Fabric defect detection method based on B-spline wavelets and deep neural network

A spline wavelet and defect technology, applied in biological neural network models, image data processing, instruments, etc., can solve problems such as manual intervention of different background patterns, slow calculation speed, etc.

Active Publication Date: 2015-07-01
南通乐亿达纺织科技有限公司
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AI Technical Summary

Problems solved by technology

[0004] The present invention overcomes the above-mentioned shortcomings of the prior art, and proposes a fabric defect detection method based on B-spline wavelet and deep neural network, which solves the shortcomings of slow calculation speed and manual intervention for different background patterns in the prior art

Method used

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  • Fabric defect detection method based on B-spline wavelets and deep neural network
  • Fabric defect detection method based on B-spline wavelets and deep neural network
  • Fabric defect detection method based on B-spline wavelets and deep neural network

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Embodiment Construction

[0064] An automatic detection method for fabric defects includes two steps: a model training stage and a detection stage.

[0065] Step 1, the model training stage has the following implementation steps:

[0066] 11. Expand the length and width pixels of the image in the sample library to 2 n The square, the extended part is filled with 0;

[0067] 12. Perform multiple B-spline wavelet transforms on the image, the specific implementation is as follows:

[0068] 12.1 Carry out B-spline wavelet transform on the image to obtain four images of diagonal direction sub-image HH, vertical direction sub-image HL, horizontal direction sub-image LH and low-frequency sub-image LL. The fast wavelet transform algorithm is shown in formula (1):

[0069] a n , m j + ...

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Abstract

A fabric defect detection method based on B-spline wavelets and deep neural network includes deep neural network model training and defect image detecting. The deep neural network model training is characterized in that by learning existing sample libraries, the defect-free reconstructed images of samples under the premise that defect images are input. The defect image detecting showed in the picture is characterized in that to-be-detected fabric images are subjected to multiple times of wavelet transformation to obtain compressed images with most of texture information reserved, and the compressed images are saved; the compressed images are input into a trained deep neural network input end, calculation is performed, and the defect-free reconstructed images are obtained at an output end; margin calculation is performed on the reconstructed images and the saved compressed images to obtain the images which only contain defects; the features of the defect images are extracted to analyze whether the fabric contains defects or not, the categories of the defects, and the like.

Description

technical field [0001] The invention relates to the field of automatic measurement and control, in particular to a method for detecting fabric defects in the production process. Background technique [0002] The traditional fabric defect detection is that inspectors evaluate the fabric grade according to personal experience and fabric rating standards. This method has many problems such as low detection speed, high missed detection rate, and subjective influence of detection results, which cannot meet the needs of rapid inspection. , High-quality product production. Therefore, it is an urgent need for textile or fabric printing enterprises to develop a fast and accurate automatic detection method for fabric defects. The automatic detection of fabric defects is the key link to control the quality of fabrics and realize the automation and unmanned process of weaving and fabric inspection. Especially with the development of image processing and artificial intelligence technol...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06N3/02
Inventor 王宪保王辛刚陈德富顾勤龙何文秀姚明海
Owner 南通乐亿达纺织科技有限公司
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