A precise segmentation method for submarine cables in side-scan sonar images

Abstract

A method for accurately segmenting submarine cables from side-scan sonar images comprises the following steps: inputting an image I1, processing the image I1 using a cable feature automatic extraction filter based on a curvelet transform, extracting cable-related information, and obtaining a feature image I2; pixel-by-pixel multiplying the image I1 by the feature image I2 to obtain a feature image I3; using a Hough transform to find no less than three straight line segments in the feature image I3, recording the angle α of the longest line segment in the image, constructing linear structural units in the same direction as the cables in the image, performing noise elimination and binarization processing on the feature image I3 to obtain a binary image I4; performing cable morphology restoration on the binary image I4 to obtain a cable morphology restoration image I5, and retaining only the largest connected area in the image as the cable segmentation result for the submarine side-scan sonar image. The method solves the problems in side-scan sonar image segmentation that a large amount of data is required for training, high image clarity requirements are required, target color information needs to be obtained, and high image size requirements are required.

Description

Technical Field

[0001] The present application belongs to the technical field of side-scan sonar image processing, and in particular relates to a method for accurately segmenting submarine cables in side-scan sonar images. Background Art

[0002] In modern marine operations, submarine cables carry crucial tasks such as resource transportation and information transmission. Side-scan sonar is humanity's most crucial tool for seafloor perception. Identifying and segmenting cables in side-scan sonar images is fundamental to their tracking, maintenance, and repair. Side-scan sonar images often suffer from color loss, low contrast, and blurred edges, making target segmentation challenging.

[0003] Currently proposed segmentation techniques can be divided into two categories: deep learning-based image segmentation methods and traditional image segmentation methods. Deep learning-based methods include: Bharti, V., D. Lane, and S. Wang. "Learning to detect subsea pipelines with deep segmentation network and self-supervision," Global Oceans 2020: Singapore-US Gulf Coast, pp. 1-7, 2020. A deep neural network was designed to segment cable targets from optical images obtained from highly turbid seafloor scenes, which are similar to sonar images; Du X, Sun Y, Song Y, Dong L, and Zhao X. "Revealing the Potential of Deep Learning for Detecting Submarine Pipelines in Side-Scan Sonar Images: An Investigation of Pre-Training Datasets," Measurement Science and Technology, vol. 15, no. 19, p. 4873, 2023. GoogleNet was used to automatically detect and identify pipelines or cables from side-scan sonar images. However, deep learning-based methods require large amounts of data to fully train the network, incurring significant costs for image acquisition and manual annotation. Furthermore, deep learning-based methods typically require fixed input image sizes. Even models trained at multiple scales can only be applied to images of specific sizes, limiting the algorithm's applicability.

[0004] Traditional image segmentation methods have also been used in sonar image cable segmentation tasks. Paredes GM, Taveira-Pinto F. “An experimental technique to track mooring cables in smallscale models using image processing,” Ocean Engineering, vol. 111, pp. 439-448, 2016. A method for segmenting cables in images was proposed, using top-hat transformation to eliminate small noise points, and using Canny edge detector and morphological dilation to segment cable targets; “An underwater sonar image target segmentation method based on an adaptive cultural algorithm” (application number: CN201811086885.6, publication date: 2019-03-08) proposed an adaptive cultural algorithm to segment objects in side-scan sonar images; “A submarine pipeline detection and positioning method and system based on forward-looking sonar” (application number: CN202110891976.2, publication date: 2023-07-18) achieved detection and segmentation of pipeline targets in sonar images by performing dynamic threshold detection, binarization processing, pipeline line segment extraction and other operations on multi-beam domain data. Traditional image segmentation methods can be divided into clustering methods based on color differences and segmentation methods based on target edge feature extraction. The former, when faced with color-deficient side-scan sonar images, only relies on pixel value size for clustering and segmentation, and cannot distinguish between cable targets and interfering objects; the latter, although showing good effects in optical images, usually cannot achieve satisfactory results when applied to low-resolution and blurred-edge seabed sonar images. Summary of the Invention

[0005] In order to overcome the above problems, the purpose of this application is to propose a method for accurately segmenting submarine cables in side-scan sonar images, which can accurately segment the cables in side-scan sonar images.

