensitivity of 1529.1 kPa−1 and a pressure range of 150 kPa, which meets the demand for human pressure detection. In addition, the unique porous structure of the fabric and the stacked multilayer structure of MXene enable the humidity sensor to exhibit extremely high monitoring sensitivity, even through clothing, and still be able to detect the humidity on the skin surface. Temperature sensors based on screen-printed thermochromic liquid crystals enable visual monitoring in the range of 0 °C–65 °C. Through further integration with flexible printed circuit board circuits, we demonstrate a proof-of-concept device that enables real-time monitoring of human physiological information such as physical pressure, humidity, and ambient temperature environment, suggesting that the device provides an excellent platform for the development of commercially viable wearable healthcare monitors.

여성 패션 가방 원단의 텍스쳐(Texture) 디자인에 따른 소비자의 구매 의사 결정에 관한 연구

With the rapid development of modern society, the fabric of bag products is constantly updated, and the purchasing behavior of consumers is constantly changing. This study investigated the effect of the texture design of women's fashion bag fabric on consumers' purchasing decisions by applying the value-attitude-action(VAB) model to adult women aged 18 to 60 in China. According to the analysis results, when looking at consumers' preference for the fabric texture of women's fashion bags, they preferred artificial leather harmonies in homogeneous coordination types, smooth PVC and natural leather in heterogeneous coordination types, and they preferred glitter and spangle textures most in homogeneous coordination. Looking at consumers' preferences for women's bag texture types by age group, those aged 18 to 29 preferred homogeneous coordination of chuede and natural leather, heterogeneous contrast coordination of coarse and smooth natural leather, and those aged 30 to 39 preferred coarse Gretel and smooth natural leather, and those aged 40 to 49 and 50 to 59 preferred rough texture and rough natural leather. According to the analysis of the effect of the texture design of women's fashion bag fabric on consumers' purchasing decisions, it was found that all of the texture designs of women's fashion bag fabric had a significant influence on consumers' purchasing decisions. Among them, heterogeneous harmonious coordination has the greatest impact on consumer value, homogeneous harmonious coordination has the greatest impact on consumer attitudes, and homogeneous contrast coordination has the greatest impact on consumer behavior.

Unsupervised anomaly detection in the textile texture database

Unsupervised anomaly detection in the textile texture database

Research on Fabric Defect Detection Algorithm Based on Improved YOLOv8n Algorithm

In the process of fabric production, various types of defects affect the quality of a fabric. However, due to the wide variety of fabric defects, the complexity of fabric textures, and the concealment of small target defects, current fabric defect detection algorithms suffer from issues such as having a slow detection speed, low detection accuracy, and a low recognition rate of small target defects. Therefore, developing an efficient and accurate fabric defect detection system has become an urgent problem that needs to be addressed in the textile industry. Addressing the aforementioned issues, this paper proposes an improved YOLOv8n-LAW algorithm based on the YOLOv8n algorithm. First, LSKNet attention mechanisms are added to both ends of the C2f module in the backbone network to provide a broader context area, enhancing the algorithm’s feature extraction capability. Next, the PAN-FPN structure of the backbone network is replaced by the AFPN structure, so that the different levels of features of the defects are closer to the semantic information in the progressive fusion. Finally, the CIoU loss is replaced with the WIoU v3 loss, allowing the model to dynamically adjust gradient gains based on the features of fabric defects, effectively focusing on distinguishing between defective and non-defective regions. The experimental results show that the improved YOLOv8n-LAW algorithm achieved an accuracy of 97.4% and a detection speed of 46 frames per second, while effectively increasing the recognition rate of small target defects.

Prediction and modeling of tie-dye polyester fabric's texture and color feature in supercritical carbon dioxide

To mitigate the water-intensive challenges of tie-dye clothing production and address the absence of objective evaluation for finished products, we have explored the correlation between tie-dye techniques in supercritical carbon dioxide (SC-CO2) and image patterns. Employing digital image processing, we extracted the average HSV three-component value of the valid tie-dye area, the proportion of incompletely dyed area in the HSV color space, and the initial three texture features of the digital tie-dye image using the Tamura method. We scrutinized five factors in the supercritical CO2 dyeing process: rotational speed, dyeing temperature, dyeing pressure, dye concentration, and dyeing time. The single-factor climbing test revealed that temperature and pressure are the predominant factors influencing tie-dyeing performance. We propose a correlation between the texture parameters of tie-dyeing and the swelling effect of polyester fabric in a supercritical CO2 environment. Subsequently, we conducted two-factor tests on temperature and pressure to investigate their specific effects on the parameters of supercritical polyester tie-dyeing. The results indicated significant changes in regularity, saturation, and the proportion of undyed area with variations in dyeing temperature and pressure. Building upon these findings, we developed an artificial neural network for predicting tie-dye dyeing temperature and pressure in supercritical CO2, utilizing features regularity, saturation, and proportion of undyed area as inputs. In the 105st round, experimental results demonstrated an accuracy of 0.99445, substantiating the efficacy of tie-dye pattern features in supercritical CO2 for technique forecasting.

