Submarine pipeline leakage monitoring method and device based on machine vision

Abstract

The invention provides a submarine pipeline leakage monitoring method and device based on machine vision. The submarine pipeline leakage monitoring method comprises the steps of obtaining a visual image of a submarine pipeline; performing circularity feature extraction and aspect ratio feature extraction based on the visual image to obtain circularity features and aspect ratio features; based on the circularity feature and the aspect ratio feature, reconstructing the submarine pipeline through a preset space reconstruction model to generate a submarine pipeline model; based on the submarine pipeline model, positioning construction is carried out on the internal space of the submarine pipeline through a preset distance function value algorithm or moment characteristic quantity, and a pipeline internal model is generated; image slicing is carried out on the pipeline internal model, pixel-by-pixel comparison is carried out on the pipeline internal model and a pre-obtained pipeline internal image in an initial state, and leakage point positioning is completed. The problems that existing submarine pipeline leakage monitoring is poor in accuracy, and leakage points cannot be found in time are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of pipeline leakage monitoring, and in particular to a method and device for monitoring submarine pipeline leakage based on machine vision. Background Art

[0002] Submarine pipeline transportation has been widely used as a major mode of transportation for energy such as oil and gas due to its high efficiency, convenience, long-distance transportation, economy and reliability. In the long-distance submarine energy transportation process, the health monitoring of submarine pipelines is particularly important. Submarine transportation pipelines are placed on the seabed for a long time. If the pipelines are affected by seawater corrosion, geological disasters, external force damage and other factors, the submarine pipelines will be damaged and leaks will occur, which will cause pollution to the marine environment, waste of energy and immeasurable economic losses.

[0003] Submarine oil and gas pipelines are the lifeline for transporting oil and natural gas. They generally include oil and gas gathering and transportation pipelines laid on the seabed, trunk pipelines and attached booster platforms, as well as main pipes connecting pipelines and platforms. Submarine pipeline transportation has many advantages, such as large transportation volume, stability, high transportation efficiency, low cost, and little impact from climate. Therefore, pipeline transportation has always been the best choice for offshore oil and gas transportation. However, as the pipelines increase in service life, they will inevitably encounter aging, corrosion perforation, force majeure and changes in external conditions (such as earthquakes or collapse of seabed supports, etc.), as well as other natural or man-made damages, which will lead to frequent pipeline leakage accidents. Once a pipeline leaks, it will threaten people's lives and property safety, living environment, and cause serious waste of resources and other adverse consequences. Therefore, it is necessary to monitor whether the submarine oil and gas pipelines are leaking.

[0004] Leak detection of pipelines can be roughly divided into the following two types: external-based methods, that is, judging whether a leak occurs based on the detection of external parameters of the pipeline, such as hydrocarbons or temperature changes; internal-based methods, that is, using sensors to monitor the internal parameters of the pipeline, such as pressure, temperature, viscosity, density, flow, sound velocity, etc., and judging whether a leak occurs through calculation. At present, the existing internal-based leakage monitoring methods can be applied to submarine pipeline leakage monitoring. However, the monitoring method is single, and there are problems such as large errors, low precision, and low accuracy in locating the leakage point. In addition, the marine environment has an impact on the performance of pipeline leakage monitoring methods, such as instrument installation, SCADA and telemetry units. Another problem is the hydrostatic pressure of seawater on marine pipelines. External pressure may reduce the leakage amount. For hydrostatic pressure leakage, it may cause external water flow to enter the pipeline, making leakage detection more difficult. In addition, in submarine production systems, most of the pipelines are multiphase fluids, which makes leakage detection and positioning more difficult. Summary of the invention

[0005] The present invention provides a method and device for monitoring submarine pipeline leakage based on machine vision, which are used to solve the problems of poor accuracy and failure to timely discover leakage points in existing submarine pipeline leakage monitoring.

[0006] The present invention provides a method for monitoring submarine pipeline leakage based on machine vision, comprising: Get visual images of subsea pipelines; Performing circularity feature extraction and aspect ratio feature extraction based on the visual image to obtain circularity features and aspect ratio features; Reconstructing the submarine pipeline through a preset spatial reconstruction model based on the circularity feature and the aspect ratio feature to generate a submarine pipeline model; Based on the submarine pipeline model, the internal space of the submarine pipeline is located and constructed by using a preset distance function value algorithm or moment feature quantity to generate an internal model of the pipeline; The image slices of the pipeline internal model are compared pixel by pixel with the pre-acquired pipeline internal image in the initial state to locate the leakage point.

