A wake detection method and device based on information entropy
Patent Information
- Application Number
- CN202611005511.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-07-07
- Publication Date
- 2026-10-20
Smart Images
Figure FT_1 
Figure FT_2 
Figure FT_3
Abstract
Description
Technical Field
[0001] This specification relates to one or more embodiments in the field of underwater target detection and sonar image processing technology, and in particular to a wake detection method and apparatus based on information entropy. Background Technology
[0002] Multibeam acoustic imaging is widely used in underwater seabed topography and sediment detection. Using multibeams for wake imaging can yield wide-coverage wake detection results. Conventional wake detection primarily utilizes amplitude information, detecting wakes by observing changes in amplitude. However, amplitude-based detection methods are unsuitable when wake echo intensity is low, especially in thin wakes, where amplitude detection methods risk failure, failing to accurately identify the presence and extent of the wake and thus failing to meet the wake detection requirements in complex underwater environments.
[0003] Wake echoes are formed by the scattering of air bubbles, and these bubbles exhibit distinct motion characteristics. Driven by buoyancy, viscous drag, and turbulent pulsation lift, wake bubbles undergo scale-dependent buoyancy, turbulent vortex-coupled transport, and dynamic evolution involving coalescence, breakup, and dissolution. These motion characteristics are directly reflected in the time-domain variations of the sonar echo signal. Current technologies lack a solution for wake detection that utilizes the entropy change in sonar echo velocity caused by wake bubble motion. This fails to fully leverage the motion characteristics of wake bubbles to improve detection accuracy and hinders detection in scenarios with thin wakes and weak echoes. Therefore, a method is urgently needed that can utilize the echo characteristics of wake bubbles to extract signal representations generated by the scattering of bubble motion in the sound wave, and utilize the entropy change in wake echo velocity to achieve wake detection. Application content
[0004] This application describes a wake detection method and apparatus based on information entropy, which can solve the above-mentioned technical problems.
[0005] According to the first aspect, a wake detection method based on information entropy is provided, including the following steps: acquiring discrete water body data in the beam domain;
[0006] The discrete water body data is gridded and mapped to obtain a sonar water body image;
[0007] Optical flow estimation is performed on at least two adjacent sonar water images to obtain the corresponding optical flow field. The optical flow field includes the motion direction information and motion amplitude information of each pixel between the adjacent sonar water images. The distribution characteristics of the optical flow vector correspond to the motion characteristics of the wake bubble.
[0008] The direction angle and magnitude of the velocity vector are determined based on the velocity vector of each pixel in the optical flow field between adjacent sonar image frames;
[0009] The velocity vector direction is divided into n directional regions; where n is an integer greater than 1.
[0010] Calculate the information entropy of the velocity vector in each directional region;
[0011] The wake detection result is obtained based on the information entropy.
[0012] In some possible embodiments, the step of performing gridding mapping on the discrete water body data to obtain a sonar water body image includes:
[0013] The range of the sonar water body data imaging grid is determined based on the beam opening angle range and the number of data sampling points;
[0014] Determine the grid spacing according to preset conditions;
[0015] Calculate the position of each grid cell;
[0016] The discrete water volume scattering intensity data is mapped to each grid to obtain a sonar water image after gridding.
[0017] In some possible embodiments, prior to performing optical flow estimation on at least two adjacent frames of the sonar water imagery, the method further includes dynamically assigning brightness to the sonar water imagery, including:
[0018] Dynamic mapping processing is performed on the intensity values of sonar water images after gridding and mapping.
[0019] The intensity values of the sonar water image are mapped to a linear grayscale range of 0 to 255 by optimizing the mapping function.
[0020] In some possible embodiments, the calculation of the information entropy of the velocity vector in each directional region includes:
[0021] Divide the velocity vector direction into n regions;
[0022] The probability distribution is obtained by summing the magnitudes of the velocity vectors in the same region and dividing by the sum of the magnitudes of the total velocity vectors. ;
[0023] The information entropy is calculated using the following formula;
[0024] .
[0025] In some possible embodiments, identifying the wake based on the information entropy includes:
[0026] Wake flows are identified based on the characteristics of information entropy changes. When the information entropy value is high, it is determined that there is no wake flow in the sonar image sequence to be detected, and the wake flow detection result is output.
[0027] When the information entropy value decreases rapidly, it is determined that a wake appears in the sonar image sequence to be detected, and the wake detection result is output.
[0028] As the information entropy value gradually increases, it is determined that the wake dissipates in the sonar image sequence to be detected, and the wake detection result is output.
[0029] In some possible embodiments, optical flow estimation is performed on at least two adjacent frames of sonar water images, including:
[0030] Treating the grayscale values of pixels in a sonar image as a function of two-dimensional variables ;
[0031] Construct a local coordinate system centered on a preset pixel;
[0032] Functions with respect to two-dimensional variables Perform a binomial expansion; the binomial expansion includes: selecting a square neighborhood of size 2n+1 centered on the preset pixel, and using the least squares method to estimate the coefficients using the values and coordinates of the pixels in the neighborhood;
[0033] Based on the original position and the moved position of the preset pixel, the constraint conditions are obtained;
[0034] The preset pixel displacement is optimized by constructing an objective function based on the pixel neighborhood.
[0035] According to the second aspect, a wake detection device based on information entropy is provided, comprising: a processing module for acquiring discrete water body data in the beam domain; and performing gridding mapping processing on the discrete water body data to obtain a sonar water body image;
[0036] An estimation module is used to perform optical flow estimation on at least two adjacent sonar water images in the sonar water image to obtain the corresponding optical flow field. The optical flow field includes the motion direction information and motion amplitude information of each pixel between the adjacent sonar water images. The distribution characteristics of the optical flow vector correspond to the motion characteristics of the wake bubble.
