Search Results (96)

This study presents an advanced framework for fabric texture classification by leveraging macro- and micro-texture extraction techniques integrated with deep learning architectures. Co-occurrence histograms, local binary patterns (LBPs), and albedo-dependent feature maps were employed to comprehensively capture the surface properties of fabrics. A late fusion approach was applied using four state-of-the-art convolutional neural networks (CNNs): InceptionV3, ResNet50_V2, DenseNet, and VGG-19. Excellent results were obtained, with the ResNet50_V2 achieving a precision of 0.929, recall of 0.914, and F1 score of 0.913. Notably, the integration of multimodal inputs allowed the models to effectively distinguish challenging fabric types, such as cotton–polyester and satin–silk pairs, which exhibit overlapping texture characteristics. This research not only enhances the accuracy of textile classification but also provides a robust methodology for material analysis, with significant implications for industrial applications in fashion, quality control, and robotics. Full article

Sign language recognition plays a crucial role in enabling communication for deaf individuals, yet current methods face limitations such as sensitivity to lighting conditions, occlusions, and lack of adaptability in diverse environments. This study presents a wearable multi-channel tactile sensing system based on smart textiles, designed to capture subtle wrist and finger motions for static sign language recognition. The system leverages triboelectric yarns sewn into gloves and sleeves to construct a skin-conformal tactile sensor array, capable of detecting biomechanical interactions through contact and deformation. Unlike vision-based approaches, the proposed sensor platform operates independently of environmental lighting or occlusions, offering reliable performance in diverse conditions. Experimental validation on American Sign Language letter gestures demonstrates that the proposed system achieves high signal clarity after customized filtering, leading to a classification accuracy of 94.66%. Experimental results show effective recognition of complex gestures, highlighting the system’s potential for broader applications in human-computer interaction. Full article

The aim of this study was to compare the types, textile structures, labels, and fiber compositions of 64 red garments submitted as evidence in selected criminal cases between 2022 and 2024. The research enhanced the current knowledge of the characteristics of red clothing available to consumers and demonstrated the relevance of textile analysis in forensic science. Knitted fabrics were the most commonly used in the garments, followed by woven fabrics, nonwovens, and felts. Fiber identification focused on color and shade, generic classification, morphological structure, and chemical composition, revealing both similarities and distinctions among the samples. In a small percentage of cases, label information was found to be inaccurate. The study also examined the fiber content of threads, patches, logos, prints, and embroidery, underscoring the forensic potential of these often-overlooked elements. The identification of over 300 individual fibers enabled a critical evaluation of the analytical procedures and confirmed their effectiveness in forensic contexts. Full article

This paper presents the design and implementation of an autonomous robotic facility for textile sorting and recycling, leveraging advanced computer vision and machine learning technologies. The system enables real-time textile classification, localization, and sorting on a dynamically moving conveyor belt. A custom-designed pneumatic gripper is developed for versatile textile handling, optimizing autonomous picking and placing operations. Additionally, digital simulation techniques are utilized to refine robotic motion and enhance overall system reliability before real-world deployment. The multi-threaded architecture facilitates the concurrent and efficient execution of textile classification, robotic manipulation, and conveyor belt operations. Key contributions include (a) dynamic and real-time textile detection and localization, (b) the development and integration of a specialized robotic gripper, (c) real-time autonomous robotic picking from a moving conveyor, and (d) scalability in sorting operations for recycling automation across various industry scales. The system progressively incorporates enhancements, such as queuing management for continuous operation and multi-thread optimization. Advanced material detection techniques are also integrated to ensure compliance with the stringent performance requirements of industrial recycling applications. Full article

Fire-resistant coatings have emerged as crucial materials for reducing fire hazards in various industries, including construction, textiles, electronics, and aerospace. This review provides a comprehensive account of recent advances in fire-resistant coatings, emphasizing environmentally friendly and high-performance systems. Beginning with a classification of traditional halogenated and non-halogenated flame retardants (FRs), this article progresses to cover nitrogen-, phosphorus-, and hybrid-based systems. The synthesis methods, structure–property relationships, and fire suppression mechanisms are critically discussed. A particular focus is placed on bio-based and waterborne formulations that align with green chemistry principles, such as tannic acid (TA), phytic acid (PA), lignin, and deep eutectic solvents (DESs). Furthermore, the integration of nanomaterials and smart functionalities into fire-resistant coatings has demonstrated promising improvements in thermal stability, char formation, and smoke suppression. Applications in real-world contexts, ranging from wood and textiles to electronics and automotive interiors, highlight the commercial relevance of these developments. This review also addresses current challenges such as long-term durability, environmental impacts, and the standardization of performance testing. Ultimately, this article offers a roadmap for developing safer, sustainable, and multifunctional fire-resistant coatings for future materials engineering. Full article

