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Textile defect detection method based on defect enhancement

A defect detection and textile technology, applied in the directions of optical testing flaws/defects, image data processing, instruments, etc., can solve the problems of complex textile image texture structure, difficult to maintain textile image texture information, etc., to improve the detection accuracy, good quality Texture information, the effect of maintaining texture information

Inactive Publication Date: 2013-04-03
HEFEI UNIV OF TECH
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Problems solved by technology

The texture structure of textile images is complex, and it is difficult for the above two methods to maintain the texture information of textile images

Method used

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  • Textile defect detection method based on defect enhancement
  • Textile defect detection method based on defect enhancement
  • Textile defect detection method based on defect enhancement

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Embodiment Construction

[0028] In this embodiment, digital image processing technology is used to detect the defects of textiles caused by machine failure or yarn problems, and the process is as follows:

[0029] see figure 1 , Image acquisition: use CCD cameras to collect various types of defective parts and non-defective parts of textiles, and obtain defective samples and non-defective samples respectively;

[0030] see figure 1 , figure 2 , defect enhancement is to use the weighted value of neighboring pixels to represent the central pixel to eliminate additive noise; the weight w(i, j) is determined by the similarity between the central pixel i and its neighboring pixel j; The similarity between point i and pixel j depends on the neighborhood feature vector v(N i ) and v(N j ) similarity; the neighborhood pixel refers to the search window S centered on the pixel i i Pixel j within, N i Refers to the neighborhood pixel block with the size of the similarity window centered on the pixel i; th...

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Abstract

The invention discloses a textile defect detection method based on defect enhancement. The textile defect detection method is characterized by comprising the following steps of: acquiring various types of defected parts and non-defected parts of a textile by using a charge coupled device (CCD) camera so as to obtain a defected sample and a non-defected sample respectively, and finishing image acquisition; enhancing the acquired image by using the similarity among local modes, wherein as the similarity of the modes is measured in a main component space, partial noise can be eliminated and the similarity of the modes can be measured more precisely; and texture information of the image of the textile can be kept better; and performing three-layer Haar wavelet decomposition on the enhanced image of the textile, cutting a defect on a wavelet sub band with the maximum defect energy distribution by using a threshold value cutting method, outputting a cutting result and finally finishing defect detection. By the method, the texture information of the image of the textile can be kept better and differences between the defected parts and the non-defected parts can be reflected remarkably; and therefore, the correct rate of detection can be increased.

Description

technical field [0001] The invention relates to a textile defect detection method, especially applied to the detection of defects caused by machine faults and yarn problems by using digital image processing technology. Background technique [0002] Defect detection is a key link in textile industrial production. In the textile industry, there are more than 50 kinds of textile defects, most of which are caused by machine failures and yarn problems. Such defects can be divided into six types: dirty yarn, cobweb, broken warp, doubling, thinning and loose yarn defect. The effect of manual textile defect detection depends heavily on the subjective experience, attention and judgment of the tester. In modern textile industrial production, automatic detection of textile defects is gradually replacing manual detection. [0003] The existing automatic detection technology of textile defects based on digital image processing and pattern recognition is usually divided into two parts:...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01N21/88G06T7/00
Inventor 杨学志田晓梅田兆楠沈仁明刘灿俊曾得生
Owner HEFEI UNIV OF TECH
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