Texture defect detection method, system and device and storage medium

A defect detection and texture technology, applied in image data processing, instruments, calculations, etc., can solve the problems of misjudgment, texture feature influence, inability to effectively distinguish texture features and defect features, etc., to achieve fast calculation speed and simple defect design , Improving the detection efficiency and quality effect

Active Publication Date: 2019-07-19
嘉兴市敏硕智能科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

When detecting defects on textured surfaces, texture features have a greater impact on defect detection. Current defect detection methods cannot effectively distinguish between texture features and defect features, which can easily lead to misjudgment of defects.

Method used

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  • Texture defect detection method, system and device and storage medium
  • Texture defect detection method, system and device and storage medium
  • Texture defect detection method, system and device and storage medium

Examples

Experimental program
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Effect test

Embodiment 1

[0044] Such as figure 1 As shown, this embodiment provides a texture defect detection method, including the following steps:

[0045] S1. Obtain the image to be detected, and after the image is identified by using a preset two-dimensional function, multiple two-dimensional coordinates and a scalar value of each two-dimensional coordinate are obtained. The scalar value is the gray value of the image.

[0046] S2. Calculate the average length energy of each two-dimensional coordinate and the average image energy of all two-dimensional coordinates by combining the scalar value and the preset method.

[0047] S3. Combine the average length energy and the average image energy to detect the defect features of the image.

[0048] After obtaining the image to be detected, the image is identified by a preset two-dimensional function, that is, the image is divided into two dimensions, so that multiple two-dimensional coordinates and the scalar value of each two-dimensional coordinate are obtain...

specific Embodiment

[0064] The following combination Figure 2 to Figure 5 An implementation of the above method will be described in detail.

[0065] step one:

[0066] Such as Figure 5 As shown, after obtaining a gray image to be processed, it is marked with a two-dimensional function shaped like f (x, y), where (x, y) represents space coordinates, and the value or amplitude of f is a positive Scalar, representing the gray value at the current coordinate.

[0067] Step two:

[0068] Get the direction length neighborhood of each two-dimensional coordinate, refer to figure 2 The length neighborhood is defined as: the coordinate point (x, y) is a neighborhood where the length of the x-coordinate axis or the y-coordinate axis is b, and the b is an integer greater than 0. After obtaining the direction length neighborhood, calculate the direction length energy, refer to image 3 , The direction length energy is defined as: for the coordinate point (x, y), the sum of squares of f(x, y) in the direction le...

Embodiment 2

[0082] Reference Image 6 , This embodiment provides a texture defect detection system, including:

[0083] The identification module is used to obtain the image to be detected, and after the image is identified by the preset two-dimensional function, multiple two-dimensional coordinates and the scalar value of each two-dimensional coordinate are obtained;

[0084] The calculation module is used to calculate the average length energy of each two-dimensional coordinate and the average image energy of all two-dimensional coordinates by combining the scalar value and the preset method;

[0085] The detection module is used to detect the defect features of the image by combining the average length energy and the average image energy.

[0086] Further as a preferred embodiment, the scalar value is the gray value of the image.

[0087] The texture defect detection system of this embodiment can execute the texture defect detection method provided by the method embodiment of the present inventi...

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Abstract

The invention discloses a texture defect detection method, system and device and a storage medium, and the method comprises the following steps: obtaining a to-be-detected image, employing a preset two-dimensional function to identify the image, and obtaining a plurality of two-dimensional coordinates and scalar values of the two-dimensional coordinates; calculating average length energy of each two-dimensional coordinate and average image energy of all two-dimensional coordinates by combining the scalar value and a preset mode; and detecting defect characteristics of the image in combinationwith the average length energy and the average image energy. According to the method, the average image energy and the average length energy are combined to remove the sub-texture features, and the lower defect features are reserved, so that the texture features and the lower defect features can be effectively distinguished, and the detection efficiency and quality are greatly improved; in addition, the detection method is simple and convenient in defect design, high in operation speed, suitable for detection of most texture defects and capable of being widely applied to the technical field ofdefect detection.

Description

Technical field [0001] The invention relates to the technical field of defect detection, in particular to a texture defect detection method, system, device and storage medium. Background technique [0002] The study of texture features has always been a hot topic in the field of image processing. When detecting defects on a textured surface, texture features have a greater impact on defect detection. Current defect detection methods cannot effectively distinguish between texture features and defect features, which may easily lead to misjudgment of defects. Summary of the invention [0003] In order to solve the above technical problems, the purpose of the present invention is to provide a defect detection method, system, device and storage medium that can effectively distinguish between texture features and defect features. [0004] The first technical solution adopted by the present invention is: [0005] A method for detecting texture defects includes the following steps: [0006] ...

Claims

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Application Information

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Patent Type & AuthorityApplications(China)
IPC IPC(8): G06T7/00G06T7/41
CPCG06T7/0002G06T7/41
Inventor郑爽陈和平李耀楠
Owner嘉兴市敏硕智能科技有限公司