[0006] In order to achieve the above objectives, the technical solutions adopted in this application are as follows:

[0007] A method for accurately segmenting submarine cables in side-scan sonar images, comprising:

[0008] Step 1: Input a seabed side-scan sonar image I1 containing cables, filter the seabed side-scan sonar image I1 containing cables using a cable feature automatic extraction filter based on curvelet transform, extract cable-related information from the image, and obtain a feature image I2;

[0009] Step 2: Multiply the seabed side-scan sonar image I1 containing the cable by the feature image I2 obtained in step 1 pixel by pixel to enhance the cable features while weakening the interference information in the background, thereby obtaining the feature image I3;

[0010] Step 3: For the feature image I3 obtained in step 2, use Hough transform to find no less than three straight line segments, and record the angle α of the longest line segment in the image as the direction angle of the cable in the image;

[0011] Step 4: Using the direction angle α of the cable in the image obtained in step 3, construct a linear structural unit in the same direction as the cable in the image;

[0012] Step 5: De-noise and binarize the feature image I3 obtained in step 2 to obtain a binary image I4.

[0013] Step 6: Perform cable shape restoration on the binary image I4 obtained in step 5. Use the linear structure unit obtained in step 4 to perform a convolution operation on the binary image I4, connect the pixel areas of the cable targets in the image, and obtain a cable shape restoration image I5.

[0014] Step 7: For the cable shape restoration image I5 obtained in step 6, only the largest connected area in the image is retained as the cable segmentation result of the seabed side-scan sonar image.

[0015] In step 1, the cable feature automatic extraction filter based on curvelet transform is used to filter the seabed side-scan sonar image I1 containing the cable, specifically:

[0016] The side-scan sonar image I1 containing cables is subjected to a 7-level curvelet decomposition. The numbers of directional divisions in levels 1-7 are 1, 16, 32, 32, 64, 64, and 128, respectively. After the curvelet decomposition, two sets of centrosymmetric coefficients at each level are used to reconstruct an image. The coefficients of levels 1-2 are directly filtered out, and the variance selection method is used for the coefficients of levels 3-7. In each level, the coefficients of the six areas with the largest variance are selected and retained, and the other coefficients are filtered out.

[0017] In step 5, a two-dimensional average constant false alarm algorithm is first used to filter out noise points from the feature image I3 obtained in step 2, and then the denoised image is binarized to obtain a binary image I4.

[0018] In step 6, the linear structure unit obtained in step 4 is used to perform a convolution operation on the binary image I4, and the cable targets in the image are connected in the pixel area to obtain the cable shape restoration image I5. Specifically, the linear structure unit obtained in step 4 is used as a mask to perform a convolution operation with the binary image I4, and the cables that are interrupted in the binary image I4 are reconnected to obtain the cable shape restoration image I5.

[0019] When the two-dimensional average constant false alarm algorithm is used to filter out noise from the feature image I3 obtained in step 2, the false alarm probability is set to 0.05. When the denoised image is binarized, the pixel values ​​of pixels with values ​​greater than 0 are all set to 1.

[0020] A side-scan sonar image submarine cable precise segmentation system, comprising:

[0021] Input module: Input a seabed side-scan sonar image I1 containing cables, and use a cable feature automatic extraction filter based on curvelet transform to filter the seabed side-scan sonar image I1 containing cables, extract information related to the cables in the image, and obtain a feature image I2;

[0022] The first processing module multiplies the seabed side-scan sonar image I1 containing the cable by the feature image I2 obtained by the input module pixel by pixel to enhance the cable features while weakening the interference information in the background, thereby obtaining the feature image I3;

[0023] The second processing module: For the feature image I3 obtained by the first processing module, use Hough transform to find no less than three straight line segments, and record the angle α of the longest line segment in the image as the direction angle of the cable in the image;

[0024] The third processing module: uses the direction angle α of the cable in the image obtained in the second processing module to construct a linear structure unit in the same direction as the cable in the image;

[0025] The fourth processing module performs noise elimination and binarization processing on the feature image I3 obtained by the first processing module to obtain a binary image I4;

[0026] Cable shape restoration module: performs cable shape restoration on the binary image I4 obtained by the fourth processing module. The linear structure unit obtained by the third processing module is used to perform a convolution operation on the binary image I4, and the cable targets in the image are connected in pixel areas to obtain a cable shape restoration image I5.

[0027] Output module: For the cable shape restoration image I5 obtained by the cable shape restoration module, only the largest connected area in the image is retained as the cable segmentation result of the seabed side-scan sonar image.

[0028] An electronic device, comprising:

[0029] at least one processor;

[0030] and, a memory communicatively coupled to the at least one processor;

[0031] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method for accurately segmenting submarine cables in side-scan sonar images.

