A method and system for self-inspection of corrosion self-marking on the outer surface of a submarine pipeline

Abstract

The application provides a self-checking method and system for corrosion self-marking of an outer surface of a submarine pipeline. The method comprises: acquiring an initial pipeline image; obtaining an HSV space image, calculating depth information; performing joint bilateral filtering processing on the depth information, calculating transmittance; calculating a stray light signal; generating a non-stray light image to be restored from a diffuse reflection model; analyzing and judging the non-stray light image to obtain a rusted surface of the outer surface of the submarine pipeline and generate rust point coordinates. The application models the depth information of the collected image, obtains model parameters to restore the depth information of the image, and with the aid of the depth information of the collected image, the transmittance and stray light estimates can be calculated through the diffuse reflection model, and then the image is effectively restored to be clear, the rust point coordinates can be accurately found, corrosion hidden dangers can be effectively found, the operation safety of the submarine pipeline is ensured, and the problems of low detection precision and difficult positioning of the existing marine pipeline exploration method are solved.

Description

Technical Field

[0001] This invention relates to the field of corrosion detection technology along the outer surface of pipelines, and more specifically, to a self-inspection method for self-marking corrosion on the outer surface of submarine pipelines. Background Technology

[0002] Pipeline transportation of oil and natural gas, characterized by low investment, short transit times, large capacity, and high efficiency, is known as the "lifeline of energy" and holds a significant advantage among the five major transportation industries, playing a vital role in economic development. However, frequent safety accidents caused by subsea pipeline damage not only damage the ecological environment and cause incalculable losses to the national economy but also endanger people's lives. Therefore, the safety of subsea pipeline operations is receiving increasing attention from all sectors of society. Currently, external corrosion detection of subsea pipelines mainly relies on visual inspection by divers or underwater camera equipment. The collected video or image information is displayed using sophisticated instruments, and image processing technology is used to determine the surface corrosion condition. This not only places high demands on divers but also cannot achieve real-time pipeline monitoring. Furthermore, it is limited by sea conditions, water depth, and season. The lack of effective external corrosion detection methods for subsea pipelines has become a technological barrier affecting subsea pipeline transportation.

[0003] Chinese patent application CN107677717A discloses a device and method for detecting external corrosion of subsea pipelines. By analyzing the surface current density distribution and surrounding environmental electric field distribution characteristics of subsea pipelines under cathodic protection, the voltage difference method is applied to the damage detection of the external anti-corrosion layer of oil and gas pipelines in marine environments. The hardware and software design of the system is completed. On the hardware side, a data acquisition card is used as the core component to achieve high-speed signal acquisition. A suitable electrochemical signal measurement probe is selected, and signal amplification circuits and data processing modules for filtering and storage are designed. Remote communication between the surface and subsea processors is achieved through an optical fiber transmission module. Based on the hardware system, software for acquiring and analyzing the environmental electric field potential signals of subsea pipelines is developed using the LabVIEW development environment. This software can realize functions such as signal acquisition and storage, morphological filtering, spectrum analysis, and alarm for damaged corrosion points.

[0004] In existing technologies, including the aforementioned patents, the methods for detecting marine pipelines primarily rely on sound waves or electromagnetic waves. The presence of rust is detected by observing changes in the reflected ripples. However, interference from marine bio-electromagnetic waves and geomagnetic waves can significantly affect the detection. While filtering can reduce noise in the received reflected waves, interference remains and cannot be completely eliminated. Therefore, the detection results will inevitably contain some errors, and even small areas of rust may remain undetected. Summary of the Invention

[0005] In view of this, the present invention proposes a self-inspection method and system for self-marking corrosion on the outer surface of submarine pipelines, aiming to solve the problems of low detection accuracy and difficulty in positioning of existing marine pipeline exploration methods.

[0006] On one hand, this invention proposes a self-inspection method for self-marking corrosion on the outer surface of a subsea pipeline. This method includes the following steps: an image acquisition step, where an image of the outer surface of the subsea pipeline is acquired to obtain an initial pipeline image; a data conversion step, where the acquired initial pipeline image is converted from RGB color space to HSV space to obtain an HSV space image, and depth information is calculated; a data processing step, where the depth information obtained in the data conversion step is subjected to joint bilateral filtering, and the transmittance is calculated based on the filtered depth information; an astigmatic signal estimation step, where an astigmatic signal is calculated based on the depth information obtained in the data conversion step; an image output step, where an astigmatic-free image to be recovered is generated using a diffuse reflection model based on the transmittance obtained in the data processing step and the astigmatic signal obtained in the astigmatic signal estimation step; and a dynamic fixed-point signal generation step, where the astigmatic-free image to be recovered generated in the image output step is analyzed and judged to obtain the rust surface of the outer surface of the subsea pipeline, and the coordinates of the rust points are generated.

