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Textile defect detecting algorithm based on texture gradients

A texture gradient and defect detection technology, which is applied in computing, image data processing, computer parts, etc., can solve the problems of inaccurate detection of textile defects, heavy labor, and high cost, so as to enhance export competitiveness and application Broad, quality-enhancing effects

Inactive Publication Date: 2013-11-27
HEFEI NORMAL UNIV
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Problems solved by technology

Manual inspection methods are expensive and labor-intensive. More importantly, this manual inspection method cannot accurately detect textile defects.
According to statistics, only 50-70% of defects can be judged by artificial vision, while the efficiency of manual detection of textile defects is less than 80%.

Method used

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  • Textile defect detecting algorithm based on texture gradients
  • Textile defect detecting algorithm based on texture gradients
  • Textile defect detecting algorithm based on texture gradients

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Embodiment Construction

[0025] See attached figure 1 , a textile defect detection algorithm based on texture gradients, which includes the following steps:

[0026] Step 1: Use non-local mean filtering to enhance the texture of the texture image, eliminate noise and irrelevant details, enhance the expressiveness of texture details, and restore the original image structure;

[0027] Step 2: Process the texture-enhanced textile image, and calculate the texture gradient image of the textile image;

[0028] Step 3: Construct the feature model of the texture gradient image;

[0029] Step 4: Use the texture watershed calculation method to segment the texture gradient image, obtain the initial segmentation of the image, and obtain a uniform region with the same characteristics, and then construct a RAG (RAG: region adjacency graph, region adjacency graph) to represent the MLL relationship model ( multi-level logistic model, each region in the multi-level logic model), and use the MLL relationship model to...

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Abstract

The invention discloses a textile defect detecting algorithm based on texture gradients. Firstly, texture gradients of sub-band characteristics after wavelet transformation are calculated, marked watershed segmentation is carried out on a texture gradient image, and a textile defect detecting system based on the texture gradients is primarily finished; secondly, non-local average filtering is utilized to eliminate imaging noise in a textile image and influences of other uncorrelated details, useful texture details are highlighted, preprocessing is completed, and texture enhancement is achieved; at last, on the bases of the texture enhancement and the texture gradients, an MRF model is utilized to extract a defect zone boundary, texture defects can also be well extracted by means of the method, and the defect that in watershed marking, a threshold value needs to be chosen manually is eliminated. The textile defect detecting algorithm based on the texture gradients has the advantages of being capable of rapidly and accurately distinguishing defects of textiles and wide in application, improving the quality of the textiles, and the like.

Description

technical field [0001] The invention relates to a textile defect detection algorithm based on texture gradient. Background technique [0002] Textile quality control occupies an important position in textile industrial production. Although the advantages of modern textile machinery have greatly reduced the probability of textile defects, the manufacturing process is still not 100% defect-free. Various types of textile defects significantly affect the appearance of textiles and, to a certain extent, the sales of textiles. According to statistics, 45% to 65% of the price cuts of textiles are caused by textile defects. Therefore, textile defect detection is a necessary and important link in the textile industry. [0003] At present, textile defect detection mainly relies on manual methods, so the quality of detection depends heavily on subjective experience, judgment and attention. Manual detection methods are expensive and labor-intensive. More importantly, this manual det...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06T5/10G06K9/34
Inventor 沈晶陈明生张量况晓静时晶晶陈孝培叶铭王昊王丁伟
Owner HEFEI NORMAL UNIV
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