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Textile defect detection method based on peak threshold and mixed characteristics

A hybrid feature and defect detection technology, applied in character and pattern recognition, image data processing, instruments, etc., can solve problems such as reducing the automation degree of machine identification of textile defects

Active Publication Date: 2018-04-27
CHANGZHOU UNIV
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Problems solved by technology

Such prior knowledge reduces the degree of automation for machines to identify textile defects to a certain extent.

Method used

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  • Textile defect detection method based on peak threshold and mixed characteristics
  • Textile defect detection method based on peak threshold and mixed characteristics
  • Textile defect detection method based on peak threshold and mixed characteristics

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Embodiment Construction

[0089] The present invention will be described in detail in conjunction with accompanying drawing now. This figure is a simplified schematic diagram only illustrating the basic structure of the present invention in a schematic manner, so it only shows the components relevant to the present invention.

[0090] The implementation of the calculation method of the present invention is completed by writing computer programs, and the self-defined algorithms involved in the specific implementation process are described by pseudocodes. The program input is a grayscale textile image, and the program output is a collection of tiles with defects. The embodiment of the present invention includes five steps, the first three steps are the training phase, and the last two steps are the testing phase.

[0091] The training phase includes the following steps:

[0092] Step 1: Calculate the cartoon component I of the training sample using the morphological component analysis method c , will ...

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Abstract

The invention provides a textile defect detection method based on the peak threshold and mixed characteristics. According to the method, the digital image pixel gray information of the flat textile surface based on an illumination light source is analyzed, an image is divided into non-overlapped grids, IRM, HOG, GLCM and Gabor characteristic values of each grid are calculated, and textile surfacedefect is automatically positioned according to characteristic value distribution. The method is advantaged in that the method is especially suitable for automatically identifying the textile surfacedefect of the gray digital image of the flat textile surface acquired under the stable light source illumination condition.

Description

technical field [0001] The invention relates to the technical field of textile defect detection, in particular to a textile defect detection method based on peak threshold and mixed features. Background technique [0002] The accuracy rate of traditional manual recognition of textile defects is only 60-75% (see literature: K.Srinivasan, P.H.Dastoor, P.Radhakrishnaiah, et al..FDAS: a knowledge-based framework for analysis of defects in woven textiles structures, J.Text .Inst.83(1992)431–448.), the method for automatic machine identification of textile defects has practical application requirements. The digital image sampling of the flat textile surface (hereinafter referred to as the textile image) belongs to the two-dimensional texture, and the two-dimensional texture has been proved to be generated according to the pattern arrangement method defined by 17 wallpaper groups (see literature: H.Y.T.Ngan, G.K.H.Pang, N.H.C. Yung.Motif-based defect detection for patterned fabric...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06T7/11G06K9/62
CPCG06T7/0004G06T7/11G06T2207/20081G06T2207/30124G06F18/214
Inventor 颜榴红庄丽华贾靓
Owner CHANGZHOU UNIV
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