[0007] Furthermore, in the above-mentioned self-inspection method for self-marking corrosion on the outer surface of subsea pipelines, the data processing steps include the following sub-steps: a filtering sub-step, which performs joint bilateral filtering on the depth information obtained in the data conversion step to obtain filtered depth information; and a transmittance calculation sub-step, which calculates and obtains transmittance based on the filtered depth information.

[0008] Furthermore, in the above-mentioned self-inspection method for self-marking corrosion on the outer surface of subsea pipelines, in the filtering sub-step, the depth information is subjected to joint bilateral filtering processing according to the following formula:

[0009]

[0010] Where x is the coordinate of the current pixel, Dfilter(x) is the filtered depth information, k is the normalization coefficient, Ω(x) is the window centered at x, q is the coordinate of the pixel within the window, f is the spatial filtering function, g is the range filtering function, and D is the depth information.

[0011] Furthermore, the above-mentioned self-inspection method for self-marking corrosion on the outer surface of subsea pipelines uses the following formula to calculate the spatial filtering function:

[0012]

[0013] Where, σ d These are spatial filtering coefficients;

[0014] The range filtering function is calculated using the following formula:

[0015]

[0016] Where, σ r These are the range filtering coefficients.

[0017] Furthermore, in the above-mentioned self-inspection method for self-marking corrosion on the outer surface of subsea pipelines, the transmittance is calculated using the following formula in the transmittance calculation sub-step:

[0018] t(x)=exp(-beta*((Dfilter(x)+0.5) 2 -1));

[0019]

[0020] Where t(x) is the transmittance; Dfilter(x) is the filtered depth information; beta is the atmospheric scattering coefficient, ranging from 0 to 1; a is the minimum threshold value; and b is the maximum threshold value.

[0021] Furthermore, in the above-mentioned self-inspection method for self-marking corrosion on the outer surface of the subsea pipeline, in the astigmatic signal estimation step, the astigmatic signal is calculated based on the depth information obtained in the data conversion step. Specifically, the depth information D is subjected to minimum value filtering, the processed image data is sorted from largest to smallest, the pixels with the highest values ​​in the top 0.1% of the data are selected, and the average brightness of the corresponding pixels in the original image is taken as the astigmatic signal A.

[0022] Furthermore, the self-inspection method for self-marking corrosion on the outer surface of the aforementioned subsea pipeline calculates the astigmatic signal using the following formula:

[0023] A = MAX(A, Amax);

[0024] Where A is the astigmatic signal; Amax is the maximum threshold value; and MAX is the maximum value function.

[0025] Furthermore, in the above-mentioned self-inspection method for self-marking corrosion on the outer surface of subsea pipelines, the formula for calculating the depth information is as follows:

[0026] D(x)=θ0+θ1V(x)+θ2S(x)+ε(x);

[0027] Where x is the coordinate of the current pixel, D is the depth information, V is the brightness, and S is the saturation; θ0, θ1, and θ2 are all linear coefficients; ε is a random variable to represent the random graph of the model.

[0028] Furthermore, the above-mentioned self-inspection method for self-marking corrosion on the outer surface of the subsea pipeline further includes, after the image output step, an image acquisition control step, which controls the detection device where the image acquisition device is located based on the astigmatic-free image to be recovered generated in the image output step, thereby adjusting the position of the detection device to capture images at different positions in real time, and thus obtaining initial pipeline images at multiple different positions.

[0029] On the other hand, this invention proposes a self-inspection system for self-marking corrosion on the outer surface of a subsea pipeline. This system includes: an image acquisition module for acquiring images of the outer surface of the subsea pipeline to obtain an initial pipeline image; a data conversion module for converting the acquired initial pipeline image from RGB color space to HSV color space to obtain an HSV space image and calculating depth information; a data processing module for performing joint bilateral filtering on the depth information acquired by the data conversion module and calculating transmittance based on the filtered depth information; an astigmatism signal estimation module for calculating astigmatism signals based on the depth information acquired by the data conversion module; an image output module for generating a recoverable astigmatism-free image using a diffuse reflection model based on the transmittance acquired by the data processing module and the astigmatism signal obtained by the astigmatism signal estimation module; and a dynamic fixed-point signal generation module for analyzing and judging the recoverable astigmatism-free image generated by the image output module, obtaining the rust surface of the outer surface of the subsea pipeline, and generating rust point coordinates.