[0007] According to a method for monitoring submarine pipeline leakage based on machine vision provided by the present invention, obtaining a visual image of the submarine pipeline specifically includes: By setting up multiple image sensors around the submarine pipeline, images of the submarine pipeline at different angles are obtained respectively; The submarine pipeline images from different angles are stitched together to construct a complete visual image of the submarine pipeline.

[0008] According to a submarine pipeline leakage monitoring method based on machine vision provided by the present invention, the circularity feature extraction and the aspect ratio feature extraction are performed based on the visual image to obtain the circularity feature and the aspect ratio feature, which specifically includes: Performing feature extraction based on the visual image using circularity to obtain circularity features; The width and height of the target area are determined based on the visual image, and aspect ratio features are extracted to obtain aspect ratio features.

[0009] According to a method for monitoring submarine pipeline leakage based on machine vision provided by the present invention, the submarine pipeline is reconstructed through a preset spatial reconstruction model based on the circularity feature and the aspect ratio feature to generate a submarine pipeline model, which specifically includes: Performing modular feature construction based on the circularity feature and the aspect ratio feature through a preset spatial reconstruction model to generate a plurality of module components; Assemble multiple module components according to their connection and positional relationships to reconstruct submarine pipelines; After all module components are assembled, the subsea pipeline model is generated.

[0010] According to a method for monitoring submarine pipeline leakage based on machine vision provided by the present invention, the internal space of the submarine pipeline is located and constructed based on the submarine pipeline model by using a preset distance function value algorithm or moment feature quantity to generate an internal model of the pipeline, specifically comprising: Based on the submarine pipeline model, the distance between each module in the submarine pipeline model is calculated by using a distance function value algorithm; Based on the submarine pipeline model, characteristic quantities of each module in the submarine pipeline model are calculated by using moment characteristic quantities; Based on the distance between each module and the characteristic quantity of each module, the internal space of the submarine pipeline is located and constructed to generate the internal model of the pipeline.

[0011] According to a method for monitoring submarine pipeline leakage based on machine vision provided by the present invention, the image slicing of the internal model of the pipeline is compared pixel by pixel with the internal image of the pipeline in the pre-acquired initial state to locate the leakage point, which specifically includes: Slice the image of the internal model of the pipeline to obtain multiple slice images; Compare the sliced ​​image with the initial state image of the interior of the pipeline pre-acquired at the corresponding position pixel by pixel to find pixel differences; The leakage point is located according to the pixel difference point to complete the leakage point location.

[0012] The present invention also provides a submarine pipeline leakage monitoring device based on machine vision, comprising: An image acquisition module, used to acquire visual images of the submarine pipeline; A feature extraction module, used to extract circularity features and aspect ratio features based on the visual image, and obtain circularity features and aspect ratio features; A model reconstruction module, used to reconstruct the submarine pipeline through a preset spatial reconstruction model based on the circularity feature and the aspect ratio feature to generate a submarine pipeline model; An internal model construction module is used to locate and construct the internal space of the submarine pipeline based on the submarine pipeline model through a preset distance function value algorithm or moment feature quantity to generate an internal model of the pipeline; The leakage point positioning module is used to perform image slicing on the pipeline internal model and compare it pixel by pixel with the pre-acquired pipeline internal image in the initial state to complete the positioning of the leakage point.

[0013] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, a submarine pipeline leakage monitoring method based on machine vision as described above is implemented.

[0014] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for monitoring submarine pipeline leakage based on machine vision as described above is implemented.

[0015] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements any of the above-mentioned methods for monitoring submarine pipeline leakage based on machine vision.

[0016] The present invention provides a method and device for monitoring submarine pipeline leakage based on machine vision. The method and device extract circularity features and aspect ratio features based on the machine vision image of the submarine pipeline, obtain the circularity features and aspect ratio features, generate a submarine pipeline model and establish an internal model of the pipeline, accurately perform pixel-by-pixel comparison, accurately reproduce texture patterns, and quickly locate leakage points. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 It is a flow chart of the submarine pipeline leakage monitoring method based on machine vision provided by the present invention.

