Textile component identification method based on hyperspectral imaging

A technology of hyperspectral imaging and textiles, which is applied in the fields of digital image processing and spectral analysis, can solve the problems of unquantifiable proportions and identification of textiles with various components, achieve fast data collection and processing, reduce identification costs, and avoid The effect of economic loss

Active Publication Date: 2015-04-29
ZHEJIANG SCI-TECH UNIV
View PDF5 Cites 20 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] However, the above-mentioned invention is aimed at a certain single fiber, and it is impossible to identify textiles containing multiple components, and th

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Textile component identification method based on hyperspectral imaging
  • Textile component identification method based on hyperspectral imaging
  • Textile component identification method based on hyperspectral imaging

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0038] like figure 1 As shown, a method for identifying textile components based on hyperspectral imaging in this embodiment includes the following steps:

[0039] (1) Establish a hyperspectral database of common textile raw materials;

[0040] Collect all kinds of common textile raw materials. The above-mentioned common textile raw materials should include natural fibers such as cotton, wool, silk, and hemp; synthetic fibers such as polyester, nylon, acrylic, and polypropylene; fiber, metal fiber, carbon fiber and other inorganic fibers, the above fibers are evenly wound on the glass slide, and the fiber on the glass slide needs to be kept flat and completely covered during the winding process, so as to ensure the accuracy of its spectral data. The spectral data and images were obtained through the hyperspectral imager VNIR-400E of Themis Vision Company in the United States, and the above data and images were imported into ENVI software to establish a database for subsequent...

Embodiment 2

[0062] (1) Establish a hyperspectral database of common textile raw materials;

[0063] Collect all kinds of common textile raw materials. The above-mentioned common textile raw materials should include natural fibers such as cotton, wool, silk, and hemp; synthetic fibers such as polyester, nylon, acrylic, and polypropylene; fiber, metal fiber, carbon fiber and other inorganic fibers, the above fibers are evenly wound on the glass slide, and the fiber on the glass slide needs to be kept flat and completely covered during the winding process, so as to ensure the accuracy of its spectral data. The spectral data and images were obtained through the hyperspectral imager VNIR-400E of Themis Vision Company in the United States.

[0064] (2) Collect hyperspectral data of textiles to be inspected;

[0065] The spectral data and images of the textile samples to be inspected are also obtained through VNIR-400E, and the test conditions for the spectral collection of the textiles to be i...

Embodiment 3

[0088] (1) Establish a hyperspectral database of common textile raw materials;

[0089] Collect all kinds of common textile raw materials. The above-mentioned common textile raw materials should include natural fibers such as cotton, wool, silk, and hemp; synthetic fibers such as polyester, nylon, acrylic, and polypropylene; fiber, metal fiber, carbon fiber and other inorganic fibers, the above fibers are evenly wound on the glass slide, and the fiber on the glass slide needs to be kept flat and completely covered during the winding process, so as to ensure the accuracy of its spectral data. The spectral data and images were obtained through the hyperspectral imager VNIR-400E of Themis Vision Company in the United States.

[0090] (2) Collect hyperspectral data of textiles to be inspected;

[0091] The spectral data and images of the textile samples to be inspected are also obtained through VNIR-400E, and the test conditions for the spectral collection of the textiles to be i...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a textile component identification method based on hyperspectral imaging. The textile component identification method is characterized by comprising the following steps: 1) setting up a hyperspectral database of common textile raw materials, 2) acquiring the hyperspectral data of a to-be-tested textile, 3) preprocessing the obtained hyperspectral data and images, 4) comparing and matching the hyperspectral data of the to-be-tested textile with the database set up in the step 1), and 5) displaying the distribution of various components in the textile in an image. The textile component identification method based on hyperspectral imaging is used for identifying the raw material components in the textiles rapidly and nondestructively, and quantitatively analyzing the proportions of the components in the textile.

Description

technical field [0001] The invention belongs to a method for identifying textile components, in particular to a method for identifying textile components based on hyperspectral imaging, and belongs to the technical fields of digital image processing and spectral analysis. Background technique [0002] For many years, fiber type identification and content analysis have been research hotspots in the field of textiles. The main methods of fiber type identification include sensory identification method, combustion method, microscope method (high-power optical microscope and scanning electronic fiberscope method), reagent color method, dissolution method, density method, and DNA identification method. Each of these textile component identification methods has advantages that other methods do not have, but each has its own disadvantages. For example, the sensory identification method has the shortcomings of poor accuracy of identification results and is greatly affected by the su...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G01N21/25
Inventor 祝成炎金肖克张红霞詹小芳田伟李艳清
Owner ZHEJIANG SCI-TECH UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap