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Strip steel surface rapid quality discrimination and defect feature automatic extraction method

A quality screening and automatic extraction technology, applied in the field of computer vision, can solve problems such as difficult to effectively extract real defects, false edges, light interference, etc., to meet the requirements of real-time online defect detection, high accuracy, and low hardware performance requirements Effect

Active Publication Date: 2020-04-24
SHANGHAI UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

Aiming at the problems in the existing technology that the defect area ratio of the strip steel is low, the false defects at the edge and the light interference are serious, making it difficult to effectively extract various real defects, this application is based on the "difference method" and according to the surface defects of the strip steel Based on the image characteristics, a method for rapid quality identification and defect feature automatic extraction of strip steel surface was studied

Method used

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  • Strip steel surface rapid quality discrimination and defect feature automatic extraction method
  • Strip steel surface rapid quality discrimination and defect feature automatic extraction method
  • Strip steel surface rapid quality discrimination and defect feature automatic extraction method

Examples

Experimental program
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Effect test

Embodiment 1

[0041] A method for rapid quality screening and defect feature automatic extraction of strip steel surface, comprising the following steps:

[0042] (1) Gray-scale projection is performed on the collected strip steel surface image in a column-down and row-to-right manner to obtain a gray-scale matrix image;

[0043] (2) Analyze the grayscale matrix image obtained in step (1) to find the maximum value R of the grayscale projection value of each row in the grayscale matrix image Max , minimum R Min and the maximum value C of each column of grayscale projection values Max , minimum value C Min , calculate the average value R of each row of grayscale projection Avg , each column of gray projection mean C Avg And the global gray mean of the entire gray matrix image Global Avg , and then judge the defect area, non-defect area, boundary pseudo-defect area, transition area between the strip boundary and the background in the strip surface image according to the mean value of the ...

Embodiment 2

[0058] Example 2: Validation experiment of the present invention's strip steel surface rapid quality screening and defect feature automatic extraction method

[0059] 1. Detect the strip steel image with longitudinal inclusion defects on the surface

[0060] Adopt the method for the embodiment of the present invention 1 to detect the strip steel image that the surface has longitudinal grain defect, wherein the strip steel surface original image that gathers is as follows figure 1 As shown in A, by figure 1 In A, it can be seen that there are longitudinal grain defects in the strip steel image to be detected, the background area of ​​the strip steel shooting, and the transition area between the strip steel boundary and the background. Test results such as figure 1 As shown in B (B is a partial image of the detection result), the defect area in Figure B has been framed by a box, that is, the area framed by the box is the detected defect characteristic ROI area, and Figure B ha...

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Abstract

The invention relates to the technical field of computer vision, and discloses a strip steel surface rapid quality discrimination and defect feature automatic extraction method, which comprises the following steps: 1) performing gray projection on an acquired strip steel surface image to obtain a gray matrix graph; 2) finding out a maximum value RMax and a minimum value RMin of each row of gray projection values and a maximum value CMax and a minimum value CMin of each column of gray projection values in the gray matrix graph; calculating a gray projection mean value RAvg of each row and a gray projection mean value CAvg of each column, and judging whether the strip steel surface image has defects or not according to the mean values of the gray projection values of each row and each columnand the difference between the maximum value and the minimum value; and 3) cutting the gray matrix graph according to the judgment result in the step 2), and marking a defect characteristic ROI areain the cut gray matrix graph. The method can quickly discriminate the defects in the strip steel surface image, is high in calculation speed, and meets the real-time online defect detection requirements of a high-speed strip steel production line.

Description

technical field [0001] The invention belongs to the technical field of computer vision, and in particular relates to a method for rapid quality identification and defect feature automatic extraction of strip steel surfaces. Background technique [0002] Strip steel is one of the main products of the steel industry and an indispensable raw material for aerospace, shipbuilding, automobile, machinery manufacturing and other industries. Its quality will directly affect the quality and performance of the final product. In the process of strip steel manufacturing, due to various factors such as raw materials, rolling equipment and processing technology, different types of defects such as cracks, scars, and holes appear on the surface of the strip steel. Strip surface defects not only easily lead to serious production accidents such as strip breakage, stacking, and parking, but also seriously wear the rolls, causing incalculable economic and social impacts on production enterprises...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06K9/32G06K9/46G01N21/88
CPCG06T7/0008G01N21/8851G01N2021/8829G06T2207/10004G06T2207/30136G06V10/25G06V10/462Y02P90/30
Inventor 万翔刘丽兰封博文张祥玉
Owner SHANGHAI UNIV
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