CN115272337B - Anomaly detection method and system for interior of pipeline - Google Patents
Anomaly detection method and system for interior of pipeline Download PDFInfo
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Abstract
The invention relates to the technical field of data processing, in particular to an abnormality detection method and system for the interior of a pipeline. The method comprises the following steps: acquiring a gray level image inside the pipeline and calculating a projection value of each row in the gray level image so as to obtain a first characteristic point and a second characteristic point; constructing a kernel window, sliding the kernel window on the gray image to update the gray image to obtain an updated image, and obtaining a third characteristic point and a fourth characteristic point based on the updated image; the method comprises the steps of obtaining a characteristic value sequence corresponding to each pixel point based on each characteristic point, further constructing a style matrix based on the characteristic value sequences of all the pixel points, comparing the style matrix with different style matrixes of a root nondestructive image, a corrosion image and a notch image to obtain style difference, judging whether a gray image is abnormal or not according to the style difference, improving the efficiency of detecting the internal abnormality of the pipeline and ensuring the accuracy of detection.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to an abnormality detection method and system for the interior of a pipeline.
Background
The corrosion problem is an inevitable problem in all industries using metals, and the metals have different corrosiveness under different environments, and although the corrosion resistance of the corrosion-resistant materials is stronger and stronger nowadays, the corrosion of the metals is delayed and not completely eradicated.
The corrosion speed of metal in the metal pipeline is related to the external environment, the internal conveying medium, the pressure and the like, pipeline corrosion can cause pipeline failure, the pipeline leaks, when pipeline corrosion detection finds out untimely and inaccurate, economic loss can be caused, and more importantly, personnel safety problems and environmental problems can be caused.
Disclosure of Invention
In order to solve the above technical problems, an object of the present invention is to provide a method and a system for detecting an abnormality in a pipeline, wherein the technical scheme adopted is as follows:
in a first aspect, an embodiment of the present invention provides an abnormality detection method for an inside of a pipeline, including the steps of:
acquiring a pipeline image inside a pipeline, and graying the pipeline image to obtain a grayscale image;
acquiring a projection value of each row in the gray level image, and fitting the projection values of all the rows to a travel curve; carrying out difference on any two adjacent points in the row curve to obtain a corresponding difference value sequence, and calculating the average value of all elements in the difference value sequence to be used as a threshold value; obtaining a first characteristic point and a second characteristic point based on the threshold;
constructing a kernel window, sliding the kernel window on the gray image to update the gray image to obtain an updated image, and obtaining a third characteristic point and a fourth characteristic point based on the updated image;
calculating a characteristic value sequence of each pixel point in the gray level image based on the first characteristic point, the second characteristic point, the third characteristic point and the fourth characteristic point, and obtaining a style matrix according to the characteristic value sequence of each pixel point in the gray level image;
acquiring a lossless style matrix corresponding to a lossless image, a corrosive style matrix corresponding to a corrosive image and a notch style matrix corresponding to a notch image, calculating style differences between the style matrix of the gray-scale image and the lossless style matrix, the corrosive style matrix and the notch style matrix respectively, and judging whether the gray-scale image is abnormal or not according to the style differences.
Preferably, the step of obtaining the projection value of each line in the grayscale image includes:
and calculating the summation result of the gray values corresponding to all the pixel points in each row in the gray image as the projection value of the corresponding row.
Preferably, the step of obtaining the first feature point and the second feature point based on the threshold value includes:
selecting corresponding rows with projection values larger than the threshold value in the gray level image, and acquiring a first row and a last row in all the selected rows; the pixel point with the maximum gray value in the first line is a first characteristic point; and the pixel point with the maximum gray value in the last line is a second feature point.
Preferably, the step of sliding the kernel window on the grayscale image to update the grayscale image to obtain an updated image includes:
acquiring a kernel window corresponding to a pixel point to be processed, calculating the gray average value of all pixel points in the kernel window, and replacing the pixel value of the pixel point to be processed with the gray average value.
