The state-of-the-art multispectral imaging system can directly acquire the reflectance of a single strand of yarn that is impossible for traditional spectrophotometers. Instead, the spectrophotometric reflectance of a yarn winding, which is constituted by yarns wound on a background card, is regarded as the yarn reflectance in textile. While multispectral imaging systems and spectrophotometers can be separately used to acquire the reflectance of a single strand of yarn and corresponding yarn winding, the quantitative relationship between them is not yet known. In this paper, the relationship is established based on models that describe the spectral response of a spectrophotometer to a yarn winding and that of a multispectral imaging system to a single strand of yarn. The reflectance matching function from a single strand of yarn to corresponding yarn winding is derived to be a second degree polynomial function, which coefficients are the solutions of a constrained nonlinear optimization problem. Experiments on 100 pairs of samples show that the proposed approach can reduce the color difference between yarn windings and single strands of yarns from 2.449 to 1.082 CIEDE2000 units. The coefficients of the optimal reflection matching function imply that the reflectance of a yarn winding measured by a spectrophotometer consists of not only the intrinsic reflectance of yarn but also the nonignorable interreflection component between yarns.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1364/JOSAA.32.001459 | DOI Listing |
Sci Rep
July 2024
School of Automation, Zhejiang Institute of Mechanical & Electrical Engineering, Hangzhou, 310053, China.
The automated replacement of empty tubes in the yarn bank is a critical step in the process of automatic winding machines with yarn banks, as the real-time detection of depleted yarn on spools and accurate positioning of empty tubes directly impact the production efficiency of winding machines. Addressing the shortcomings of traditional methods, such as poor adaptability and low sensitivity in optical and visual tube detection, and aiming to reduce the computational and detection time costs introduced by neural networks, this paper proposes a lightweight yarn spool detection model based on YOLOv8. The model utilizes Darknet-53 as the backbone network, and due to the dense spatial distribution of yarn spool targets, it incorporates large selective kernel units to enhance the recognition and positioning of dense targets.
View Article and Find Full Text PDFData Brief
June 2024
Algoritmi Research Centre, School of Engineering, University of Minho, Guimaraes, Portugal.
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.
View Article and Find Full Text PDFiScience
March 2024
Optics and Thermal Radiation Research Center, Institute of Frontier & Interdisciplinary Science, Shandong University, Qingdao 266237, China.
In the article (Advanced Materials 2023; 2305914, https://doi.org/10.1002/adma.
View Article and Find Full Text PDFSensors (Basel)
August 2023
Research Institute for Textile and Clothing (FTB), Niederrhein University of Applied Sciences, Webschulstr. 31, 41065 Mönchengladbach, Germany.
A person's body temperature is an important indicator of their health status. A deviation of that temperature by just 2 °C already has or can lead to serious consequences, such as fever or hypothermia. Hence, the development of a temperature-sensing and heatable yarn is an important step toward enabling and improving the monitoring and regulation of a person's body temperature.
View Article and Find Full Text PDFACS Appl Nano Mater
July 2023
FAMU-FSU College of Engineering, High-Performance Materials Institute, Florida State University, 2005 Levy Avenue, Tallahassee, Florida 32311, United States.
An approach is established for fabricating high-strength and high-stiffness composite laminates with continuous carbon nanotube (CNT) yarns for scaled-up mechanical tests and potential aerospace structure applications. Continuous CNT yarns with up to 80% degree of nanotube alignment and a unique self-assembled graphitic CNT packing result in their specific tensile strengths of 1.77 ± 0.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!