Cannabis sativa L. is divided into three main groups: drug-type, intermediate-type, and fiber-type. The presence of tetrahydrocannabinol (THC) exceeding 0.2–0.3% in drug-type and intermediate Cannabis that utilized for recreational and medicinal purposes renders them illegal due to potential mental health implications. Fiber-type contains high cannabidiol (CBD) and low THC, making it suitable for household use such as textiles and animal feed. Accurate classification is essential to prevent misuse of the plant. High-performance thin-layer chromatography (HPTLC) and ultra-performance liquid chromatography (UPLC), used respectively for the qualitative and quantitative analyses of THC and CBD particularly in female inflorescences, categorized 85 samples of 46 cultivars used in this study into three distinct chemotypes. While chemotype analysis of a very specific organ of the plants accurately identifies Cannabis groups, it requires time-consuming plant development to maturity. Genotype analysis targeting tetrahydrocannabinolic acid synthase (THCAS) and cannabidiolic acid synthase (CBDAS) genes offers a faster alternative for classifying Cannabis types, allowing for sample determination from any part at any developmental stage of the plant. DNA sequencing allowed a phylogenetic analysis based on these genes, classifying all 85 samples of 46 cultivars into the same three groups identified by chemotype analysis. This study is the first to successfully examine the relationship between chemotype and genotype in 85 samples of 46 cultivars. Rapid identification of Cannabis types through genotype analysis lays the groundwork for future development of detection kits. Full article

Cotton is a vital economic crop in global agriculture and the textile industry, contributing significantly to food security, industrial competitiveness, and sustainable development. Traditional technologies such as spectral imaging and machine learning improved cotton cultivation and processing, yet their performance often falls short in complex agricultural environments. Deep learning (DL), with its superior capabilities in data analysis, pattern recognition, and autonomous decision-making, offers transformative potential across the cotton value chain. This review highlights DL applications in seed quality assessment, pest and disease detection, intelligent irrigation, autonomous harvesting, and fiber classification et al. DL enhances accuracy, efficiency, and adaptability, promoting the modernization of cotton production and precision agriculture. However, challenges remain, including limited model generalization, high computational demands, environmental adaptability issues, and costly data annotation. Future research should prioritize lightweight, robust models, standardized multi-source datasets, and real-time performance optimization. Integrating multi-modal data—such as remote sensing, weather, and soil information—can further boost decision-making. Addressing these challenges will enable DL to play a central role in driving intelligent, automated, and sustainable transformation in the cotton industry. Full article

The textile and apparel industry faces significant sustainability challenges due to the high volume of waste it generates and the limitations of current recycling systems. Automation in textile waste management has emerged as a promising solution to enhance material recovery through accurate and efficient sorting. This systematic literature review, conducted using the PRISMA-guided PSALSAR methodology, examines recent advancements in computer-based sorting technologies applied in textile recycling. This study identifies and evaluates major technological methods often integrated with machine learning, deep learning, or computer vision models. The strengths and limitations of these approaches are discussed, highlighting their impact on classification accuracy, reliability, and scalability. This review emphasizes the need for further research on blended fiber detection, data availability, and hybrid models to advance automated textile waste management and support a sustainable circular economy. Full article

Advanced deep learning has assisted in various operations in different industries. In the textile industry, the professional must be trained and experienced in fabric classification. Fabrics such as Abaca are difficult to classify as the same base material is intertwined with a different material. The versatile nature of Abaca is used in various products including paper bills, ropes, handwoven handicrafts, and fabric. Abaca fabric is an unsought product of fabric due to its rough texture. Blended Abaca fabrics are traditionally mixed with cotton, silk, and polyester. Due to the combination of the characteristics of the materials, the fabric classification is prone to human error. Therefore, we created a device capable of classifying blends of Abaca fabric using YOLOv8 architecture. We used a Raspberry Pi 4B with camera module v3 to capture images for classification. The dataset consisted of four blends, specifically Abaca, Cotton Abaca, Polyester Abaca, and Silk Abaca. A total 500 images were used to test the model’s performance, and the performance accuracy was 94.6%. Full article

The Barong Tagalog is a formal attire traditionally worn by men for special occasions. Despite its cultural significance, distinguishing between the Cocoon silk, Jusi, and Piña-silk types of Philippine Barong Tagalog is challenging due to their similar colors. Although these textiles share similar hues, their patterns and textures differ significantly, leading to potential misidentification by individuals. To identify structural patterns in textile classification, machine learning was used. Especially convolutional neural networks (CNNs) and support vector machines (SVMs) were used. The system employed a Raspberry Pi (RPI) V4 as the microprocessor and an RPI Camera V2 for image capture. The system performance was validated involving 30 sample images per classification and an additional 30 unknown samples. The system correctly classified 64 out of 90 sample images with an accuracy of 71.1%. For evaluation, a confusion matrix was determined. By combining CNN V1 and SVM V2, the textile analysis using image processing was conducted precisely to identify Barong Tagalog textiles. Full article