[0019] Figure 2 It is a schematic diagram of module connection of a submarine pipeline leakage monitoring device based on machine vision provided by the present invention.

[0020] Figure 3 It is a structural schematic diagram of the electronic device provided by the present invention.

[0021] Reference numerals: 110: image acquisition module; 120: feature extraction module; 130: model reconstruction module; 140: internal model building module; 150: leakage point location module; 310: processor; 320: communication interface; 330: memory; 340: communication bus. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] Combine the following Figure 1 A method for monitoring submarine pipeline leakage based on machine vision according to the present invention is described, comprising: step 100, obtaining a visual image of the submarine pipeline.

[0024] In the present invention, multiple image sensors are arranged around the submarine pipeline to obtain images of the submarine pipeline at different angles. The submarine pipeline images from different angles are stitched together to construct a complete visual image of the submarine pipeline.

[0025] Obtaining a complete visual image of the submarine pipeline will help to accurately locate the leak point later.

[0026] Step 200: extract circularity features and aspect ratio features based on the visual image to obtain circularity features and aspect ratio features.

[0027] Specifically, based on the visual image, circularity is used to perform feature extraction to obtain circularity features; The width and height of the target area are determined based on the visual image, and aspect ratio features are extracted to obtain aspect ratio features.

[0028] In the present invention, in image processing, circularity is a commonly used shape feature extraction method for evaluating the similarity between a contour shape and an ideal circle. Circularity can be used to detect and describe circular objects in an image or to evaluate the circularity of a contour.

[0029] Following are the steps for shape feature extraction using circularity: 1. Contour extraction: Extract the contour of the target from the image. You can use edge detection algorithm or contour detection algorithm to obtain the contour point set; 2. Calculation feature: Calculate the circularity feature of the contour. The circularity can be obtained by calculating the ratio of the area of ​​the contour to the area of ​​the circumscribed circle of the contour. The closer the circularity is to 1, the closer the contour is to a circle; Circularity = (4T*contour area) / (square of contour perimeter); Among them, the contour area can be obtained by calculating the number of pixels enclosed by the contour, the contour perimeter can be obtained by calculating the number of pixels on the contour, and π is the pi; 3. Feature application: According to application requirements, circularity can be used as a shape feature. For example, circularity can be used to filter out circular objects, or used as a feature in tasks such as image classification and object recognition.

[0030] Circularity can be used in applications such as shape description and shape matching. It has a good effect on circular targets or similar circular targets. However, for non-circular targets, the circularity may be lower than 1. Therefore, it needs to be considered in combination with other shape features when applied.

[0031] It should be noted that circularity, as a simple shape feature extraction method, may not be accurate or complete enough for complex shapes. In practical applications, it can be combined with other more comprehensive shape feature extraction methods, such as Fourier description, Zemnike moment, etc., to obtain richer and more accurate shape feature information.

[0032] The specific process of obtaining the aspect ratio feature is: determine the width and height of the target area; determining the width and height of the target area is the first step in calculating the aspect ratio, and it is necessary to obtain the width and height information of the target object in the original image through the relevant functions in the image processing software or programming language. In this step, it is necessary to clarify the position of the target object in the image and use appropriate tools or functions to measure its width and height.

[0033] To calculate the scaling factor, you need to calculate the scaling factor of the target object relative to the original image. The scaling factor can be obtained by dividing the width or height of the target area by its original size. If the width of the target object in the original image is 100 pixels, and the width in the processed image is 50 pixels, the width scaling factor is 0.5.

[0034] To calculate the aspect ratio, divide the width scaling factor of the target object by the height scaling factor to get the aspect ratio. If the target object is reduced in the horizontal direction and enlarged in the vertical direction, the aspect ratio is less than 1. If the target object is enlarged in the horizontal direction and reduced in the vertical direction, the aspect ratio is greater than 1.

[0035] In the field of image processing, aspect ratio is a very important parameter. It can be used to describe the size and shape changes of the target object in the image. By calculating the aspect ratio, the size and shape changes of the target object can be evaluated more accurately.

[0036] For a square, its area is the square of its side length, that is, Area = L2. The aspect ratio of a square is 1, which has no direct relationship with the area. For a circle, its area is π times the square of the radius, that is, Area = πxr 2 The aspect ratio (diameter / radius) of a circle has no direct relationship with its area, but the radius and diameter of a circle will affect its area.