[0032] A computer-readable storage medium stores a computer program. When the computer program is executed by a processor, a method for accurately segmenting submarine cables in side-scan sonar images is implemented.

[0033] Compared with the prior art, the present invention has the following advantages:

[0034] 1. Based on the straight line features of artificially laid submarine cables in side-scan sonar images, this application designs a line feature extraction filter based on curvelet transform, which can automatically extract cable information from side-scan sonar images while weakening the interference information in the background.

[0035] 2. This application proposes an image binarization method that does not require a pre-set threshold. Prior to the binarization operation, a two-dimensional average constant false alarm algorithm is used to remove noise from the feature image. Pixels in the denoised feature image with values ​​greater than 0 are set to 1. Since no threshold is required, there is no need to extract prior information from a large number of acquired images. This also reduces the impact of the rationality of the threshold setting on experimental results.

[0036] 3. This application designs a method for automatic restoration of line feature morphology, which can automatically restore the morphology of cables in any direction in the image without human intervention. The Hough transform is used to determine the angular information of the cable in the image, and then a linear structural unit in the same direction as the cable is constructed. Finally, the constructed linear structural unit is used to perform a convolution operation with the feature image. The non-continuous cables in the feature image can be restored and reconnected to obtain a complete cable target.

[0037] In summary, the algorithms involved in this application are improved or combined with each other based on the characteristics of artificially laid submarine cables in side-scan sonar images. The overall algorithm does not require prior knowledge as guidance, does not require a large amount of data for training, has low requirements for image clarity, does not need to obtain target color information, and has no special requirements for image size. It is suitable for segmenting cable targets in seabed side-scan sonar images. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 A flow chart of the method of this application;

[0039] Figure 2 This is a schematic diagram of the image of this application being divided by the curvelet 7-level decomposition coefficients;

[0040] Figure 3 This is the seabed side-scan sonar image of the embodiment of the present application;

[0041] Figure 4 is the normalized variance of the curvelet coefficients in each direction of levels 3-7 of the embodiment of the present application;

[0042] Figure 5 The image is reconstructed using the fourth-level curvelet coefficients of the embodiment of the present application;

[0043] Figure 6 This is a feature map of the image in the embodiment of the present application after it is subjected to a line feature automatic extraction filter based on curvelet transform;

[0044] Figure 7 This is a feature image obtained by fusing the feature map after the curvelet transform-based line feature automatic extraction filter in the embodiment of the present application with the original image;

[0045] Figure 8 The image after noise reduction and binarization processing in the embodiment of the present application;

[0046] Figure 9 In the embodiment of the present application, Hough transform is used to construct a linear structural unit in the same direction as the cable;

[0047] Figure 10 This is the morphological restoration result of the embodiment of this application;

[0048] Figure 11 This is the target segmentation result of the cable in the embodiment of the present application;

[0049] Figure 12 This is a comparison chart of the side scan sonar image and cable target segmentation results in an embodiment of the present application. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0051] like Figure 1 As shown, a method for accurately segmenting submarine cables in side-scan sonar images includes:

[0052] A method for accurately segmenting submarine cables in side-scan sonar images, comprising:

[0053] Step 1: Input a seabed side-scan sonar image I1 containing cables, filter the seabed side-scan sonar image I1 containing cables using a cable feature automatic extraction filter based on curvelet transform, extract cable-related information from the image, and obtain a feature image I2;

[0054] Step 2: Multiply the seabed side-scan sonar image I1 containing the cable by the feature image I2 obtained in step 1 pixel by pixel to enhance the cable features while weakening the interference information in the background, thereby obtaining the feature image I3;

[0055] Step 3: For the feature image I3 obtained in step 2, use Hough transform to find no less than three straight line segments, and record the angle α of the longest line segment in the image as the direction angle of the cable in the image;

[0056] Step 4: Using the direction angle α of the cable in the image obtained in step 3, construct a linear structural unit in the same direction as the cable in the image;

[0057] Step 5: De-noise and binarize the feature image I3 obtained in step 2 to obtain a binary image I4.

[0058] Step 6: Perform cable shape restoration on the binary image I4 obtained in step 5. Use the linear structure unit obtained in step 4 to perform a convolution operation on the binary image I4, connect the pixel areas of the cable targets in the image, and obtain a cable shape restoration image I5.

