Oil well pipe sleeve damage image edge detection method based on grey correlation analysis and Zernike moment

A gray correlation analysis and image edge technology, applied in the field of image processing, can solve problems such as noise interference, low edge precision, and failure to achieve better recognition results

Inactive Publication Date: 2014-09-17
XI'AN PETROLEUM UNIVERSITY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Due to the influence of some external factors, the image is easily disturbed by noise with a frequency close to the edge point during the acquisition and transmission process,...

Method used

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  • Oil well pipe sleeve damage image edge detection method based on grey correlation analysis and Zernike moment
  • Oil well pipe sleeve damage image edge detection method based on grey correlation analysis and Zernike moment
  • Oil well pipe sleeve damage image edge detection method based on grey correlation analysis and Zernike moment

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0032] The image processing in this example can be divided into two parts. The first part uses the gray correlation analysis algorithm to detect the pixel-level edge of the casing damage image of the oil well pipe to realize the pixel-level edge location. The second part uses the Zernike moment operator to perform secondary subpixel-level edge positioning on the image in the previous step. Take the original image of M×N pixel size as an example to illustrate the implementation steps of this example:

[0033] (1) Use the gray correlation analysis algorithm to detect the edge of the preprocessed oil well casing damage image, and complete the rough positioning of the target edge;

[0034] 1. Determine the reference sequence and comparison sequence

[0035] For the convenience of calculation, for an image of M×N size, a 3×3 template with a value of 1 is used as a reference sequence, namely: x 0 =(1,1,1,1,1,1,1,1,1), the comparison sequence is composed of each pixel in the image ...

Embodiment 2

[0076] Take the video screenshot of the underground TV software system in a certain well depth as an example to illustrate the implementation steps of this example:

[0077] (1) Use the gray correlation analysis algorithm to detect the edge of the preprocessed oil well casing damage image, and complete the rough positioning of the target edge;

[0078] 1. Determine the reference sequence and comparison sequence

[0079] For the convenience of calculation, the 3×3 template with the value of 1 is used as the reference sequence, namely: x 0 =(1,1,1,1,1,1,1,1,1), the comparison sequence is composed of each pixel in the image and the surrounding 8 neighboring pixels, that is:

[0080] x ij =(x i-1,j-1 ,x i-1,j ,x i-1,j+1 ,x i,j-1 ,x i,j ,x i,j+1 ,x i+1,j-1 ,x i+1,j ,x i+1,j+1 )

[0081] Where i=1,2,...,M; j=1,2,...,N, when i,j=1 or i=M, j=N, repeat the corresponding pixel on the adjacent row or column value as the value of the point. For convenience of description, us...

Embodiment 3

[0122] Take the video screenshot of the underground TV software system in a certain well depth as an example to illustrate the implementation steps of this example:

[0123] (1) Use the gray correlation analysis algorithm to detect the edge of the preprocessed oil well casing damage image, and complete the rough positioning of the target edge;

[0124] 1. Determine the reference sequence and comparison sequence

[0125] For the convenience of calculation, the 3×3 template with the value of 1 is used as the reference sequence, namely: x 0 =(1,1,1,1,1,1,1,1,1), the comparison sequence is composed of each pixel in the image and the surrounding 8 neighboring pixels, that is:

[0126] x ij =(x i-1,j-1 ,x i-1,j ,x i-1,j+1 ,x i,j-1 ,x i,j ,x i,j+1 ,x i+1,j-1 ,x i+1,j ,x i+1,j+1 )

[0127] Where i=1,2,...,M; j=1,2,...,N, when i,j=1 or i=M, j=N, repeat the corresponding pixel on the adjacent row or column value as the value of the point. For convenience of description, us...

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Abstract

The invention provides an oil well pipe sleeve damage image edge detection method based on grey correlation analysis and Zernike moment. For an intercepted oil well pipe sleeve damage image, at first, a grey correlation analysis algorithm is adopted for locating image edges coarsely, and then a Zernike moment operator is utilized for achieving sub-pixel level location of the image edges. By means of a fast edge detecting method combining the grey correlation analysis with the Zernike moment, the continuous effective image edges can be detected through the grey correlation analysis, a large number of image details can be retained by adjusting a threshold value, and the applicability range is wide. Due to Zernike, in the sub-pixel edge detecting process, the number of templates needed by sub-pixel edge detecting can be effectively reduced, orders of a function are reduced, the capacity of resisting disturbance is improved, and the edge locating precision is improved. According to the method, at first, coarse locating is carried out for sleeve damage image edge detection through the grey correlation analysis, then the Zernike moment operator is utilized, and therefore the method has the advantages of being high in locating precision and good in noise immunity, and the sleeve damage image edges can be located accurately to the sub-pixel level.

Description

technical field [0001] The invention relates to the field of image processing, in particular to an edge detection method for casing damage images of oil well pipes based on gray correlation analysis and Zernike moments. Background technique [0002] At present, there are more than 20,000 oil and gas wells with damaged casings in various oil fields in my country, and the number of wells is still increasing by nearly 1,000 every year, which seriously affects the safe production and development benefits of oil fields. The basis for the implementation of casing repair work during oil well casing damage detection is crucial to accurately evaluating the degree of oil well casing damage, reasonably analyzing formation stress, and timely repairing of casing. Traditional classic edge detection operators such as Sobel, Priwitt, Canny, etc. are all pixel-level detection operators, that is, the detection accuracy can reach the pixel level at most. Due to the influence of some external ...

Claims

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Application Information

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IPC IPC(8): G06T7/00
Inventor 爨莹薛继军赵洋
Owner XI'AN PETROLEUM UNIVERSITY
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