Test paper surface texture defect detection method based on gray scale gradient clustering

Abstract

The invention discloses a test paper surface texture defect detection method based on gray scale gradient clustering, which comprises the following steps: collecting a frame of image of test paper, and carrying out graying and median filtering preprocessing on the image; performing binary segmentation on the image based on a four-point gray scale dynamic threshold, and extracting a test paper areathrough a difference method; carrying out Gama gray scale enhancement on the image and filtering out part of periodic textures by adopting a Gaussian low-pass filter; constructing a single-directionGaussian kernel function to carry out convolution filtering on the image in the vertical direction; calculating the gradient grad _ x of the image in the horizontal direction; dividing the test paperarea into n columns of subareas along the horizontal direction, and calculating the position of a gradient maximum value area of each subarea; carrying out clustering calculation on the position of the gradient maximum value area of each sub-area in the vertical direction, and marking the area with the area clustering number reaching the threshold range as a texture defect area; and judging whether the test paper is qualified according to the marked area. The method has the advantages of high detection speed, high detection precision, good robustness and the like.

Description

technical field [0001] The invention belongs to the technical field of computer vision, in particular to a method for detecting texture defects on the surface of test paper based on gray gradient clustering. Background technique [0002] At present, the automatic detection technology for texture defects on the test paper surface is relatively immature, and the missed detection rate and false detection rate for subtle texture defects are relatively high. Under normal circumstances, the detection of surface texture defects of test paper products is done manually, but the judgment of the surface texture of test paper by naked eyes is subjective, and because of the large batch of test paper, long-term detection will cause visual fatigue of the testing staff , thereby reducing the detection efficiency and accuracy. [0003] With the rapid development of computer technology, the detection efficiency can be greatly improved by applying computer vision detection technology to the i...

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Problems solved by technology

For example: Chinese invention patent CN201711262071.9, announced a fabric defect detection method based on spectral curvature analysis, which has good adaptive and anti-interference capabilities, but due to the need to convert the spatial domain image to the frequency domain and then to the spatial domain, It is difficult to guarantee real-time detection of large images; Chinese invention patent CN201310362813.0 discloses a textile defect detection algorithm based on texture gradient, which has the advantage of quickly and accurately distinguishing textile defects

[0004] However, the above methods are all aimed at the detection of textiles, and have limitations on the defects caused by the subtle texture changes on the surface of the test paper. At present, there is no effective visual detection algorithm that can effectively detect the test paper with texture defects in a periodic texture background.

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