[0030] The self-inspection method for self-marking corrosion on the outer surface of subsea pipelines provided by this invention models the depth information of the acquired images, obtains model parameters to recover the depth information of the images, and uses the depth information of the acquired images to calculate the transmittance and astigmatism estimates through a diffuse reflection model, thereby effectively recovering the images and making them clear. It can accurately locate the coordinates of corrosion points, effectively detect potential corrosion hazards, ensure the safe operation of subsea pipelines, and solve the problems of low detection accuracy and difficult positioning in existing marine pipeline inspection methods. Attached Figure Description

[0031] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0032] Figure 1 A flowchart illustrating the self-inspection method for self-marking corrosion on the outer surface of a subsea pipeline provided in an embodiment of the present invention;

[0033] Figure 2 This is a schematic diagram of the detection device provided in an embodiment of the present invention;

[0034] Figure 3 This is a schematic diagram of the detection device provided in an embodiment of the present invention with the cover removed.

[0035] Figure 4 A flowchart illustrating the data processing steps provided in this embodiment of the invention;

[0036] Figure 5 This is another flowchart of the self-inspection method for self-marking corrosion on the outer surface of a subsea pipeline provided in this embodiment of the invention;

[0037] Figure 6 This is a structural block diagram of a self-inspection system for self-marking corrosion on the outer surface of a subsea pipeline provided in an embodiment of the present invention;

[0038] Figure 7 This is a structural block diagram of the data processing module provided in an embodiment of the present invention. Detailed Implementation

[0039] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0040] Method Implementation Examples:

[0041] See Figure 1 This is a flowchart illustrating a self-inspection method for self-marking corrosion on the outer surface of a subsea pipeline provided in an embodiment of the present invention. As shown in the figure, the self-inspection method includes the following steps:

[0042] Image acquisition step S1: Image acquisition is performed on the outer surface of the subsea pipeline to obtain an initial pipeline image.

[0043] Specifically, an image acquisition device installed on the detection device can be used to acquire an initial pipeline image I(x). Wherein, such as Figures 2 to 3As shown, the detection device can be a corrosion detection device for the outer surface of a subsea pipeline, including: a detection body 1; wherein, the forward end of the detection body 1 is provided with an image acquisition device 2, which can be a camera arranged facing the forward end, used to acquire images of the outer surface of the subsea pipeline. Of course, the image acquisition device 2 can also be other components, and no limitation is made on it in this embodiment. The rear end of the detection body 1 is provided with a propulsion propeller assembly 3, and the detection body 1 is also provided with a rising propulsion assembly 4 and a steering propulsion assembly 5. The rising propulsion assembly 4 and the steering propulsion assembly 5 are arranged vertically. The propulsion propeller assembly 2 is used to realize the horizontal forward and backward movement of the detection body 1. The rising propulsion assembly 3 is used to adjust the vertical 180° azimuth angle of the detection body 1. The steering propulsion assembly 4 is used to adjust the horizontal 180° azimuth angle of the detection body 1. The rising propulsion assembly 3 and the steering propulsion assembly 4 are also used to cooperate to realize the 360° rotation movement of the detection body 1. In this embodiment, the forward end of the detection body 1 may be provided with a transparent cover 6, and the port of the forward end of the detection body 1 is provided with a mounting plate 7. The mounting plate 7 may be a black plate, and there may be two image acquisition devices 2, which are symmetrically mounted on the mounting plate 7. Fiber optic lamp posts 8 are arranged in a circumferential array on the side wall of the mounting plate 7. The detection body 1 has symmetrically opened first through holes and second through holes, which are perpendicular to each other. There are two rising propeller groups 3, each disposed within one of the two first through holes, and two steering propeller groups 4, each disposed within one of the two second through holes. Each of the propulsion propeller group 2, rising propeller group 3, and steering propeller group 4 includes a motor and a propeller mounted on the motor output end, enabling the position adjustment of the detection body 1 in the water, thereby achieving image acquisition from different positions and directions. In this embodiment, image acquisition can be performed using the image acquisition devices 2 on the detection body 1, or it can be performed using other methods; no limitation is made in this embodiment.

[0044] In data conversion step S2, the acquired initial pipeline image is converted from RGB color space to HSV space to obtain an HSV space image, and depth information is calculated.

[0045] Specifically, the initial pipeline image I(x) is converted from the RGB color space (RGB Color Space, three primary color mode) to the HSV space to obtain an HSV space image, and the depth information D is calculated. The RGB color space is based on the three primary colors R (Red), G (Green), and B (Blue), which are superimposed to varying degrees to produce a rich and wide range of colors. The HSV (Hue, Saturation, Value) space is a hexagonal pyramid model, where the color parameters are hue (H), saturation (S), and value (V), meaning that modeling is based on the initial pipeline image. The formula for calculating the depth information D is:

[0046] D(x)=θ0+θ1V(x)+θ2S(x)+ε(x);

[0047] Where x is the coordinate of the current pixel, D is the depth information, V is the brightness, and S is the saturation; θ0, θ1, and θ2 are all linear coefficients; ε is a random variable to represent the random graph of the model.