Preferably, the step of obtaining a third feature point and a fourth feature point based on the updated image includes:
and acquiring the average value of all pixel points in the updated image as an image threshold, selecting all pixel points which are larger than the image threshold in the updated image, and selecting pixel points with the largest gray value in two lines with the smallest line number and the largest line number from the selected pixel points as a third characteristic point and a fourth characteristic point.
Preferably, the step of calculating a feature value sequence of each pixel point in the grayscale image based on the first feature point, the second feature point, the third feature point, and the fourth feature point includes:
the eigenvalues are calculated as:
wherein the content of the first and second substances,
representing the characteristic value corresponding to the pixel point;
expressing the gray value of the pixel point;
representing the gray value of the v-th characteristic point;
representing the distance between the pixel point and the v-th characteristic point;
and obtaining characteristic values corresponding to the pixel points based on the first characteristic points, the second characteristic points, the third characteristic points and the fourth characteristic points to form a characteristic value sequence.
Preferably, the method for acquiring the style difference includes:
and calculating the difference values among the corresponding elements in the style matrix, and acquiring the square sum corresponding to all the difference values, wherein the smaller the square sum is, the larger the style difference is.
In a second aspect, another embodiment of the present invention provides an anomaly detection system for the interior of a pipeline, the system comprising: a memory, a processor and a computer program stored in said memory and executable on said processor, said processor implementing the steps of a method for anomaly detection inside a pipeline as described above when executing said computer program.
The invention has the following beneficial effects: the method comprises the steps of analyzing a gray image in a pipeline, and summing pixel values of pixel points in each line in the gray image to obtain a first characteristic point and a second characteristic point; further, a kernel window is constructed, the pixel value of each pixel point in the gray level image is updated by the kernel window to obtain an updated image, and a third feature point and a fourth feature point are obtained based on the updated image; calculating the characteristic value of each pixel point in the gray-scale image based on the four characteristic points and constructing a style matrix, and judging whether the gray-scale image is abnormal or not based on the style matrix, so that the reliability of data in the detection process is improved, and the accuracy of the detection result is ensured.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions and advantages of the prior art, the drawings used in the embodiments or the description of the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flow chart of an anomaly detection method for the interior of a pipeline according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a lossless internal grayscale image of a pipeline according to an embodiment of the present invention.
Detailed Description
To further illustrate the technical means and effects of the present invention adopted to achieve the predetermined objects, the following detailed description will be given to a method and a system for detecting an abnormality in a pipeline according to the present invention, with reference to the accompanying drawings and preferred embodiments, and the detailed implementation, structure, features and effects thereof are described below. In the following description, different "one embodiment" or "another embodiment" refers to not necessarily the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The following describes a specific scheme of the method and system for detecting an abnormality in a pipeline according to the present invention in detail with reference to the accompanying drawings.
Referring to fig. 1, a flow chart of an anomaly detection method for an interior of a pipeline according to an embodiment of the present invention is shown, the method including the following steps:
and step S100, acquiring a pipeline image in the pipeline, and graying the pipeline image to obtain a gray image.
Because the interior of the pipeline cannot be opened during detection, nondestructive detection equipment is used for acquiring images in the pipeline, the nondestructive detection equipment is provided with instruments such as an ultrasonic detector, the acquired images are acquired by nondestructive detection by utilizing the characteristic of sound wave penetrability, and the penetrability of a pipeline region and a corrosion region to sound waves is different, so that the pipeline images in the pipeline are acquired by the nondestructive detection equipment.
Further, for convenience of subsequent analysis, graying the image by adopting a weighted average method to obtain a grayscale image corresponding to the pipeline image.
Step S200, acquiring a projection value of each row in the gray level image, and fitting the projection values of all the rows to a travel curve; carrying out difference on any two adjacent points in the line curve to obtain a corresponding difference value sequence, and calculating the average value of all elements in the difference value sequence as a threshold value; and obtaining a first characteristic point and a second characteristic point based on the threshold value.