[0059] Step 7: For the cable shape restoration image I5 obtained in step 6, only the largest connected area in the image is retained as the cable segmentation result of the seabed side-scan sonar image.

[0060] In step 6, the linear structure unit obtained in step 4 is used to perform a convolution operation on the binary image I4, and the cable targets in the image are connected in the pixel area to obtain the cable shape restoration image I5. Specifically, the linear structure unit obtained in step 4 is used as a mask to perform a convolution operation with the binary image I4, and the cables that are interrupted in the binary image I4 are reconnected to obtain the cable shape restoration image I5.

[0061] Design of the automatic line feature extraction filter based on curvelet transform in step 1:

[0062] The image is decomposed into 7 levels of curvelet. The number of directional divisions of levels 1 to 7 are 1, 16, 32, 32, 64, 64, and 128 respectively. The coefficients after curvelet decomposition are divided as follows: Figure 2 As shown in the figure, it should be noted that after the image is decomposed by curvelet decomposition, two sets of coefficients that are symmetrical at each level reconstruct an image; the experiment found that after the seabed side-scan sonar image containing cables is decomposed by curvelet 7 levels, the low-frequency coefficients of levels 1-2 contain a lot of background information, while the cable information is concentrated in the high-frequency coefficients of specific directions of levels 3-7; the coefficients of levels 1-2 are directly filtered out, and the variance selection method is used for the coefficients of levels 3-7. The coefficients of the six areas with the largest variance are selected in each level to retain, and the other coefficients are filtered out.

[0063] In step 5, the feature image I3 obtained in step 2 is first filtered using a two-dimensional average constant false alarm (CFAR) algorithm to remove noise. The denoised image is then binarized. In the two-dimensional average constant false alarm (CFAR) algorithm, the false alarm probability is set to 0.05. Since the image has already undergone denoising, the binarization method does not require a separate binarization threshold. Pixels with values ​​greater than 0 are set to 1.

[0064] In step 6, the binary image I4 is convolved with the linear structure unit obtained in step 4 to connect the pixel regions of the cable targets in the image to obtain the cable morphology restoration image I5. Specifically, using the direction angle of the cable in the image obtained in step 3, a linear structure unit with a length of 40 pixels is constructed in step 4. The binary image I4 is convolved with the linear structure unit to connect the pixel regions representing the cable.

[0065] Example

[0066] This example is about Figure 3 Perform pipeline target segmentation.

[0067] (1) First, the image is filtered using a line feature automatic extraction filter based on curvelet transform. The variance of the curvelet coefficients in each direction of levels 3-7 is as follows: Figure 4 As shown, the variance value is normalized at each scale; taking the coefficient of level 4 as an example, the three images reconstructed by the six groups of coefficients selected by the variance selection method are as follows Figure 5 As shown; the feature map of the image after curvelet filtering is as follows Figure 6 shown.

[0068] (2) Multiply the original image and the filtered feature map pixel by pixel to obtain the fused feature map, such as Figure 7 shown.

[0069] (3) Using the two-dimensional average constant false alarm algorithm Figure 7 Noise elimination is performed, and the false alarm probability is set to 0.05; then a binarization operation is performed, and the pixel values ​​of the pixels with values ​​greater than 0 are set to 1, as shown below: Figure 8 The binary image shown.

[0070] (4) Yes Figure 7 Apply Hough transform to detect the three straight line segments and record the angle α of the longest line segment in the image as the direction angle of the cable in the image. Based on this, the linear structure unit is constructed. This process is as follows: Figure 9 shown.

[0071] (5) Morphological restoration of cables: Figure 9 The linear structural unit constructed in Figure 8After de-noising, the binary image is convolved to connect the pixel areas of the cable targets that may be interrupted to ensure the integrity of the final segmentation result. The morphological restoration result is as follows: Figure 10 shown.

[0072] (6) Only retain Figure 10 The largest connected area in is taken as the cable target segmentation result, such as Figure 11 shown.

[0073] like Figure 12 As shown, the left side figures (a1), (a2), (a3), (a4), (a5) and (a6) are side scan sonar images, and the right side figures (b1), (b2), (b3), (b4), (b5) and (b6) are corresponding cable target segmentation result figures. This application performs target segmentation on 6 seabed side scan sonar images containing cables and many interference targets. The 6 side scan sonar images are acquired under different background and distance conditions. The background contains a large amount of interference information such as seabed rocks, sand textures, and uneven terrain, and the overall image resolution is low. The algorithms designed in this application can accurately segment the cable targets therein.

