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Fabric Defect Detection Method Based on Local Statistical Features and Global Significance Analysis

A technology of statistical features and local statistics, applied in image analysis, calculation, image data processing and other directions, can solve problems such as poor fabric image detection effect

Inactive Publication Date: 2016-04-27
ZHONGYUAN ENGINEERING COLLEGE
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  • 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
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  • Fabric Defect Detection Method Based on Local Statistical Features and Global Significance Analysis
  • Fabric Defect Detection Method Based on Local Statistical Features and Global Significance Analysis
  • Fabric Defect Detection Method Based on Local Statistical Features and Global Significance Analysis

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Embodiment

[0125] 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 1a-Figure 1f shown. 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 -0.45, and local texture features and overall saliency analysis are used to generate a visual saliency map, such as Figure 2a-Figure 2f As shown, it can be seen from the figure that Figure 2a , Figure 2b and Figure 2e The effect of generating visual saliency map 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 Figure 3a-Figure 3f As shown, it can be seen from the figure that Figure 3...

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
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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
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Application Information

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Patent Type & Authority Patents(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