In the embodiment of the invention, whether the interior of the pipeline is abnormal is identified by using a template matching method, which is specifically shown in the steps that a gray matrix with the same size is constructed for a lossless image, a corroded image, a notch image and a gray image to be detected by using the same method respectively, and the abnormality in the interior of the pipeline is judged according to the similarity degree of the gray matrix. To construct a gray matrix by using an image method, the features extracted by constructing the matrix must be able to reflect the corrosion condition inside the pipeline and be distinguished from the features of the non-corrosion image.
A white coating is generally formed in the middle of a gray-scale image of the interior of a pipeline, where the white coating in the acquired image is a straight line segment with a width when the interior of the pipeline is not damaged, see fig. 2, which shows a schematic diagram of a gray-scale image of the interior of a lossless pipeline; when corrosion or gaps occur in the pipeline, the corrosion or gaps correspond to different defects respectively, the white coating area of the corrosion defect presents a broken arc shape, the gaps present the ghost of the white coating, the middle part becomes thicker, and a Graham matrix is constructed based on the characteristics of different defects.
Solving the gray matrix can directly calculate any two pixel points to obtain the relationship between the two points, and further form a sequence after obtaining the relationship of the whole image to obtain the style matrix of the image, namely the gray matrix; however, the processing result is slower due to the method for calculating all the pixel points of the whole graph, so that the style matrix is constructed by selecting the characteristic points in the embodiment of the invention.
Firstly, summing the gray values of all pixel points of each line in the gray image, then averaging to obtain the projection value corresponding to each line, obtaining the projection values corresponding to all the lines in the gray image, fitting to obtain a curve, wherein the abscissa of the curve is different lines, and the ordinate is the projection value corresponding to each line.
Then, calculating the difference between any two adjacent points in the curve, and obtaining the difference between all the two adjacent points in the curve to obtain a difference sequence; then, calculating an average value of all elements in the difference value sequence, and recording the average value as a threshold, namely obtaining the threshold as:
wherein the content of the first and second substances,
is shown as
The projection values corresponding to the rows;
is shown as
The projection values corresponding to the rows;
representing the number of all rows in the grayscale image;
representing a threshold value.
Based on the threshold value and the projection value of each line in the gray image, the line number with larger change in the curve is obtained, the part with larger change can be considered as the line number from the black area to the white coating, the line number with the change larger than the threshold value at the earliest and the line number with the change larger than the threshold value at the latest are obtained, and the pixel point with the largest gray value in the two lines is taken as the characteristic point, so that the first characteristic point is obtained
And a second characteristic point
。
And step S300, constructing a kernel window, sliding the kernel window on the gray image to update the gray image to obtain an updated image, and obtaining a third characteristic point and a fourth characteristic point based on the updated image.
Because the gray level image of the interior of the pipeline shot in the corrosion state has a relatively obvious radian, the higher the corrosion degree is, the larger the radian is, and the radian is certainly the middle arch, and the two sides sink; therefore, in the embodiment of the present invention, a triangular kernel window is set, where the kernel window has three rows of pixel points, the first row is a pixel point, the position of the pixel point to be calculated is also the position of the pixel point, the second row has three pixel points, and the third row has five pixel points.
Processing all pixel points in the gray-scale image by using the kernel window, calculating the mean value of the pixel points in the kernel window region corresponding to each pixel point, replacing the positions of the pixel points by the mean value to update the pixel points, and obtaining corresponding updated images after all the pixel points in the gray-scale image are updated by using the kernel window;
calculating the average value of all pixel points in the updated image as an image threshold, selecting all pixel points larger than the image threshold, and selecting the pixel point with the largest gray value in two lines with the smallest line number and the largest line number from the pixel points as a third characteristic point
And a fourth characteristic point
。
Step S400, calculating a characteristic value sequence of each pixel point in the gray level image based on the first characteristic point, the second characteristic point, the third characteristic point and the fourth characteristic point, and obtaining a style matrix according to the characteristic value sequence of each pixel point in the gray level image.