Content-based image retrieval of Indian traditional textile motifs using deep feature fusion

In the fast-paced fashion world, unique designs are like early birds, grabbing attention as online shopping surges. Fabric texture plays an immense role in selecting the perfect design. Indian Traditional textile motifs are pivotal, showing rich cultural origins and attracting worldwide art fanatics. Yet, technology-driven abstract forms are posing a challenge for them. The decline of handmade artistic ability due to computerization is concerning. Crafting new designs associated with the latest trends is time- consuming and requires diligence. In this work an interactive CBIR (content-based image retrieval) system is presented. It utilizes deep features from InceptionV3 and InceptionResNetV2 models to match query designs with a database of traditional Indian textiles. Its performance is tested with Caltech-101, Corel-1K state-of-the-art datasets, and Indian Textiles datasets and the results are shown to be finer than the existing approaches. The similarity-based fine-grained saliency maps (SBFGSM) approach is employed to visualize the importance of features. Our approach combines deep feature fusion with PCA dimensionality reduction and speeds up search using a clustering approach. Relevance feedback is employed to refine the retrievals. This tool is expected to benefit designers by accelerating the design cycles by bridging the gap between human creativity and A.I. assistance.

Virtual design of woven fabrics based on parametric modeling and physically based rendering

Textures of woven fabrics are usually designed and produced according to geometric laws in the 2D plane. Physically Based Rendering (PBR) can further optimize and enrich the texture effect, but its application to the more complex 3D structures has been limited. This work reports a method that uses PBR and parametric modeling to construct woven textured materials with centimeter and millimeter level 3D structures. The method can design the structures of various woven fabrics without the need for analyzing the fabric structure details and transfer the inherently iterative work of fabric design to the digital space. The design can be directly applied to mainstream 3D modeling software for virtual presentations in different applications, hence improving the efficiency of woven fabric design and the fidelity of virtual presentation of fabric materials.

Inspiration from hyperbolic paraboloid folding for clothing design

PurposeThis study aims to increase the novelty of clothing design and fabric texture. The element library that can be used for design is systematically summarized. The element database can also be continuously filled according to the existing logic to realize the diversity of design. Improve the theory of fashion design, expand the designer's design ideas and improve design efficiency. Clear design steps and logic can help students and machines learn the design process and promote the development of intelligent design. And verify the feasibility of the simulation software to assist pleated clothing design.Design/methodology/approachFirstly, according to the logical framework of origami theory, different innovative designs and combined designs are made for the basic units of hyperbolic paraboloid, and the element library that can be used for design is systematically summarized. This database can also be continuously filled according to the existing logic to realize the diversity of design. Secondly, it summarizes three methods of pleated element filling clothing – uniform filling method, the irregular filling method and geometric addition method – that improve the theory of fashion design, expand the designer's design ideas and improve design efficiency. Clear design steps and logic can help students and machines learn the design process and promote the development of intelligent design. Finally, the virtual software is used to simulate the effect of pleated clothing, and the three-dimensional simulation software 3dclo is used to make an empirical study on the application of hyperbolic paraboloid origami in clothing pleated design to verify the feasibility of the simulation software to assist pleated clothing design.FindingsThe theoretical results of hyperbolic paraboloid origami are collected and arranged to establish the element library of hyperbolic paraboloid origami. The results expand the designer's design ideas and auxiliary design technology and improve the design efficiency using a sample of hyperbolic paraboloid fabric to verify its practicability and three-dimensional clothing simulation software for exploring the design. The design rules of hyperbolic paraboloid clothing and the realization method of fabric are summarized, including the expansion and combing of elements, the application of size and shape and the method of combination.Research limitations/implicationsOwing to the hyperbolic paraboloid origami’s length shrinkage, the loose computation of clothing requires targeted computation. This paper solely applies a paper model for estimating the shrinkage, and then we tend to subsequently explore the way to precisely compute the porosity, to determine the existing differences in the two-dimensional shrinkage of hyperbolic paraboloid creases of varying materials and to know if the clothing after large-scale production is capable of reaching the anticipated value.Practical implicationsThe exploration of this experiment brings a new 3D experiment process to the design process.Social implicationsThis experiment brings new possibilities for the development of virtual fitting and virtual display in the industry.Originality/valueThis study combines hyperbolic paraboloid origami and clothing and combs and expands the unit with logical thinking to expand the designer's design ideas.