[0037] Step 300: Reconstruct the submarine pipeline through a preset spatial reconstruction model based on the circularity feature and the aspect ratio feature to generate a submarine pipeline model.

[0038] Specifically, modular feature construction is performed based on the circularity feature and the aspect ratio feature through a preset spatial reconstruction model to generate a plurality of module components; Assemble multiple module components according to their connection and positional relationships to reconstruct submarine pipelines; After all module components are assembled, the subsea pipeline model is generated.

[0039] In the present invention, by establishing a submarine pipeline model, based on circularity and aspect ratio feature extraction, the problem of different sizes and changes in the internal spatial contrast of submarine oil and gas pipelines, large differences in the number and background of various texture pixels, and different degrees of regularity and randomness of the changes in the constituent textures is solved. For the problem that multi-parameter features are difficult to separate from visual data, a single value can be measured with high precision or the value most relevant to identification can be determined, and textures can be measured, identified and segmented very simply in complex environments such as submarine oil and gas pipelines containing oil and gas.

[0040] Step 400: Based on the submarine pipeline model, the internal space of the submarine pipeline is located and constructed by using a preset distance function value algorithm or moment feature quantity to generate an internal model of the pipeline.

[0041] Specifically, based on the submarine pipeline model, the distance between each module in the submarine pipeline model is calculated by a distance function value algorithm; Based on the submarine pipeline model, characteristic quantities of each module in the submarine pipeline model are calculated by using moment characteristic quantities; Based on the distance between each module and the characteristic quantity of each module, the internal space of the submarine pipeline is located and constructed to generate the internal model of the pipeline.

[0042] In the present invention, on the basis of comparing pixel by pixel with the texture outside the camera, circularity and aspect ratio extraction technology are used to describe and reconstruct the internal spatial shape of the submarine oil and gas pipeline, and an algorithm or moment feature with a distance function value is used to accurately locate and reconstruct the internal space of the pipeline, and pixel by pixel matching is performed in terms of intensity, which greatly reduces the descriptor and accurately reproduces the texture pattern.

[0043] Step 500: Slice the pipeline internal model and compare it pixel by pixel with the pre-acquired pipeline internal image in the initial state to locate the leakage point.

[0044] Specifically, the internal model of the pipeline is sliced ​​to obtain a plurality of slice images; Compare the sliced ​​image with the initial state image of the interior of the pipeline pre-acquired at the corresponding position pixel by pixel to find pixel differences; The leakage point is located according to the pixel difference point to complete the leakage point location.

[0045] A submarine pipeline leakage monitoring method based on machine vision provided by the present invention performs circularity feature extraction and aspect ratio feature extraction based on a machine vision image of the submarine pipeline, obtains circularity features and aspect ratio features, generates a submarine pipeline model and establishes an internal model of the pipeline, can accurately perform pixel-by-pixel comparison, accurately reproduce texture patterns, and quickly locate leakage points.

[0046] refer to Figure 2 The present invention also discloses a submarine pipeline leakage monitoring device based on machine vision, comprising: An image acquisition module 110, for acquiring a visual image of a submarine pipeline; A feature extraction module 120 is used to extract circularity features and aspect ratio features based on the visual image to obtain circularity features and aspect ratio features; A model reconstruction module 130, configured to reconstruct the submarine pipeline through a preset spatial reconstruction model based on the circularity feature and the aspect ratio feature, and generate a submarine pipeline model; The internal model construction module 140 is used to locate and construct the internal space of the submarine pipeline based on the submarine pipeline model by using a preset distance function value algorithm or moment feature quantity to generate an internal model of the pipeline; The leakage point positioning module 150 is used to perform image slicing on the pipeline internal model and compare it pixel by pixel with the pre-acquired pipeline internal image in the initial state to complete the positioning of the leakage point.

[0047] Wherein, obtaining the visual image of the submarine pipeline specifically includes: By setting up multiple image sensors around the submarine pipeline, images of the submarine pipeline at different angles are obtained respectively; The submarine pipeline images from different angles are stitched together to construct a complete visual image of the submarine pipeline.

[0048] The extracting circularity features and aspect ratio features based on the visual image to obtain circularity features and aspect ratio features specifically includes: Performing feature extraction based on the visual image using circularity to obtain circularity features; The width and height of the target area are determined based on the visual image, and aspect ratio features are extracted to obtain aspect ratio features.

