Textile defect detection method based on peak value threshold, rotation calibration and mixed characteristic

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: 2018-04-20
CHANGZHOU UNIV
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Problems solved by technology

Such prior knowledge reduces the degree of automation ...

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  • Textile defect detection method based on peak value threshold, rotation calibration and mixed characteristic
  • Textile defect detection method based on peak value threshold, rotation calibration and mixed characteristic
  • Textile defect detection method based on peak value threshold, rotation calibration and mixed characteristic

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[0097] 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.

[0098] 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 six steps, the first three steps are the training phase, and the last three steps are the testing phase.

[0099] Described training phase comprises the following steps: Step 1: use morphological component analysis method to calculate training sample cartoon component I c , will I c Divide acco...

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Abstract

The invention provides a textile defect detection method based on a peak value threshold, rotation calibration and a mixed characteristic. The method is characterized by analyzing digital image pixelgray scale information of a flat textile surface under an illumination light source; segmenting an image into grids which are not overlapped; calculating IRM, HOG, GLCM and Gabor characteristic valuesof each grid; and according to characteristic value distribution, automatically positioning a textile surface defect. The method is especially suitable for automatically identifying the textile surface defect in a textile flat surface gray scale digital image 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 threshold, rotation calibration 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 detectio...

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Application Information

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IPC IPC(8): G06T7/00G06T7/13G06T7/136G06K9/62
CPCG06T7/001G06T7/13G06T7/136G06T2207/30124G06T2207/20061G06F18/23213G06F18/214
Inventor 贾靓王新鹏庄丽华颜榴红
Owner CHANGZHOU UNIV
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