The first feature point, the second feature point, the third feature point and the fourth feature point are obtained in steps S200 and S300, and a feature value corresponding to each pixel point in the grayscale image is calculated based on the four feature points, where the feature values specifically are:
wherein the content of the first and second substances,
expressing the gray value of the pixel point;
representing the gray value of the v-th characteristic point;
representing the distance of the pixel point from the v-th feature point.
By analogy, four characteristic values corresponding to each pixel point are obtained by utilizing each characteristic point for calculation, and the four characteristic values are sequentially arranged to obtain a characteristic sequence corresponding to each pixel point as
(ii) a The characteristic sequence represents the relationship between any one pixel point and four characteristic points, so that a style matrix corresponding to the image can be obtained:
the style matrix can represent the style value corresponding to the gray level image, the style value is obtained according to different characteristics, and if corrosion or defect images occur, the obtained matrix can be better distinguished from a normal matrix; in a matrix
Representing the dot product of two signature sequences.
And S500, acquiring a lossless style matrix corresponding to the lossless image, a corrosive style matrix corresponding to the corrosive image and a notch style matrix corresponding to the notch image, calculating the style difference between the style matrix of the gray-scale image and the lossless style matrix, the corrosive style matrix and the notch style matrix respectively, and judging whether the gray-scale image is abnormal or not according to the style difference.
Based on the method for obtaining the same gray level image style matrix in step S400, a lossless style matrix corresponding to the lossless image, a erosion style matrix corresponding to the erosion image, and a notch style matrix corresponding to the notch image are obtained and recorded as
，
，
. And respectively performing style difference calculation between the style matrix corresponding to the gray level image and the three style matrices, wherein the style difference calculation method is that elements between the style matrices are correspondingly subtracted, then squares are taken, and then the squares are summed, and the smaller the square sum is, the more similar the squares are.
The style difference between the style matrix corresponding to the gray-scale image and the other three style matrices is obtained, and the style of the two images is closer as the style difference value is smaller, so that the images are more likely to be in the same class. Therefore, after all the style differences are calculated, the image category corresponding to the style matrix when the style differences are minimum is selected, whether the abnormality occurs is judged according to the image category, and the specific abnormality can be judged.
In summary, in the embodiment of the present invention, a pipeline image inside a pipeline is obtained, and the pipeline image is grayed to obtain a grayscale image; acquiring a projection value of each row in the gray level image, and fitting the projection values of all the rows to a travel curve; carrying out difference on any two adjacent points in the row curve to obtain a corresponding difference value sequence, and calculating the average value of all elements in the difference value sequence to be used as a threshold value; obtaining a first characteristic point and a second characteristic point based on a threshold value; constructing a kernel window, sliding the kernel window on the gray image to update the gray image to obtain an updated image, and obtaining a third characteristic point and a fourth characteristic point based on the updated image; calculating a characteristic value sequence of each pixel point in the gray level image based on the first characteristic point, the second characteristic point, the third characteristic point and the fourth characteristic point, and obtaining a style matrix according to the characteristic value sequence of each pixel point in the gray level image; the method comprises the steps of obtaining a lossless style matrix corresponding to a lossless image, a corrosion style matrix corresponding to a corrosion image and a notch style matrix corresponding to a notch image, calculating style differences between the style matrix of the gray-scale image and the lossless style matrix, the corrosion style matrix and the notch style matrix respectively, judging whether the gray-scale image is abnormal or not according to the style differences, improving the efficiency of detecting the abnormality in the pipeline, and ensuring the accuracy of detection.
Based on the same inventive concept as the method embodiment, the embodiment of the present invention further provides an abnormality detection system for the inside of a pipeline, the system including: a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor, when executing the computer program, implements the steps of one of the above-described embodiments of the method for detecting an anomaly within a pipeline, such as the steps shown in fig. 1. The method for detecting an abnormality in a pipeline has been described in detail in the above embodiments, and is not described again.