[0049] The reconstructing the submarine pipeline based on the circularity feature and the aspect ratio feature through a preset spatial reconstruction model to generate a submarine pipeline model specifically includes: Performing modular feature construction based on the circularity feature and the aspect ratio feature through a preset spatial reconstruction model to generate a plurality of module components; Assemble multiple module components according to their connection and positional relationships to reconstruct submarine pipelines; After all module components are assembled, the subsea pipeline model is generated.

[0050] The method of positioning and constructing the internal space of the submarine pipeline based on the submarine pipeline model by using a preset distance function value algorithm or moment feature quantity to generate the internal model of the pipeline specifically includes: Based on the submarine pipeline model, the distance between each module in the submarine pipeline model is calculated by using a distance function value algorithm; Based on the submarine pipeline model, characteristic quantities of each module in the submarine pipeline model are calculated by using moment characteristic quantities; Based on the distance between each module and the characteristic quantity of each module, the internal space of the submarine pipeline is located and constructed to generate the internal model of the pipeline.

[0051] The slicing of the pipeline internal model and comparing the image of the pipeline internal model pixel by pixel with the pre-acquired initial state image of the pipeline internal model to locate the leakage point specifically include: Slice the image of the internal model of the pipeline to obtain multiple slice images; Compare the sliced ​​image with the initial state image of the interior of the pipeline pre-acquired at the corresponding position pixel by pixel to find pixel differences; The leakage point is located according to the pixel difference point to complete the leakage point location.

[0052] A submarine pipeline leakage monitoring device based on machine vision provided by the present invention performs circularity feature extraction and aspect ratio feature extraction based on a machine vision image of the submarine pipeline, obtains circularity features and aspect ratio features, generates a submarine pipeline model and establishes an internal model of the pipeline, can accurately perform pixel-by-pixel comparison, accurately reproduce texture patterns, and quickly locate leakage points.

[0053] Figure 3 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 3As shown, the electronic device may include: a processor 310, a communication interface 320, a memory 330 and a communication bus 340, wherein the processor 310, the communication interface 320 and the memory 330 communicate with each other through the communication bus 340. The processor 310 may call the logic instructions in the memory 330 to execute a method for monitoring leakage of a submarine pipeline based on machine vision, the method comprising: obtaining a visual image of the submarine pipeline; extracting circularity features and aspect ratio features based on the visual image to obtain circularity features and aspect ratio features; reconstructing the submarine pipeline through a preset spatial reconstruction model based on the circularity features and aspect ratio features to generate a submarine pipeline model; locating and constructing the internal space of the submarine pipeline through a preset distance function value algorithm or moment feature quantity based on the submarine pipeline model to generate a pipeline internal model; slicing the pipeline internal model and performing pixel-by-pixel comparison with the pre-acquired pipeline internal image in the initial state to complete the location of the leakage point.

[0054] In addition, the logic instructions in the above-mentioned memory 330 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0055] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute a submarine pipeline leakage monitoring method based on machine vision provided by the above methods, the method including: acquiring a visual image of the submarine pipeline; extracting circularity features and aspect ratio features based on the visual image to obtain circularity features and aspect ratio features; reconstructing the submarine pipeline through a preset spatial reconstruction model based on the circularity features and aspect ratio features to generate a submarine pipeline model; locating and constructing the internal space of the submarine pipeline based on the submarine pipeline model through a preset distance function value algorithm or moment feature quantity to generate a pipeline internal model; slicing the pipeline internal model and performing pixel-by-pixel comparison with the pre-acquired pipeline internal image in the initial state to complete the positioning of the leakage point.

[0056] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which is implemented by a processor to execute a method for monitoring submarine pipeline leakage based on machine vision provided by the above methods when the computer program is executed, the method comprising: acquiring a visual image of the submarine pipeline; extracting circularity features and aspect ratio features based on the visual image to acquire circularity features and aspect ratio features; reconstructing the submarine pipeline through a preset spatial reconstruction model based on the circularity features and aspect ratio features to generate a submarine pipeline model; locating and constructing the internal space of the submarine pipeline through a preset distance function value algorithm or moment feature quantity based on the submarine pipeline model to generate a pipeline internal model; slicing the pipeline internal model and performing pixel-by-pixel comparison with a pre-acquired pipeline internal image in an initial state to locate the leakage point.