[0048] In data processing step S3, the depth information obtained in the data conversion step is subjected to joint bilateral filtering, and the transmittance is calculated based on the filtered depth information.

[0049] Specifically, the depth information D can be subjected to joint bilateral filtering first, and the transmittance t(x) can be calculated based on the filtered depth information.

[0050] In the astigmatic signal estimation step S4, the astigmatic signal is calculated based on the depth information obtained in the data conversion step.

[0051] Specifically, the astigmatic signal A can be calculated based on the depth information D. The depth information D can be filtered by minimum values, and the processed image data can be sorted from largest to smallest. The pixels with the highest values ​​in the top 0.1% of the data are selected, and the average brightness of the corresponding pixels in the original image is taken as the astigmatic signal A. The astigmatic signal A is calculated using the following formula:

[0052] A = MAX(A, Amax);

[0053] Where A is the astigmatic signal; Amax is the maximum threshold value; and MAX is the maximum value function.

[0054] In the image output step S5, based on the transmittance obtained in the data processing step and the astigmatic signal obtained in the astigmatic signal estimation step, the diffuse reflection model generates the astigmatic-free image to be recovered.

[0055] Specifically, the unacquired image to be recovered can be obtained from the diffuse reflection model based on the transmittance t(x) and the astigmatic signal A.

[0056] In the dynamic fixed-point signal generation step S6, the astigmatic-free image to be recovered generated in the image output step is analyzed and judged to obtain the rust surface on the outer surface of the submarine pipeline and generate the coordinates of the rust points.

[0057] Specifically, images without acquisition can be uploaded for display, while the coordinate positioning information of the detection body 1 can be uploaded in real time, and the coordinates of the rust points can be marked to form key marker points; for example, the acquired image without astigmatism can be uploaded to the terminal, and the terminal can analyze and judge the rust surface based on the image without astigmatism, and generate the coordinates of the rust points based on the coordinate positioning information of the detection body 1.

[0058] See Figure 4 This is a flowchart of the data processing steps provided in an embodiment of the present invention. As shown in the figure, the data processing step S3 includes the following sub-steps:

[0059] In the filtering sub-step S31, the depth information obtained in the data conversion step S2 is subjected to joint bilateral filtering to obtain the filtered depth information.

[0060] Specifically, the depth information D obtained in data conversion step S2 is subjected to joint bilateral filtering to obtain filtered depth information. The joint bilateral filtering of the depth information D can be performed according to the following formula:

[0061]

[0062] Where x is the coordinate of the current pixel, Dfilter(x) is the filtered depth information, k is the normalization coefficient, Ω(x) is the window centered at x, q is the coordinate of the pixel within the window, f is the spatial filtering function, g is the range filtering function, and D is the depth information.

[0063] Furthermore, the spatial filtering function can be calculated using the following formula:

[0064]

[0065] Where, σ d These are spatial filtering coefficients.

[0066] Furthermore, the range filtering function is calculated using the following formula:

[0067]

[0068] Where, σ r These are the range filtering coefficients.

[0069] In this embodiment, the transmittance t(x) can be calculated using the following formula:

[0070] t(x)=exp(-beta*((Dfilter(x)+0.5) 2 -1));

[0071]

[0072] Where t(x) is the transmittance; Dfilter(x) is the filtered depth information; beta is the atmospheric scattering coefficient, ranging from 0 to 1; a is the minimum threshold value; and b is the maximum threshold value.

[0073] In the transmittance calculation sub-step S32, the transmittance is calculated based on the filtered depth information.

[0074] Specifically, the transmittance t(x) can be calculated using the following formula:

[0075] t(x)=exp(-beta*((Dfilter(x)+0.5) 2 -1));

[0076]

[0077] Where t(x) is the transmittance; Dfilter(x) is the filtered depth information; beta is the atmospheric scattering coefficient, ranging from 0 to 1; a is the minimum threshold value; and b is the maximum threshold value.

[0078] See Figure 5 This is another flowchart illustrating the self-inspection method for self-marking corrosion on the outer surface of a subsea pipeline provided in this embodiment of the invention. As shown in the figure, the self-inspection method may further include the following steps:

[0079] Image acquisition step S1: Image acquisition is performed on the outer surface of the subsea pipeline to obtain an initial pipeline image;

[0080] In data conversion step S2, the acquired initial pipeline image is converted from RGB color space to HSV space to obtain an HSV space image, and depth information is calculated.

