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Method for detecting and classifying fabric defects

A classification method and defect detection technology, applied in the field of image recognition, can solve problems such as inability to classify defects, and achieve the effect of reducing correlation and saving computation.

Inactive Publication Date: 2010-10-20
XIDIAN UNIV
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Problems solved by technology

The above-mentioned detection methods are only for the positioning of the flaws, but not for the classification of the flaws.

Method used

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  • Method for detecting and classifying fabric defects
  • Method for detecting and classifying fabric defects
  • Method for detecting and classifying fabric defects

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Embodiment Construction

[0022] refer to figure 1 , the present invention proposes a method for automatic detection and classification of fabric defects, and its implementation steps include the following:

[0023] Step 1: Use an ordinary industrial camera to obtain grayscale images of fabric defects with a resolution of 256*256.

[0024] The present invention uses industrial cameras to obtain fabric pictures from the fabric inspection production line, selects the flaw pictures, and collects 9 common types of fabric flaws, such as figure 2 , each category includes 80 pictures, all of which are 8-bit grayscale images with a size of 256*256. For each category, 75% of the pictures randomly selected are used as training samples, and all pictures are used for testing.

[0025] Step 2: Normalize the pixel gray values ​​of the fabric gray image so that all pixel values ​​in an image have zero mean and a standard deviation of 1.

[0026] Input the grayscale fabric image I, the grayscale value of each pixe...

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Abstract

The invention discloses a method for detecting and classifying fabric defects and mainly aims to solve the problem of automatic detection and classification of fabric defects. The method comprises the following steps of: firstly, detecting a picture of the fabric defects, filtering the picture by using a Gabor filter group, selecting an optimal filtering result and performing binaryzation on the optimal filtering result by using a reference picture so as to position the positions of the defects in the picture; secondly, extracting a compound characteristic consisting of a Gabor characteristic and a partial binary model characteristic according to the positions of the defects; thirdly, performing pre-treatment on the compound characteristic by main constituent analysis and generalized discriminant analysis algorithm; fourthly, training a neural network classifier by using a pre-treated defect characteristic; and lastly, realizing accurate classification of a fabric defect characteristic by using a trained classifier. The method has the advantages of accurate defect positioning and high classification accuracy and can be used for detecting and classifying the fabric defects in a textile mill.

Description

technical field [0001] The invention belongs to the technical field of image recognition, and in particular relates to a method for detecting image textures, which can be used for recognizing fabric defect pictures collected in the textile inspection link. Background technique [0002] As an effective quality assurance method, fabric defect detection is mainly realized manually, which has a large workload and low detection efficiency. Therefore, it is a reasonable choice to adopt automated machine detection, which can ensure a high detection speed and detection rate. [0003] One of the difficulties in detecting and classifying fabric defects is that there are many types and shapes of defects. According to the standards of my country's textile industry, there are 55 kinds of fabric defects that have been defined. The descriptions of the characteristics of various defects are mostly subjective, and there is no quantitative description of the similarities and differences bet...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06K9/60
Inventor 卢朝阳李静张宇孙华凯崔玲玲李益红屈博
Owner XIDIAN UNIV
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