[0057] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0058] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0059] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for monitoring submarine pipeline leakage based on machine vision, characterized in that: include: Get visual images of subsea pipelines; Performing circularity feature extraction and aspect ratio feature extraction based on the visual image to obtain circularity features and aspect ratio features; Reconstructing the submarine pipeline through a preset spatial reconstruction model based on the circularity feature and the aspect ratio feature to generate a submarine pipeline model; Based on the submarine pipeline model, the internal space of the submarine pipeline is located and constructed by using a preset distance function value algorithm or moment feature quantity to generate an internal model of the pipeline; The image slices of the pipeline internal model are compared pixel by pixel with the pre-acquired pipeline internal image in the initial state to locate the leakage point.

2. The method for monitoring submarine pipeline leakage based on machine vision according to claim 1, characterized in that: The obtaining of the visual image of the submarine pipeline specifically includes: By setting up multiple image sensors around the submarine pipeline, images of the submarine pipeline at different angles are obtained respectively; The submarine pipeline images from different angles are stitched together to construct a complete visual image of the submarine pipeline.

3. The method for monitoring submarine pipeline leakage based on machine vision according to claim 1, characterized in that: The extracting circularity features and aspect ratio features based on the visual image to obtain circularity features and aspect ratio features specifically includes: Performing feature extraction based on the visual image using circularity to obtain circularity features; The width and height of the target area are determined based on the visual image, and aspect ratio features are extracted to obtain aspect ratio features.

4. The method for monitoring submarine pipeline leakage based on machine vision according to claim 1, characterized in that: The reconstructing the submarine pipeline based on the circularity feature and the aspect ratio feature through a preset spatial reconstruction model to generate a submarine pipeline model specifically includes: Performing modular feature construction based on the circularity feature and the aspect ratio feature through a preset spatial reconstruction model to generate a plurality of module components; Assemble multiple module components according to their connection and positional relationships to reconstruct submarine pipelines; After all module components are assembled, the subsea pipeline model is generated.

5. The method for monitoring submarine pipeline leakage based on machine vision according to claim 1, characterized in that: The method of positioning and constructing the internal space of the submarine pipeline based on the submarine pipeline model by using a preset distance function value algorithm or moment feature quantity to generate the internal model of the pipeline specifically includes: Based on the submarine pipeline model, the distance between each module in the submarine pipeline model is calculated by using a distance function value algorithm; Based on the submarine pipeline model, characteristic quantities of each module in the submarine pipeline model are calculated by using moment characteristic quantities; Based on the distance between each module and the characteristic quantity of each module, the internal space of the submarine pipeline is located and constructed to generate the internal model of the pipeline.

6. The method for monitoring submarine pipeline leakage based on machine vision according to claim 1, characterized in that: The slicing of the pipeline internal model and comparing the image of the pipeline internal in the initial state pixel by pixel to locate the leakage point specifically include: Slice the image of the internal model of the pipeline to obtain multiple slice images; Compare the sliced ​​image with the initial state image of the interior of the pipeline pre-acquired at the corresponding position pixel by pixel to find pixel differences; The leakage point is located according to the pixel difference point to complete the leakage point location.

7. A submarine pipeline leakage monitoring device based on machine vision, characterized in that: include: An image acquisition module, used to acquire visual images of the submarine pipeline; A feature extraction module, used to extract circularity features and aspect ratio features based on the visual image, and obtain circularity features and aspect ratio features; A model reconstruction module, used to reconstruct the submarine pipeline through a preset spatial reconstruction model based on the circularity feature and the aspect ratio feature to generate a submarine pipeline model; An internal model construction module is used to locate and construct the internal space of the submarine pipeline based on the submarine pipeline model through a preset distance function value algorithm or moment feature quantity to generate an internal model of the pipeline; The leakage point positioning module is used to perform image slicing on the pipeline internal model and compare it pixel by pixel with the pre-acquired pipeline internal image in the initial state to complete the positioning of the leakage point.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method for monitoring submarine pipeline leakage based on machine vision as claimed in any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for monitoring submarine pipeline leakage based on machine vision as claimed in any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for monitoring submarine pipeline leakage based on machine vision as claimed in any one of claims 1 to 6 is implemented.

Images