[0074] A side-scan sonar image submarine cable precise segmentation system, comprising:

[0075] Input module: Input a seabed side-scan sonar image I1 containing cables, and use a cable feature automatic extraction filter based on curvelet transform to filter the seabed side-scan sonar image I1 containing cables, extract information related to the cables in the image, and obtain a feature image I2;

[0076] The first processing module multiplies the seabed side-scan sonar image I1 containing the cable by the feature image I2 obtained by the input module pixel by pixel to enhance the cable features while weakening the interference information in the background, thereby obtaining the feature image I3;

[0077] The second processing module: For the feature image I3 obtained by the first processing module, use Hough transform to find no less than three straight line segments in the feature image I3, and record the angle α of the longest line segment in the image as the direction angle of the cable in the image;

[0078] The third processing module: uses the direction angle α of the cable in the image obtained in the second processing module to construct a linear structure unit in the same direction as the cable in the image;

[0079] The fourth processing module performs noise elimination and binarization processing on the feature image I3 obtained by the first processing module to obtain a binary image I4;

[0080] Cable shape restoration module: performs cable shape restoration on the binary image I4 obtained by the fourth processing module. The linear structure unit obtained by the third processing module is used to perform a convolution operation on the binary image I4, and the cable targets in the image are connected in pixel areas to obtain a cable shape restoration image I5.

[0081] Output module: For the cable shape restoration image I5 obtained by the cable shape restoration module, only the largest connected area in the image is retained and output as the cable segmentation result of the seabed side-scan sonar image.

[0082] A terminal / electronic device / server, comprising:

[0083] at least one processor;

[0084] and, a memory communicatively coupled to the at least one processor;

[0085] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method for accurately segmenting submarine cables in side-scan sonar images.

[0086] A computer-readable storage medium stores a computer program. When the computer program is executed by a processor, a method for accurately segmenting submarine cables in side-scan sonar images is implemented.

[0087] It is not difficult to find that this embodiment is a system embodiment corresponding to the above-mentioned method embodiment, and this embodiment can be implemented in conjunction with the above-mentioned method embodiment. The relevant technical details and technical effects mentioned in the above-mentioned embodiments are still valid in this embodiment, and to reduce repetition, they are not repeated here. Accordingly, the relevant technical details mentioned in this embodiment can also be applied to the above-mentioned embodiments.

[0088] It is worth mentioning that all modules involved in this embodiment are logical modules. In actual applications, a logical unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. In addition, to highlight the innovation of this application, this embodiment does not include units that are not closely related to solving the technical problem proposed by this application. However, this does not mean that other units do not exist in this embodiment.

[0089] Another embodiment of the present application relates to an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method for accurately segmenting submarine cables in side-scan sonar images in the above-mentioned embodiments.

[0090] The memory and processor are connected using a bus, which can include any number of interconnected buses and bridges. The bus connects various circuits of one or more processors and memories. The bus can also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits. These are all well known in the art and are therefore not described further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be a single component or multiple components, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by the processor is transmitted over a wireless medium via an antenna. Furthermore, the antenna receives data and transmits it to the processor.

[0091] The processor is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory can be used to store data used by the processor when performing operations.

[0092] Another embodiment of the present application relates to a computer-readable storage medium storing a computer program, which implements the above method embodiment when executed by a processor.

[0093] That is, those skilled in the art will understand that all or part of the steps in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a program, which is stored in a storage medium and includes a number of instructions for causing a device (which may be a single-chip microcomputer, chip, etc.) or a processor to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: a USB flash drive, a mobile hard drive, a ROM (Read-Only Memory), a RAM (Random Access Memory), a magnetic disk, or an optical disk, etc., various media that can store program code.

[0094] Those skilled in the art will appreciate that the above embodiments are specific embodiments for implementing the present application, and that in actual applications, various changes may be made thereto in form and detail without departing from the spirit and scope of the present application.