[0081] In data processing step S3, the depth information obtained in the data conversion step is subjected to joint bilateral filtering, and the transmittance is calculated based on the filtered depth information.

[0082] Astigmatism signal estimation step S4: Calculate and obtain the astigmatism signal based on the depth information obtained in the data conversion step;

[0083] In the image output step S5, based on the transmittance obtained in the data processing step and the astigmatic signal obtained in the astigmatic signal estimation step, the astigmatic-free image to be recovered is generated by the diffuse reflection model.

[0084] In the dynamic fixed-point signal generation step S6, the astigmatic-free image to be recovered generated in the image output step is analyzed and judged to obtain the rust surface on the outer surface of the submarine pipeline and generate the coordinates of the rust points.

[0085] In the image acquisition control step S7, based on the astigmatic-free image to be recovered generated in the image output step, the detection device where the image acquisition device is located is controlled to adjust the position of the detection device so as to capture images at different positions in real time, thereby obtaining initial pipe images at multiple different positions.

[0086] Specifically, based on the generated astigmatic-free image to be recovered, the propeller assembly 3, the rising propeller assembly 4, and the steering propeller assembly 5 on the detection body 1 are controlled to coordinate through network data transmission. This controls the position adjustment of the detection body 1, thereby adjusting the position and direction of the image acquisition device 2 for image acquisition. This allows for real-time capture of images from different positions, obtaining multiple initial pipe images from different positions. After adjusting the position, the process jumps to the image acquisition step S1, and repeats the image acquisition step S1 until the dynamic fixed-point signal generation step S6, thus enabling the analysis and judgment of images from different positions.

[0087] There is no sequential order between the image acquisition control step S7 and the dynamic fixed-point signal generation step S6. After the image acquisition control step S7, the process can jump to the image acquisition step S1 and loop to the dynamic fixed-point signal generation step S6 to repeatedly process and analyze the initial pipeline image acquired in real time in order to analyze whether there are rust points at each location, that is, to determine the corrosion location.

[0088] In summary, the self-inspection method for self-marking corrosion on the outer surface of subsea pipelines provided in this embodiment models the depth information of the acquired images, obtains model parameters to recover the depth information of the images, and, with the help of the depth information of the acquired images, calculates the transmittance and astigmatism estimates through a diffuse reflection model, thereby effectively recovering the images and making them clear. It can accurately locate the coordinates of corrosion points, effectively detect potential corrosion hazards, ensure the operational safety of subsea pipelines, and solve the problems of low detection accuracy and difficult positioning in existing marine pipeline inspection methods.

[0089] System Implementation Example:

[0090] See Figure 6The present invention provides a structural block diagram of a self-inspection system for self-marking corrosion on the outer surface of a subsea pipeline. As shown in the figure, the self-inspection system includes: an image acquisition module 100, a data conversion module 200, a data processing module 300, an astigmatism signal estimation module 400, an image output module 500, a dynamic fixed-point signal generation module 600, and an image acquisition control module 700. The image acquisition module 100 is used to acquire images of the outer surface of the subsea pipeline to obtain an initial pipeline image; the data conversion module 200 is used to convert the acquired initial pipeline image from RGB color space to HSV space to obtain an HSV space image and calculate depth information; the data processing module 300 is used to perform joint bilateral filtering on the depth information obtained by the data conversion module and calculate the transmittance based on the filtered depth information; the astigmatism signal estimation module 400... The image output module 500 is used to calculate and obtain astigmatic signals based on the depth information obtained by the data conversion module; the image output module 500 is used to generate an astigmatic-free image to be recovered from the diffuse reflection model based on the transmittance obtained by the data processing module and the astigmatic signal obtained by the astigmatic signal estimation module; the dynamic fixed-point signal generation module 600 is used to analyze and judge the astigmatic-free image to be recovered generated by the image output module, obtain the rust surface of the outer surface of the subsea pipeline, and generate the coordinates of the rust points; the image acquisition control module 700 is used to control the detection device where the image acquisition device is located based on the astigmatic-free image to be recovered generated by the image output module, realize the adjustment of the position of the detection device, so as to capture images at different positions in real time, and thus obtain initial pipeline images at multiple different positions.

[0091] In this embodiment, the image acquisition control module 700 can be a remote control module. Based on the generated astigmatic-free image, it can use network data transmission to control the propeller assembly 3, the rising propeller assembly 4, and the steering propeller assembly 5 on the detection body 1 to coordinate and control the position adjustment of the detection body 1.

