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Fabric flaw detection method

A defect detection and fabric technology, which is applied in the direction of optical detection of defects/defects, can solve problems such as false detection or missed detection, insufficient use of feature space, inaccurate positioning, etc.

Active Publication Date: 2016-01-27
南通大学技术转移中心有限公司
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Problems solved by technology

[0004] The problem to be solved by the present invention is: the existing method of fabric defect detection through machine vision is susceptible to noise interference, often resulting in false detection or missed detection, and inaccurate positioning; the existing defect detection method based on GLCM features does not utilize the feature space. Adequate, parameter setting can not achieve optimal and other shortcomings; further, the existing threshold for segmentation defects is often set artificially, which affects the degree of algorithm automation

Method used

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specific Embodiment approach

[0035] The method of the invention mainly includes three steps of obtaining nonlinear mapping functions, learning optimal parameters and detecting defects. The specific implementation is as follows:

[0036] 1. The nonlinear mapping function calculation stage

[0037] For fabric images, the gray levels are often concentrated in a certain interval, rather than uniformly distributed in the entire gray space. If the general GLCM feature extraction method is used, linear quantization will be performed, and a large number of quantization values ​​are not or rarely used, resulting in The generated GLCM matrix is ​​very sparse and cannot fully reflect the texture features of the image. The present invention proposes nonlinear GLCM feature extraction. The basic idea is to quantify according to the occurrence probability of gray levels, refine the high-probability gray-scale intervals, and coarsely quantize the low-probability gray-scale intervals, so that image features can be effect...

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Abstract

The invention relates to a fabric flaw detection method. Firstly, a nonlinear gray scale co-occurrence matrix characteristic is constructed in order to utilize characteristic space fully and extract image characteristics effectively, then an optimal scale direction parameter and a self-adaptation flaw segmentation threshold of nonlinear gray scale co-occurrence matrix characteristic extraction are obtained through learning of a flawless fabric image, finally, the obtained parameter is employed to characteristics of an image to be detected, and a flaw area is positioned through characteristic similarity distance measurement. The method can position a fabric flaw area effectively, and noise interference is small.

Description

technical field [0001] The invention relates to the technical field of machine vision and video image processing, in particular to a fabric defect detection method. Background technique [0002] The method of detecting fabric defects through artificial vision has many problems such as heavy workload, high missed detection rate, high false detection rate, and being affected by subjective feelings. Automatic detection of fabric defects based on machine vision can effectively solve this problem. question. In order to distinguish the flawed and non-flawed regions, it is a key issue to adopt suitable feature descriptions for fabric images. [0003] The method of extracting fabric image features from the frequency domain, such as wavelet transform, Fourier transform, Gabor filter, etc., first decomposes the image into each defined frequency domain sub-band, and distinguishes the defective area through the difference of sub-band coefficients. Methods for extracting features from ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01N21/88
Inventor 董蓉李勃
Owner 南通大学技术转移中心有限公司
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