Claims

1. A method for accurately segmenting submarine cables from side-scan sonar images, characterized in that: include: Step 1: Input a seabed side-scan sonar image I1 containing cables, filter the seabed side-scan sonar image I1 containing cables using a cable feature automatic extraction filter based on curvelet transform, extract cable-related information from the image, and obtain a feature image I2; Step 2: Multiply the seabed side-scan sonar image I1 containing the cable by the feature image I2 obtained in step 1 pixel by pixel to enhance the cable features while weakening the interference information in the background, thereby obtaining the feature image I3; Step 3: For the feature image I3 obtained in step 2, use Hough transform to find no less than three straight line segments, and record the angle α of the longest line segment in the image as the direction angle of the cable in the image; Step 4: Using the direction angle α of the cable in the image obtained in step 3, construct a linear structural unit in the same direction as the cable in the image; Step 5: De-noise and binarize the feature image I3 obtained in step 2 to obtain a binary image I4. Step 6: Perform cable shape restoration on the binary image I4 obtained in step 5. Use the linear structure unit obtained in step 4 to perform a convolution operation on the binary image I4, connect the pixel areas of the cable targets in the image, and obtain a cable shape restoration image I5. Step 7: For the cable shape restoration image I5 obtained in step 6, only the largest connected area in the image is retained as the cable segmentation result of the seabed side-scan sonar image.

2. The method for accurately segmenting submarine cables from side-scan sonar images according to claim 1, characterized in that: In step 1, the cable feature automatic extraction filter based on curvelet transform is used to filter the seabed side-scan sonar image I1 containing the cable, specifically: The side-scan sonar image I1 containing cables is subjected to a 7-level curvelet decomposition. The numbers of directional divisions in levels 1-7 are 1, 16, 32, 32, 64, 64, and 128, respectively. After the curvelet decomposition, two sets of centrosymmetric coefficients at each level are used to reconstruct an image. The coefficients of levels 1-2 are directly filtered out, and the variance selection method is used for the coefficients of levels 3-7. In each level, the coefficients of the six areas with the largest variance are selected and retained, and the other coefficients are filtered out.

3. The method for accurately segmenting submarine cables from side-scan sonar images according to claim 1, characterized in that: In step 5, a two-dimensional average constant false alarm algorithm is first used to filter out noise points from the feature image I3 obtained in step 2, and then the denoised image is binarized to obtain a binary image I4.

4. The method for accurately segmenting submarine cables from side-scan sonar images according to claim 1, characterized in that: In step 6, the linear structure unit obtained in step 4 is used to perform a convolution operation on the binary image I4, and the cable targets in the image are connected in the pixel area to obtain the cable shape restoration image I5. Specifically, the linear structure unit obtained in step 4 is used as a mask to perform a convolution operation with the binary image I4, and the cables that are interrupted in the binary image I4 are reconnected to obtain the cable shape restoration image I5.

5. The method for accurately segmenting submarine cables from side-scan sonar images according to claim 3, characterized in that: When the two-dimensional average constant false alarm algorithm is used to filter out noise from the feature image I3 obtained in step 2, the false alarm probability is set to 0.

05. When the denoised image is binarized, the pixel values ​​of pixels with values ​​greater than 0 are all set to 1.

6. A side-scan sonar image submarine cable accurate segmentation system, characterized by: include: Input module: Input a seabed side-scan sonar image I1 containing cables, and use a cable feature automatic extraction filter based on curvelet transform to filter the seabed side-scan sonar image I1 containing cables, extract information related to the cables in the image, and obtain a feature image I2; The first processing module multiplies the seabed side-scan sonar image I1 containing the cable by the feature image I2 obtained by the input module pixel by pixel to enhance the cable features while weakening the interference information in the background, thereby obtaining the feature image I3; The second processing module: For the feature image I3 obtained by the first processing module, use Hough transform to find no less than three straight line segments, and record the angle α of the longest line segment in the image as the direction angle of the cable in the image; The third processing module: uses the direction angle α of the cable in the image obtained in the second processing module to construct a linear structure unit in the same direction as the cable in the image; The fourth processing module performs noise elimination and binarization processing on the feature image I3 obtained by the first processing module to obtain a binary image I4; Cable shape restoration module: performs cable shape restoration on the binary image I4 obtained by the fourth processing module. The linear structure unit obtained by the third processing module is used to perform a convolution operation on the binary image I4, and the cable targets in the image are connected in pixel areas to obtain a cable shape restoration image I5. Output module: For the cable shape restoration image I5 obtained by the cable shape restoration module, only the largest connected area in the image is retained as the cable segmentation result of the seabed side-scan sonar image.

7. An electronic device, characterized in that: include: at least one processor; and, a memory communicatively coupled to the at least one processor; The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method for accurately segmenting submarine cables in side-scan sonar images according to any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for accurately segmenting submarine cables in side-scan sonar images according to any one of claims 1 to 5 is implemented.

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