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Textile defect detection method based on peak coverage value and mixed features

A technology of mixing features and textiles, applied in image data processing, instruments, calculations, etc., can solve problems such as reducing the degree of automation of machine identification of textile defects

Active Publication Date: 2018-05-01
CHANGZHOU UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

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 coverage value and mixed features
  • Textile defect detection method based on peak coverage value and mixed features
  • Textile defect detection method based on peak coverage value and mixed features

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Embodiment Construction

[0105] 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.

[0106] 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.

[0107] The training phase includes the following steps:

[0108] Step 1: Calculate the parameters required for grid segmentation based on a series of flawless images to determine the ideal s...

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Abstract

The invention provides a textile defect detection method based on a peak coverage value and mixed features. According to the method, pixel grayscale information of a digital image of a flat textile surface under an illumination light source is analyzed, the image is segmented into grids which are not mutually overlapped, IRM, HOG, GLCM and Gabor feature values of each grid are calculated, and a textile surface defect is automatically positioned according to the distribution of the feature values. The textile defect detection method is especially applicable to automatically recognizing textilesurface defects in textile flat surface grayscale digital images collected under a stable illumination light source.

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 coverage values ​​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 patterne...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06T7/11G06T7/41
Inventor 贾靓石林庄丽华颜榴红
Owner CHANGZHOU UNIV
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