In Situ Growth of (−201) Fiber‐Textured β‐Ga2O3 Semiconductor Tape for Flexible Thin‐Film Transistor

AbstractHigh‐temperature in situ growth of Ga2O3 thin film on flexible substrates for flexible/portable electronic devices is never realized because the conventional polymer substrates cannot meet the thermal‐stability requirements. In the research, for the first time, Ga2O3 thin films are directly grown on SiOx/Al2Ox buffered Hastelloy substrates. With pulsed laser deposition under high temperature, as‐grown Ga2O3 thin films have a fabric texture with a preferred out‐of‐plane orientation along (−201) and a good field‐effect mobility of 18.07 cm2 V−1 s−1. The Ga2O3 coated tape is patterned with transistor arrays and exhibited good performance homogeneity. The representative transistor devices have a threshold voltage of ≈−2.75 V and a breakdown voltage of 116 V measured under a −20 V gate voltage. Moreover, the tape transistors also have good mechanical robustness. The transistors’ good electrical performance, high uniformity, and mechanical robustness suggest that the in situ deposition technique using Hastelloy tape is promising for fabricating various flexible Ga2O3 channeled electronic circuits. Furthermore, it is believed that this technique can be extended to other flexible semiconductor devices that require high‐temperature processing.

Delamination Assessment in Composite Laminates through Local Impulse Excitation Technique (IET)

This paper deals with an innovative nondestructive technique for composites (local-IET), which is based on the Impulse Excitation Technique (IET) and, in the presence of damage, assesses the degradation of the elastic properties of a local region of the laminate by reversibly clamping its boundaries. In this paper, a numerical analysis of the sensitivity of the local-IET to the delamination damage mechanism is conducted. Firstly, a Finite Element (FE) model of the local-IET test is determined through experimental investigations on undamaged composite laminates, which cover a wide range and are made of glass or carbon fibers, through resin infusion or pre-preg consolidation and with unidirectional or fabric textures. The vibrational response of a glass fiber composite with local delamination is then assessed with the local-IET. By modeling the delamination in the simulation environment, the effectiveness of the FE model in replicating the vibrational response, even in the presence of delamination, is shown through a comparison with the experimental results. Finally, the FE model is exploited to perform a sensitivity analysis, showing that the technique is able to detect the presence of delamination.

Innovative Application and Design of Smocking Technology in Local Chinese Clothing Brands

Abstract This study explores the innovative application and design of smocking technology in local Chinese clothing brands, emphasizing the technique’s evolution from a functional necessity to a decorative art form. It delves into the integration of smocking with modern fashion design, highlighting its significance in enhancing fabric texture, silhouette, and overall aesthetic appeal. Through comprehensive analyses of case studies and craft types, the paper reveals how local brands creatively adapt smocking, merging traditional craftsmanship with contemporary design principles. The findings suggest that smocking technology not only enriches the textile design repertoire but also contributes to the global identity and innovation of local Chinese clothing brands, bridging heritage crafts with modern design innovations.

Development of Fabric Surface Texture and Design Natural Dyed Hand-Woven Patterns in the Northern Regio

This article aims to 1) Study the potential production, structure and consumer demand of hand-woven products with natural dyed fabric. 2) Develop the surface texture of fabric and design patterns of hand-woven fabrics that are suitable for textile manufacturing and meet the lifestyle of consumers. 3) Produce the prototypes of hand-woven fabrics which developed surface texture and pattern. 4) Assess consumer satisfaction with prototype product. The target group of this study is entrepreneurs of hand-woven fabrics using natural dyes in the northern region, there are 5 groups with 110 people in total, including the Ampan cotton group, Dao Mang Hemp Fabric Community Enterprise Group, Ban Pa Ngiu Karen Weaving Group, Ban Na Hang Karen Weaving Group, and Ban Huai Ta Community Enterprise Group. The research methods utilized in this study were focus groups, questionnaires, and collaboration with community entrepreneurs in developing and producing woven fabrics. The results show that the 15 woven fabrics developed by the researcher had better surface textures, with a soft touch and good recovery against wrinkles. Additionally, the new weaving structures resulted in new patterns. The researchers designed and produced 21 styles of contemporary casual wear, folk costume, and dress, which meet the requirements of consumers who value eco-friendliness, culture, healthy lifestyles, and minimalism. Furthermore, textile products such as bags, satchels, and hats were also designed and produced, emphasizing the fineness of sewing and the community product standards. Finally, the satisfaction of consumers with the prototype products was assessed, with an average satisfaction level of 4.84 out of 5.