[0092] See Figure 7 This is a structural block diagram of the data processing module provided in an embodiment of the present invention. As shown in the figure, the data processing module 300 includes: a filtering processing unit 310 and a transmittance calculation unit 320; wherein, the filtering processing unit 310 is used to perform joint bilateral filtering processing on the depth information obtained by the data conversion module to obtain filtered depth information; the transmittance calculation unit 320 is used to calculate and obtain transmittance based on the filtered depth information.

[0093] Preferably, the filtering unit 310 is used to perform joint bilateral filtering on the depth information according to the following formula:

[0094]

[0095] Where x is the coordinate of the current pixel, Dfilter(x) is the filtered depth information, k is the normalization coefficient, Ω(x) is the window centered at x, q is the coordinate of the pixel within the window, f is the spatial filtering function, g is the range filtering function, and D is the depth information.

[0096] More preferably, the spatial domain filtering function is calculated using the following formula:

[0097]

[0098] Where, σ d These are spatial filtering coefficients.

[0099] More preferably, the range filtering function is calculated using the following formula:

[0100]

[0101] Where, σ r These are the range filtering coefficients.

[0102] Preferably, the transmittance calculation unit 320 is also used to calculate the transmittance using the following formula:

[0103] t(x)=exp(-beta*((Dfilter(x)+0.5) 2 -1));

[0104]

[0105] Where t(x) is the transmittance; Dfilter(x) is the filtered depth information; beta is the atmospheric scattering coefficient, ranging from 0 to 1; a is the minimum threshold value; and b is the maximum threshold value.

[0106] Preferably, the astigmatic signal is calculated based on the depth information obtained by the data conversion module. Specifically, the depth information D is subjected to minimum value filtering, the processed image data is sorted from largest to smallest, the pixels with the highest values ​​in the first 0.1% of the data are selected, and the average brightness of the corresponding pixels in the original image is taken as the astigmatic signal A.

[0107] More preferably, the astigmatism signal is calculated using the following formula:

[0108] A = MAX(A, Amax);

[0109] Where A is the astigmatic signal; Amax is the maximum threshold value; and MAX is the maximum value function.

[0110] Preferably, the formula for calculating depth information is:

[0111] D(x)=θ0+θ1V(x)+θ2S(x)+ε(x);

[0112] Where D represents depth information, V represents brightness, and S represents saturation; θ0, θ1, and θ2 are all linear coefficients; ε is a random variable to represent the random graph of the model.

[0113] Furthermore, the system also includes a network transmission module; wherein, the network transmission module is used to use the 5G network to realize the control of the image acquisition control module 700 on the detection body 1, and also uses the 5G network to realize the uploading of the astigmatic-free image generated by the image output module 500 by the image acquisition control module 700, so that the image output module 500 can upload the generated astigmatic-free image to the terminal. The dynamic fixed-point signal generation module 600 can be located at the terminal. The dynamic fixed-point signal generation module 600 of the terminal analyzes and judges the rust surface based on the astigmatic-free image, and generates the coordinates of the rust points.

[0114] In this embodiment, the image acquisition module 100, data conversion module 200, data processing module 300, astigmatism signal estimation module 400, image output module 500, dynamic fixed-point signal generation module 600, and image acquisition control module 700 can be mounted on an integrated circuit board, which can be mounted on the detection body 1.

[0115] In summary, the self-inspection system for self-marking corrosion on the outer surface of subsea pipelines provided in this embodiment models the depth information of the acquired images, obtains model parameters to recover the depth information of the images, and, with the help of the depth information of the acquired images, calculates the transmittance and astigmatism estimates through a diffuse reflection model, thereby effectively recovering the images and making them clear. It can accurately locate the coordinates of corrosion points, effectively detect potential corrosion hazards, ensure the operational safety of subsea pipelines, and solve the problems of low detection accuracy and difficult positioning in existing marine pipeline inspection methods.

[0116] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0117] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0118] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0119] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0120] Specific embodiments have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

[0121] The embodiments of this application also provide a specific implementation of an electronic device capable of implementing all the steps in the methods described above. The electronic device specifically includes the following:

[0122] Processor, memory, communications interface, and bus;

[0123] The processor, memory, and communication interface communicate with each other via a bus.

[0124] The processor is used to invoke a computer program stored in memory. When the processor executes the computer program, it implements all the steps in the methods described above. For example, when the processor executes the computer program, it implements the following steps:

[0125] The image acquisition step involves acquiring images of the outer surface of the subsea pipeline to obtain an initial pipeline image.

[0126] The data conversion step converts the acquired initial pipeline image from the RGB color space to the HSV color space to obtain an HSV space image and calculates depth information.

[0127] The data processing step involves performing joint bilateral filtering on the depth information calculated in the data conversion step, and calculating the transmittance based on the filtered depth information.

