Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Fabric defect detection method based on image decomposition algorithm with sparse representation

A sparse representation and defect detection technology, which is used in image analysis, image enhancement, image data processing, etc.

Inactive Publication Date: 2017-08-22
XI'AN POLYTECHNIC UNIVERSITY
View PDF2 Cites 8 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Most of the existing research algorithms can only detect defects on fabric images with simple texture structures or fabric images with specific textures. Therefore, it is of profound research significance to study how to detect defects on fabrics with complex textures.

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
  • Fabric defect detection method based on image decomposition algorithm with sparse representation
  • Fabric defect detection method based on image decomposition algorithm with sparse representation
  • Fabric defect detection method based on image decomposition algorithm with sparse representation

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0066] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0067] Such as figure 1 As shown, the fabric defect detection method based on the sparse representation image decomposition algorithm includes the following steps:

[0068] Step 1, collecting multiple fabric images;

[0069] Step 2, performing preprocessing of histogram equalization on the fabric image;

[0070] Step 3. Modeling the fabric image processed in step 2 based on sparse representation, and decomposing it into a texture part and a defect part;

[0071] Step 4: Carry out image segmentation on the blemish part by superimposed binarization method, and obtain binarized image detection results.

[0072] In step 1, the various fabric images collected are processed into color images in JPG format with a size of 256×256 pixels and a resolution of 300dpi. Such as figure 2 As shown, the tested printed pattern fabric samples come from th...

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 provides a fabric defect detection method based on an image decomposition algorithm with sparse representation. The method comprises the steps that step 1 a number of fabric images are collected; 2 the preprocessing of histogram equalization is carried out on the fabric images; 3 the fabric images after the step 2 are modeled based on sparse representation and are decomposed into a texture part and a defective part; and 4 a superposition binarization method is used to carry out image segmentation on the defective part to acquire a binarization image detection result. The fabric defect detection method based on the image decomposition algorithm with sparse representation can efficiently decompose and clearly display the defective part contained in the fabric images with complex background textures, and has the advantages of high detection rate and high versatility. The disadvantages of manual detection are reduced. The detection need of a variety of fabrics in an industrial production process is met.

Description

technical field [0001] The invention belongs to the technical field of textile surface image processing methods, and relates to a fabric defect detection method based on a sparse representation image decomposition algorithm. Background technique [0002] Effective detection and control of textile surface defects is one of the key links for modern textile enterprises to control costs and improve product competitiveness. At present, most domestic textile enterprises still use the traditional method of manual cloth inspection to detect surface defects of textiles. Since different inspectors have different standards for defining defects, it is difficult to guarantee the consistency and objectivity of the test results. Moreover, manual detection has low accuracy and low efficiency. In terms of detection accuracy, because people's attention can only be concentrated for 20-30 minutes, and the cloth is in motion during the detection process, inspectors are prone to visual fatigue, ...

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): G06T7/00G06T7/11G06T7/136G06T7/194G06T7/168G01N21/88
CPCG06T7/0008G01N21/8851G01N2021/8887G06T2207/10004G06T2207/20052
Inventor 景军锋刘茁梅李鹏飞张蕾张宏伟
Owner XI'AN POLYTECHNIC UNIVERSITY
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com