Cloth defect detecting method based on machine vision

A defect detection and machine vision technology, which is applied in the direction of optical test defects/defects, instruments, computer parts, etc., can solve the problems of large amount of calculation and slow detection speed, so as to save labor costs, reduce the number of filters, The effect of reducing the missed detection rate and false detection rate

Inactive Publication Date: 2012-10-03
TIANJIN POLYTECHNIC UNIV
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Problems solved by technology

[0004] In order to overcome the deficiencies of manual detection of cloth defects, and aiming at the shortcomings of the large amount of calculation and slow detection speed of the cloth defect detection algorithm based on the Gabor filter bank, the present invention provides a method with strong versatility, low cost, and can improve detection efficiency and effect. Cloth defect detection method based on machine vision

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  • Cloth defect detecting method based on machine vision
  • Cloth defect detecting method based on machine vision
  • Cloth defect detecting method based on machine vision

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Embodiment Construction

[0025] The general flow chart of the cloth defect detection technology method based on machine vision of the present invention is as follows figure 1 As shown, firstly, the power spectrum analysis is performed on the normal cloth texture image, and the acquired texture feature information is used to set the adaptive Gabor filter bank (see figure 2 ), by filtering the cloth sample image, extracting features and judging the defect information to detect cloth defects.

[0026] see image 3 , Common cloth defects are broken weaves, flour grains, dense roads, oil stains, rough knots, colored threads, skipped yarns, holes, skipped flowers, slubs, double warp, double weft defects, etc. Use the following Figure 4 The entire detection process of the broken weave image is further described in detail.

[0027] 1. Power Spectrum Analysis of Cloth Texture Image

[0028] The image power spectrum is expressed as the distribution of image energy in frequency, and the image energy is de...

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Abstract

The invention belongs to the technical field of image processing and pattern recognition, and relates to a cloth defect detecting method based on machine vision. The cloth defect detecting method includes performing power spectral density analysis for an image of a normal cloth texture and acquiring central frequency F and an azimuthal angle theta of the texture; constructing an SXL adaptive Gabor filter bank; filtering the image of the normal cloth texture to obtain a feature image group, and computing the mean value and the variance of each image of the feature image group; acquiring an image of to-be-detected cloth; filtering the image of the to-be-detected cloth to obtain a feature image group; performing threshold post-processing for the feature image group of the image of the to-be-detected cloth to obtain an absolute feature image group; carrying out normalization processing; fusing images and performing binarization processing for the images to obtain a detected binary image; and removing noise interference to obtain a final detection result. The S represents the number of the selected central frequency, and the L represents the number of the selected azimuthal angle. The cloth defect detecting method has the advantages of high universality and efficiency.

Description

technical field [0001] The invention belongs to the technical field of image processing and pattern recognition, and relates to a cloth defect detection method based on machine vision. Background technique [0002] In the textile industry, the detection of cloth defects is a key factor affecting product quality. In the process of product production, due to the influence of factors such as machine operation errors or machine failures, cloth usually contains various defects. At present, most domestic weaving factories mainly rely on artificial vision to detect cloth defects, which have obvious defects: (1) labor intensity is high, detection speed is slow, and detection efficiency is low; (2) inspectors need to concentrate on work for a long time, the whole Work is unfavorable to the health of workers; (3) The subjective factors of the inspectors have a great influence, and the false detection rate and missed detection rate are high, and generally only 40% to 60% of defects ca...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01N21/88G06K9/60
Inventor 肖志涛吴骏张芳耿磊刘彦北
Owner TIANJIN POLYTECHNIC UNIV
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