[0128] The astigmatism signal estimation step calculates the astigmatism signal based on the depth information calculated in the data conversion step.

[0129] In the image output step, based on the transmittance calculated in the data processing step and the astigmatic signal calculated in the astigmatic signal estimation step, an astigmatic-free image to be recovered is generated by the diffuse reflection model.

[0130] The dynamic fixed-point signal generation step analyzes and judges the astigmatic-free image to be recovered generated in the image output step, obtains the rust surface of the outer surface of the submarine pipeline, and generates the coordinates of the rust points.

[0131] Embodiments of this application also provide a computer-readable storage medium capable of implementing all steps of the methods in the above embodiments. The computer-readable storage medium stores a computer program that, when executed by a processor, implements all steps of the methods in the above embodiments. For example, when the processor executes the computer program, it implements the following steps:

[0132] The image acquisition step involves acquiring images of the outer surface of the subsea pipeline to obtain an initial pipeline image.

[0133] The data conversion step converts the acquired initial pipeline image from the RGB color space to the HSV color space to obtain an HSV space image and calculates depth information.

[0134] The data processing step involves performing joint bilateral filtering on the depth information calculated in the data conversion step, and calculating the transmittance based on the filtered depth information.

[0135] The astigmatism signal estimation step calculates the astigmatism signal based on the depth information calculated in the data conversion step.

[0136] In the image output step, based on the transmittance calculated in the data processing step and the astigmatic signal calculated in the astigmatic signal estimation step, an astigmatic-free image to be recovered is generated by the diffuse reflection model.

[0137] The dynamic fixed-point signal generation step analyzes and judges the astigmatic-free image to be recovered generated in the image output step, obtains the rust surface of the outer surface of the submarine pipeline, and generates the coordinates of the rust points.

[0138] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on its differences from other embodiments. In particular, for hardware + program embodiments, since they are basically similar to the method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. Although the embodiments in this specification provide method operation steps as shown in the embodiments or flowcharts, more or fewer operation steps may be included based on conventional or non-inventive means. The order of steps listed in the embodiments is merely one possible execution order among many steps and does not represent the only execution order. In actual device or terminal product execution, the method can be executed in the order shown in the embodiments or drawings or in parallel (e.g., in a parallel processor or multi-threaded processing environment, or even a distributed data processing environment). The terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, product, or apparatus that includes a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, product, or apparatus. Without further limitations, it is not excluded that other identical or equivalent elements may exist in the process, method, product, or apparatus that includes elements. For ease of description, the above devices are described in terms of function, divided into various modules. Of course, in implementing the embodiments of this specification, the functions of each module can be implemented in one or more software and / or hardware, or the same function can be implemented by a combination of multiple sub-modules or sub-units. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the shown or discussed mutual couplings or direct couplings or communication connections may be through some interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms. This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions, which are executable by the processor of the computer or other programmable data processing device, produce instructions for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0139] Those skilled in the art will understand that the embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, the embodiments of this specification can take the form of computer program products implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The various embodiments in this specification are described in a progressive manner, and similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and relevant parts can be referred to the description of the method embodiments. In the description of this specification, the reference to the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., means that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the embodiments of this specification.

[0140] In this specification, the illustrative expressions of the terms used do not necessarily refer to the same embodiments or examples. Furthermore, those skilled in the art can combine and integrate different embodiments or examples described in this specification, as well as features of different embodiments or examples, without contradiction. The above descriptions are merely embodiments of this specification and are not intended to limit the embodiments of this specification. Various modifications and variations can be made to the embodiments of this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the embodiments of this specification should be included within the scope of the claims of the embodiments of this specification.

Claims

1. A self-inspection method for self-marking corrosion on the outer surface of a subsea pipeline, characterized in that, Includes the following steps: The image acquisition step involves acquiring images of the outer surface of the subsea pipeline to obtain an initial pipeline image. The data conversion step converts the acquired initial pipeline image from the RGB color space to the HSV color space to obtain an HSV space image, and calculates to obtain depth information; The data processing step involves performing joint bilateral filtering on the depth information obtained in the data conversion step, and calculating the transmittance based on the filtered depth information. The astigmatic signal estimation step calculates and obtains the astigmatic signal based on the depth information obtained in the data conversion step. In the image output step, based on the transmittance obtained in the data processing step and the astigmatic signal obtained in the astigmatic signal estimation step, an astigmatic-free image to be recovered is generated by the diffuse reflection model. The dynamic fixed-point signal generation step analyzes and judges the astigmatic-free image to be recovered generated in the image output step, obtains the rust surface of the outer surface of the subsea pipeline, and generates the coordinates of the rust points. The data processing steps include the following sub-steps: The filtering sub-step performs joint bilateral filtering on the depth information obtained in the data conversion step to obtain filtered depth information. The transmittance calculation sub-step calculates the transmittance based on the filtered depth information. In the transmittance calculation sub-step, the transmittance is calculated using the following formula: ； ； Where t(x) is the transmittance; Dfilter(x) is the filtered depth information; beta is the atmospheric scattering coefficient, ranging from 0 to 1; a is the minimum threshold value, and b is the maximum threshold value. In the astigmatism signal estimation step, the astigmatism signal is calculated based on the depth information obtained in the data conversion step, specifically as follows: Minimum filtering is applied to the depth information D. The processed image data is sorted from largest to smallest, and the pixels with the highest values ​​in the top 0.1% of the data are selected. The average brightness of the corresponding pixels in the original image is taken as the astigmatism signal A.