It should be noted that: the sequence of the above embodiments of the present invention is only for description, and does not represent the advantages or disadvantages of the embodiments. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and should not be taken as limiting the scope of the present invention, which is intended to cover any modifications, equivalents, improvements, etc. within the spirit of the present invention.
Claims (8)
1. An anomaly detection method for the interior of a pipeline, characterized in that it comprises the following steps:
acquiring a pipeline image inside a pipeline, and graying the pipeline image to obtain a grayscale image;
acquiring a projection value of each row in the gray level image, and fitting the projection values of all the rows to a travel curve; carrying out difference on any two adjacent points in the row curve to obtain a corresponding difference value sequence, and calculating the average value of all elements in the difference value sequence to be used as a threshold value; obtaining a first characteristic point and a second characteristic point based on the threshold;
constructing a kernel window, sliding the kernel window on the gray image to update the gray image to obtain an updated image, and obtaining a third characteristic point and a fourth characteristic point based on the updated image;
calculating a characteristic value sequence of each pixel point in the gray level image based on the first characteristic point, the second characteristic point, the third characteristic point and the fourth characteristic point, and obtaining a style matrix according to the characteristic value sequence of each pixel point in the gray level image;
acquiring a lossless style matrix corresponding to a lossless image, a corrosive style matrix corresponding to a corrosive image and a notch style matrix corresponding to a notch image, calculating style differences between the style matrix of the gray-scale image and the lossless style matrix, the corrosive style matrix and the notch style matrix respectively, and judging whether the gray-scale image is abnormal or not according to the style differences.
2. The method of claim 1, wherein the step of obtaining the projection value of each row in the gray-scale image comprises:
and calculating the summation result of the gray values corresponding to all the pixel points in each row in the gray image as the projection value of the corresponding row.
3. The abnormality detection method for the inside of a pipeline according to claim 1, characterized in that said step of obtaining a first feature point and a second feature point based on said threshold value includes:
selecting corresponding lines of which the projection values are larger than the threshold value in the gray level image, and acquiring a first line and a last line in all the selected lines; the pixel point with the maximum gray value in the first line is a first characteristic point; and the pixel point with the maximum gray value in the last row is a second feature point.
4. The method according to claim 1, wherein the step of sliding the kernel window over the grayscale image to update the grayscale image to obtain an updated image comprises:
acquiring a kernel window corresponding to a pixel point to be processed, calculating the gray average value of all pixel points in the kernel window, and replacing the pixel value of the pixel point to be processed with the gray average value.
5. The abnormality detection method for the inside of a pipeline according to claim 1, characterized in that said step of obtaining a third feature point and a fourth feature point based on said updated image includes:
and obtaining the average value of all pixel points in the updated image as an image threshold, selecting all pixel points which are larger than the image threshold in the updated image, and selecting pixel points with the largest gray value in two lines with the minimum line number and the maximum line number from the selected pixel points as a third characteristic point and a fourth characteristic point.
6. The method according to claim 1, wherein the step of calculating the feature value sequence of each pixel point in the grayscale image based on the first feature point, the second feature point, the third feature point, and the fourth feature point comprises:
the eigenvalues are calculated as:
wherein the content of the first and second substances,
representing the characteristic value corresponding to the pixel point;
expressing the gray value of the pixel point;
a gray value representing the v-th feature point;
representing the distance between the pixel point and the v-th characteristic point;
and obtaining characteristic values corresponding to the pixel points based on the first characteristic points, the second characteristic points, the third characteristic points and the fourth characteristic points to form a characteristic value sequence.
7. The method for detecting the abnormality in the interior of the pipeline according to claim 1, wherein the method for acquiring the style difference is:
and calculating differences among corresponding elements in the style matrix, and acquiring the sum of squares corresponding to all the differences, wherein the smaller the sum of squares is, the larger the style difference is.
8. An anomaly detection system for the interior of a pipeline, comprising a memory, a processor and a computer program stored in said memory and executable on said processor, characterized in that said processor, when executing said computer program, carries out the steps of the method according to any one of the preceding claims 1 to 7.
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