The quality of yarn is essential in the control of the fabrics processes. There is some commercial equipment that measures the quality of yarn based on sensors, of different types, used for collecting data about some textile yarn characteristic parameters. The irregularity of the textile thread influences its physical properties/characteristics and there may be a possibility of a break in the textile thread during the fabric manufacturing process. This can contribute to the occurrence of unwanted patterns in fabrics that deteriorate their quality. The existing equipment, for the above-mentioned purpose, is characterized by its high size and cost, and for allowing the analysis of only few yarn quality parameters. The main findings/results of the study are the yarn analysis method as well as the developed algorithm, which allows the analysis of defects in a more precise way. Thus, this paper presents the development and results obtained with the design of a mechatronic prototype integrating a computer vision system that allows, among other parameters, the analysis and classification, in real time, of the hairs of the yarn using artificial intelligence techniques. The system also determines other characteristics inherent to the yarn quality analysis, such as: linear mass, diameter, volume, twist orientation, twist step, average mass deviation, coefficient of variation, hairiness coefficient, average hairiness deviation, and standard hairiness deviation, as well as performing spectral analysis. A comparison of the obtained results with the designed system and a commercial equipment was performed validating the undertaken methodology.

This paper introduces an online dataset focused on detecting hairiness in yarn, including loop and protruding fibers. The dataset is designed for use in assessing artificial intelligence algorithms. The dataset consists of 684 original images. Through augmentation, this number increases to 1644, with 11,037 annotations derived from videos featuring 56.4tex purple cotton yarn. The videos were captured during the winding and unwinding processes of the purple yarn coil. An image acquisition system capable of capturing high-resolution images while the yarn is in motion was used, reaching speeds of up to 4.2 m/s and producing images with a resolution of 1.6M pixels. This dataset containing 100m of purple cotton yarn images was recorded and is available for download in various formats, including, among others, YOLOv8, YOLOv5, YOLOv7, MT-YOLOv6, COCO JSON, YOLO Darknet, Pascal VOC XML, TFRecord, CreateML JSON. Within an interface developed for a designed mechatronic prototype, users can choose to gather images or videos of yarn. Various characteristics of the yarn, such us: diameter, linear mass, volume, twist orientation, twist step, number of cables, hairiness index, number of loose fibers, thin places, thick places, neps (mass parameters) and U, CV and sH (statistical parameters) can be obtained. Recently, this online yarn spinning dataset was employed to validate artificial neural network models for real-time detection of hairiness in yarns, including loop fibers and protruding fibers. The dataset presented, with its clear annotations and wide array of augmentation techniques, serves as a foundational resource for prospective studies in textile engineering, enabling progress in the analysis and comprehension of yarn analysis.

Blind people often encounter challenges in managing their clothing, specifically in identifying defects such as stains or holes. With the progress of the computer vision field, it is crucial to minimize these limitations as much as possible to assist blind people with selecting appropriate clothing. Therefore, the objective of this paper is to use object detection technology to categorize and detect stains on garments. The defect detection system proposed in this study relies on the You Only Look Once (YOLO) architecture, which is a single-stage object detector that is well-suited for automated inspection tasks. The authors collected a dataset of clothing with defects and used it to train and evaluate the proposed system. The methodology used for the optimization of the defect detection system was based on three main components: (i) increasing the dataset with new defects, illumination conditions, and backgrounds, (ii) introducing data augmentation, and (iii) introducing defect classification. The authors compared and evaluated three different YOLOv5 models. The results of this study demonstrate that the proposed approach is effective and suitable for different challenging defect detection conditions, showing high average precision (AP) values, and paving the way for a mobile application to be accessible for the blind community.

In textile manufacturing, ensuring high-quality yarn is crucial, as it directly influences the overall quality of the end product. However, imperfections like protruding and loop fibers, known as ‘hairiness’, can significantly impact yarn quality, leading to defects in the final fabrics. Controlling yarn quality in the spinning process is essential, but current commercial equipment is expensive and limited to analyzing only a few parameters. The advent of artificial intelligence (AI) offers a promising solution to this challenge. By utilizing deep learning algorithms, a model can detect various yarn irregularities, including thick places, thin places, and neps, while characterizing hairiness by distinguishing between loop and protruding fibers in digital yarn images. This paper proposes a novel approach using deep learning, specifically, an enhanced algorithm based on YOLOv5s6, to characterize different types of yarn hairiness. Key performance indicators include precision, recall, F1-score, mAP0.5:0.95, and mAP0.5. The experimental results show significant improvements, with the proposed algorithm increasing model mAP0.5 by 5% to 6% and mAP0.5:0.95 by 11% to 12% compared to the standard YOLOv5s6 model. A 10k-fold cross-validation method is applied, providing an accurate estimate of the performance on unseen data and facilitating unbiased comparisons with other approaches.

The quality of yarn is essential in the control of the fabrics processes. There is some commercial equipment that measures the quality of yarn based on sensors, of different types, used for collecting data about some yarn characteristic parameters. The existing equipment, for the above-mentioned purpose, is characterized by its high size and cost, and for allowing the analysis of only few yarn quality parameters. Thus, this paper presents the development and results obtained with the design of a mechatronic prototype integrating a computer vision system that allows, among other parameters, the analysis and classification, in real time, of the hairs of the yarn using artificial intelligence techniques. The system also determines other characteristics inherent to the yarn quality analysis, such as: linear mass, diameter, volume, twist orientation, twist step, average mass deviation, coefficient of variation, hairiness coefficient, average hairiness deviation and standard hairiness deviation, as well as performs spectral analysis. A comparison of the obtained results with the designed system and a commercial equipment was performed validating the undertaken methodology.