The integration of machine learning (ML) has begun to reshape the development of advanced polymeric materials used in technical textiles. Polymeric materials, with their versatile properties, are central to the performance of technical textiles across industries such as healthcare, aerospace, automotive, and construction. By utilizing ML and AI, researchers are now able to design and optimize polymers for specific applications more efficiently, predict their behavior under extreme conditions, and develop smart, responsive textiles that enhance functionality. This review highlights the transformative potential of ML in polymer-based textiles, enabling advancements in waste sorting (with classification accuracy of up to 100% for pure fibers), material design (predicting stiffness properties within 10% error), defect prediction (enabling proactive interventions in fabric production), and smart wearable systems (achieving response times as low as 192 ms for physiological monitoring). The integration of AI technologies drives sustainable innovation and enhances the functionality of textile products. Through case studies and examples, this review provides guidance for future research in the development of polymer-based technical textiles using AI and ML technologies. Full article

Egyptian cotton fibres have worldwide recognition due to their distinct quality and luxurious textile products known by the “Egyptian Cotton“ label. However, cotton fibre trading in Egypt still depends on human grading of cotton quality, which is resource-intensive and faces challenges in terms of subjectivity and expertise requirements. This study investigates colour vision and transfer learning to classify the grade of five long (Giza 86, Giza 90, and Giza 94) and extra-long (Giza 87 and Giza 96) staple cotton cultivars. Five Convolutional Neural networks (CNNs)—AlexNet, GoogleNet, SqueezeNet, VGG16, and VGG19—were fine-tuned, optimised, and tested on independent datasets. The highest classifications were 75.7%, 85.0%, 80.0%, 77.1%, and 90.0% for Giza 86, Giza 87, Giza 90, Giza 94, and Giza 96, respectively, with F1-Scores ranging from 51.9–100%, 66.7–100%, 42.9–100%, 40.0–100%, and 80.0–100%. Among the CNNs, AlexNet, GoogleNet, and VGG19 outperformed the others. Fused CNN models further improved classification accuracy by up to 7.2% for all cultivars except Giza 87. These results demonstrate the feasibility of developing a fast, low-cost, and low-skilled vision system that overcomes the inconsistencies and limitations of manual grading in the early stages of cotton fibre trading in Egypt. Full article

Smart textiles are an evolving field, but challenges in durability, washing, interfacing, and sustainability persist. Widespread adoption requires robust, lightweight, fully integrated fiber-based conductors. This paper proposes using ultralong carbon nanotube (UCNT) yarns with a width-to-length ratio of several orders of magnitude larger than typical carbon nanotube fibers. These yarns enable the manufacturing of stable, workable structures, composed of a network of twisted fibers (tows), which are suitable for fabric integration. Our research includes the creation of textile prototype demonstrators integrated with coated and non-coated UCNT yarns, tested under military-grade standards for both mechanical durability and electric functionality. The demonstrators were evaluated for their electrical and mechanical properties under washability, abrasion, and weathering. Notably, polymer-coated UCNT yarns demonstrated improved mechanical durability and electrical performance, showing promising results. However, washing tests revealed the presence of UCNT nanofibers in the residue, raising concerns due to their classification as hazards by the World Health Organization. This paper examines the sources of fiber release and discusses necessary improvements to coating formulations and testing protocols to mitigate fiber loss and enhance their practical viability. These findings underscore both the potential and limitations of UCNT yarns in military textile applications. Full article

Waste management is one of the key areas where circular models should be promoted, as it plays a crucial role in minimizing environmental impact and conserving resources. Effective material identification and classification are essential for optimizing recycling processes and selecting the appropriate production equipment. Proper sorting of materials enhances both the efficiency and sustainability of recycling systems. The proposed study explores the potential of using a cost-effective strategy based on hyperspectral imaging (HSI) to classify space waste products, an emerging challenge in waste management. Specifically, it investigates the use of HSI sensors operating in the near-infrared range to detect and identify materials for sorting and classification. Analyses are focused on textile and plastic materials. The results show promising potential for further research, suggesting that the HSI approach is capable of effectively identifying and classifying various categories of materials. The predicted images achieve exceptional sensitivity and specificity, ranging from 0.989 to 1.000 and 0.995 to 1.000, respectively. Using cost-effective, non-invasive HSI technology could offer a significant improvement over traditional methods of waste classification, particularly in the challenging context of space operations. The implications of this work identify how technology enables the development of circular models geared toward sustainable development hence proper classification and distinction of materials as they allow for better material recovery and end-of-life management, ultimately contributing to more efficient recycling, waste valorization, and sustainable development practices. Full article

The YOLO series is widely recognized for its efficiency in the real-time detection of objects within images and videos. Accurately identifying and classifying fabric types in the textile industry is vital to ensuring quality, managing supply, and increasing customer satisfaction. We developed a method for fabric type classification and object detection using the YOLOv5 architecture. The model was trained on a diverse dataset containing images of different fabrics, including cotton, hanbok, dyed cotton yarn, and a plain cotton blend. We conducted a dataset preparation process, including data collection, annotation, and training procedures for data augmentation to improve model generalization. The model’s performance was evaluated using precision, recall, and F1-score. The developed model detected and classified fabrics with an accuracy of 81.08%. YOLOv5s allowed a faster performance than other models. The model can be used for automated quality control, inventory tracking, and retail analytics. The deep learning-based object detection method with YOLOv5 addresses challenges related to fabric classification, improving the abilities and productivity of manufacturing and operations. Full article