Modeling the relationship between fabric textures and the evoked emotions through different sensory perceptions

Texture represents the surface quality of fabrics, which is one of the key factors for textiles design. Fabric texture can be perceived via sensory perceptions, like vision and touching, causing different psychological feelings and emotions. This paper aimed to model the correlation between fabric textures and the evoked emotions through different sensory perceptions. Firstly, 20 subjects were required to make sensory evaluation on 10 fabric samples, rating fabric textures and the induced emotions via visual and visual-tactile perceptions. Then, the differences in sensory evaluation on fabric textures and the evoked emotions were analyzed. The results showed that only by visual perception, the visual effects dominate the evaluation on fabric textures and emotions. Pile fabrics that look warm can induce positive emotions. By visual-tactile perception, the sensation of contact between fabric and skin accounts due to the intervention of tactile perception. Soft and smooth fabrics, like coral fleece and velvet can stimulate positive emotions. Finally, the emotion-fabric textures model for pleasure and arousal was established using stepwise regression analysis. It’s revealed that only by visual perception, cold/warm had a significant positive effect on pleasure, while no textures were found that have a significant impact on arousal. By visual-tactile perception, both hard/soft and rough/smooth had significant positive impact on pleasure, while thick/thin had a significant negative influence on arousal. The present findings can help direct textiles design, production and sales, so as to meet the specific requirements.

Research on fabric defect detection method based on lightweight network

Due to the complexity of fabric texture, the diversity of defect types and the high real-time requirements of textile enterprises, fabric defect detection is faced with considerable challenges. At present, fabric defect detection algorithms based on deep learning have achieved good results, but there are still some key problems to be solved. Firstly, due to the complex construction of deep learning models and high network complexity, it is difficult to meet the real-time requirements of industrial sites, which limits its application in industrial sites. Secondly, in the face of textile enterprises’ requirements for detection accuracy, how to achieve fabric defect detection through a simpler network model, so as to better balance the accuracy and complexity of deep learning models is a major challenge for textile enterprises and academic researchers. In order to solve these problems, a fabric defect detection method based on lightweight network is proposed in this paper. This method takes lightweight network YOLOv5s model as the infrastructure, integrates Convolution Block Attention Module and Feature Enhancement Module in Backbone part and Neck part respectively, and modifies the loss function of YOLOv5s to CIoU_Loss. Compared with the original YOLOv5s, it makes up for the lack of information extraction ability of the network, improves the speed of model inference and the speed and accuracy of prediction box regression. It provides technical support for the application of lightweight network model in industrial field. The performance of the model is tested by using raw fabric and patterned fabric data sets on the deep learning workstation platform. The experimental results show that when the IoU threshold is 0.5, the mean Accuracy Precision mAP of raw fabric and pattern fabric is 86.4% and 75.8%, respectively, which increases by 7.6% and 1.7% compared with the original YOLOv5s algorithm. The average detection speed is as high as 102 FPS, which can meet the real-time requirement of industrial field target detection.

Functional Analysis of psychological healing based on 3D omni-directional scanning method to simulate the design of natural textured fabrics

Abstract This paper is centered on applying 3D omnidirectional scanning technology to design natural textured fabrics to explore its functionality in psychological healing. The therapeutic potential of fabric art has been widely recognized due to the prevalence of psychological stress in modern society. This study uses 3D omnidirectional scanning technology to scan knitted textures, optimize fabric texture models, and conduct experimental research and material development using elevation and lighting angles. The size, Angle, color, and other factors that affect the experiment of knitted materials were tested, and eight convenient materials were selected for psychological experiments. A two-week experiment was conducted to record and analyze changes in the psychological state of 30 subjects (15 men and 15 women). It was obtained that the intervention group using simulated fabrics decreased from an average of 38.14 to 27.25 in the level of mental awareness, which was significantly higher than the control group (p<0.01). In terms of mental health indicators, the intervention group showed significant improvement in depression, anxiety, and other dimensions. Using 3D omnidirectional scanning technology to simulate natural texture fabric design positively enhances mental awareness and improves mental health, which has a promising potential for psychological healing.