Fabric defect detection method based on local statistical characteristics and overall significance analysis

A technique of statistical features and local statistics, applied in image analysis, calculation, image data processing, etc., can solve the problems of poor detection effect of fabric images, achieve strong adaptability and robustness, and expand the scope of use

Inactive Publication Date: 2014-04-16
ZHONGYUAN ENGINEERING COLLEGE
View PDF1 Cites 33 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the existing visual saliency models are poor in detecting fabric images, and cannot effectively highlight defects from complex texture backgrounds.

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 local statistical characteristics and overall significance analysis
  • Fabric defect detection method based on local statistical characteristics and overall significance analysis
  • Fabric defect detection method based on local statistical characteristics and overall significance analysis

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0106] In the embodiment, images of common defects in the fabric image library are used for experiments, including yarn leakage, damage, loose weft, and skipping. Knot head, etc., the size of the image is 512×512, select some images such as figure 1 a-

[0107] figure 1 shown in f. In the embodiment, the value of P is 8, the value of R is 3, the value of m is 16, the value of c is 4, and the value of R j The value is 0.34×0.15, K is 20, c is -

[0108] 0.45, using local texture features and overall saliency analysis to generate visual saliency maps, such as figure 2 a-

[0109] figure 2 As shown in f, it can be seen from the figure figure 2 a. figure 2 b and figure 2 The visual saliency map generated by e is poor, and there is a certain gap between the highlighted defect area and the actual defect; the visual saliency map is generated by using grayscale statistical features and overall saliency analysis, such as image 3 a-

[0110] image 3 As shown in f, it ...

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 fabric defect detection method based on local statistical characteristics and overall significance analysis. The fabric defect detection method includes local texture and gray statistical characteristic extraction, visual saliency map generation and visual saliency map segmentation. Firstly, an image is subjected to blocking, and local texture and gray statistical characteristics of image blocks are extracted; then, K other image blocks are randomly selected as for each current image block, the contrast ratio between statistical characteristics of the current image block and statistical characteristics of other image blocks is calculated, and visual saliency maps are generated based on overall significance analysis; finally, the saliency maps are segmented according to the optimal threshold iteration segmentation algorithm to acquire the fabric defect detection result. By means of the method, fabric texture statistical characteristics and gray statistical characteristics are comprehensively taken into consideration, and high detection precision is achieved; training samples are not needed, and the self-adaptability is strong; the calculation speed is high and on-line detection is facilitated.

Description

technical field [0001] The invention relates to a method for detecting a defect in a fabric image, in particular to detecting and locating a defect in a fabric defect image by using local texture and grayscale statistical feature extraction and an overall significance analysis method, and belongs to the field of textile image processing. Background technique [0002] Fabric defect detection is a key link in textile quality control and management. With the rapid development of integrated circuits and image processing technology, machine vision has been more and more widely used in the field of industrial surface inspection. Replacing manual operation with computer vision can not only improve the detection speed and reduce labor costs, but also eliminate the defects of fabrics. The automatic detection system can provide credible reference standards for both parties for the assessment of cloth quality grades, which is beneficial to international trade. Fabric defect detection ...

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
Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06T5/40
Inventor 刘洲峰李春雷朱永胜张爱华赵全军闫磊
Owner ZHONGYUAN ENGINEERING COLLEGE
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