2. The self-inspection method for self-marking corrosion on the outer surface of a subsea pipeline according to claim 1, characterized in that, In the filtering sub-step, the depth information is subjected to joint bilateral filtering according to the following formula: ； Where x is the coordinate of the current pixel, Dfilter(x) is the filtered depth information, k is the normalization coefficient, Ω(x) is the window centered at x, q is the coordinate of the pixel within the window, f is the spatial filtering function, g is the range filtering function, and D is the depth information.

3. The method for self-monitoring of the external surface corrosion self-marking of a submarine pipeline according to claim 2, characterized by the fact that, The spatial domain filtering function is calculated using the following formula: ； wherein is a spatial filter coefficient; The range filtering function is calculated using the following formula: ； wherein is a range filter coefficient.

4. The self-inspection method for self-marking corrosion on the outer surface of a subsea pipeline according to claim 1, characterized in that, The astigmatic signal is calculated using the following formula: A = MAX(A, Amax); Where A is the astigmatic signal; Amax is the maximum threshold value; and MAX is the maximum value function.

5. The self-inspection method for self-marking corrosion on the outer surface of a subsea pipeline according to any one of claims 1 to 3, characterized in that, The formula for calculating the depth information is: ； Where x is the coordinate of the current pixel, D is the depth information, V is the brightness, and S is the saturation; θ0, θ1, and θ2 are all linear coefficients; ε is a random variable to represent the random graph of the model.

6. The self-inspection method for self-marking corrosion on the outer surface of a subsea pipeline according to any one of claims 1 to 3, characterized in that, The image output step is followed by: The image acquisition control step, based on the astigmatism-free image to be recovered generated in the image output step, controls the detection device where the image acquisition device is located, thereby adjusting the position of the detection device to capture images at different positions in real time, and thus obtaining initial pipe images at multiple different positions.

7. A self-inspection system for self-marking corrosion on the outer surface of a subsea pipeline, characterized in that, include: The image acquisition module is used to acquire images of the outer surface of the subsea pipeline in order to obtain an initial image of the pipeline. The data conversion module is used to convert the acquired initial pipeline image from RGB color space to HSV color space to obtain an HSV space image, and calculate to obtain depth information; The data processing module is used to perform joint bilateral filtering on the depth information obtained by the data conversion module, and calculate the transmittance based on the filtered depth information. The astigmatism signal estimation module is used to calculate and obtain the astigmatism signal based on the depth information obtained by the data conversion module. The image output module is used to generate an astigmatism-free image to be recovered from the diffuse reflection model based on the transmittance obtained by the data processing module and the astigmatism signal obtained by the astigmatism signal estimation module. The dynamic fixed-point signal generation module is used to analyze and judge the astigmatic-free image to be recovered generated by the image output module, obtain the rust surface on the outer surface of the subsea pipeline, and generate the coordinates of the rust points. The data processing module includes: The filtering processing unit is used to perform joint bilateral filtering on the depth information obtained by the data conversion module to obtain filtered depth information. The transmittance calculation unit is used to calculate and obtain the transmittance based on the filtered depth information. In the transmittance calculation sub-step, the transmittance is calculated using the following formula: ； ； Where t(x) is the transmittance; Dfilter(x) is the filtered depth information; beta is the atmospheric scattering coefficient, ranging from 0 to 1; a is the minimum threshold value; and b is the maximum threshold value. In the astigmatism signal estimation step, the astigmatism signal is calculated based on the depth information obtained in the data conversion step, specifically as follows: Minimum filtering is applied to the depth information D. The processed image data is sorted from largest to smallest, and the pixels with the highest values ​​in the top 0.1% of the data are selected. The average brightness of the corresponding pixels in the original image is taken as the astigmatism signal A.

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