Integral graph algorithm-based fabric flaw detection method

Abstract

An integral graph algorithm-based fabric flaw detection method is disclosed. An integral graph algorithm is used for rapidly extracting statistical characteristics of gradient energy, and the statistical characteristics of gradient energy are used for flaw detection; image learning operation on a flawless template is performed, statistics are run on characteristic distribution of the gradient energy, distribution peak values are extracted, threshold parameters are calculated in a self-adaptive manner and used for distinguishing subsequent flaws, gradient energy of a window where each pixel point of an image to be detected is positioned can be calculated via the integral graph algorithm, whether a current pixel point is a defect point can be determined based on the threshold parameters, and whether the current image is a flawed fabric can be determined after statistics are run on a total quantity of flaw points of the whole image. According to the method disclosed in the invention, based on principles of accelerated operation of integral graphs, the characteristic distribution of the gradient energy of the fabric image can be rapidly extracted, real time detection of fabric flaws can be realized, the peak values of the distribution are calculated, the self-adaptive threshold parameters for flaw determination are obtained, and accurate segmentation of fabric flaws can be realized. Via the method, real-time property and high accuracy can be ensured.

Description

technical field [0001] The invention relates to the technical field of machine vision and video image processing, in particular to a fast fabric defect detection method based on an integral graph algorithm. Background technique [0002] The defect detection in the traditional textile industry is mostly based on manual naked eye detection. However, human vision is prone to fatigue, which leads to missed detection, low efficiency of manual observation, and high labor costs. This is extremely inconsistent with large-scale industrial production. Using computer vision and image processing The algorithm automatically detects fabric defects, which can effectively solve this problem. [0003] The method based on image filtering extracts fabric texture features in the frequency domain for flaw detection, such as Gabor filtering and wavelet transform. Since the scale and direction of flaws are not determined, it is often necessary to extract the results of multiple scales and multiple...

AI Technical Summary

Problems solved by technology

[0005] The problem to be solved by the present invention is: the existing fabric defect detection system relies on human eye observation, which is inefficient; the existing method of fabric defect detection with high accuracy through various complex algorithms has a large amount of computation, which does not meet the real-time requirements of industrial production. The existing methods for quickly detecting fabric defects can only deal with simple fabric images, and the effect on complex textured fabrics is poor

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