[0037] The information entropy calculation module is used to determine the direction angle and magnitude of the velocity vector based on the velocity vector of each pixel in the optical flow field between adjacent sonar image frames; divide the velocity vector direction into n directional regions; where n is an integer greater than 1; and calculate the information entropy of the velocity vector in each directional region.
[0038] The result acquisition module is used to obtain the wake detection result based on the information entropy.
[0039] In some possible embodiments, the information entropy calculation module is specifically used for:
[0040] Divide the velocity vector direction into n regions;
[0041] The probability distribution is obtained by summing the magnitudes of the velocity vectors in the same region and dividing by the sum of the magnitudes of the total velocity vectors. ;
[0042] The information entropy is calculated using the following formula;
[0043] .
[0044] According to a third aspect, a computer storage medium is provided, on which a computer program is stored, which, when executed by one or more processors, implements the information entropy-based wake detection method as described in any of the above embodiments.
[0045] According to a fourth aspect, an electronic device is provided, including a memory and one or more processors, wherein a computer program is stored on the memory, and the computer program, when executed by the one or more processors, implements any of the information entropy-based wake detection methods described in the above embodiments. Attached Figure Description
[0046] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0047] Figure 1 This is a schematic diagram of a wake detection method based on information entropy provided in an embodiment of this application;
[0048] Figure 2 This is another flowchart illustrating a wake detection method based on information entropy provided in an embodiment of this application;
[0049] Figure 3 This is a schematic diagram of the data information entropy processing result provided in the embodiments of this application;
[0050] Figure 4 This is a schematic diagram of a module for a wake detection method based on information entropy. Detailed Implementation
[0051] The solution provided in this specification will now be described with reference to the accompanying drawings.
[0052] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions in the embodiments of this application will be described below with reference to the accompanying drawings.
[0053] In the description of the embodiments of this application, the words "exemplary," "for example," or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplary," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the words "exemplary," "for example," or "for instance" is intended to present the relevant concepts in a specific manner.
[0054] In the description of the embodiments of this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, B existing alone, and A and B existing simultaneously. Furthermore, unless otherwise stated, the term "multiple" means two or more.
[0055] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and their variations all mean "including but not limited to," unless otherwise specifically emphasized.
[0056] Multibeam acoustic imaging is widely used in underwater seabed topography and sediment detection. Using multibeams for wake imaging can yield wide-coverage wake detection results. Conventional wake detection primarily utilizes amplitude information, detecting wakes by observing changes in amplitude. However, amplitude-based detection methods are unsuitable when wake echo intensity is low, especially in thin wakes, where amplitude detection methods risk failure, failing to accurately identify the presence and extent of the wake and thus failing to meet the wake detection requirements in complex underwater environments.
[0057] The wake echo is formed by the scattering of air bubbles, and the bubbles within the wake exhibit distinct motion characteristics—the wake bubbles are affected by buoyancy, viscous drag, and turbulent pulsation lift, exhibiting scale-dependent buoyancy motion, turbulent vortex coupled transport, and dynamic evolution of coalescence-breakup-dissolution. These motion characteristics are directly reflected in the time-domain variations of the sonar echo signal.
[0058] Ship wakes are groups of bubbles generated by the collision of propellers and hulls with the water. They are characterized by diffusion over time, slow time-varying properties, and continuous volume, making them a crucial feature of ships. Optics, acoustics, and magnetism are all effective physical methods for observing them. Among these, acoustic imaging detection offers wide detection range and long distance, making it of significant engineering value. Compared to optics, which acquires microscopic features such as bubble volume and density, acoustics primarily acquires macroscopic features such as morphology, volume, and thickness. Utilizing the characteristics of acoustic echoes for target detection has significant academic implications. Currently, there is no technology that utilizes the entropy change in sonar echo velocity caused by the movement of wake bubbles to achieve wake detection. This makes it difficult to fully leverage the motion characteristics of wake bubbles to improve detection accuracy and solves the detection challenges in scenarios with sparse wakes and weak echoes.
[0059] Therefore, this invention proposes a wake detection method based on information entropy, which utilizes the echo characteristics of wake bubbles to extract the signal characterization generated by the scattering bubble motion in the sound wave. Specifically, wake detection is achieved by utilizing the entropy change of the wake echo velocity.
[0060] Figure 1 This is a schematic diagram of a wake detection method based on information entropy. (Example) Figure 1 As shown in the figure, this application proposes a wake detection method based on information entropy, which specifically includes the following steps:
[0061] 110: Determine the grid range.
[0062] In this embodiment of the application, the acquired beam domain discrete water body data is gridded into a graph to form regular water body image intensity values.
[0063] Water body data gridding is the process of creating a water body raster image from discrete water body data points through grid interpolation. The specific process is as follows:
[0064] (1) Determine the grid range: In single-ping multibeam sonar water data, the imaging grid range of the water data is determined according to the beam opening angle range and the number of data sampling points. The grid intervals are calculated on the horizontal x-axis and the vertical y-axis, respectively. and .
[0065] (2) Set grid spacing: Set the grid spacing of the x-axis and y-axis according to the actual application requirements. and The number of pixels is calculated based on the grid spacing as follows:
[0066]
[0067] In the formula, `round` is the rounding function. The grid spacing determines the resolution of the sonar water image. If the grid spacing is too large, the water image resolution will be too low, leading to the mosaic phenomenon in the water image; if the grid spacing is too small, it will greatly increase the computational load of water image processing. Therefore, the grid spacing needs to be selected reasonably according to the actual application requirements.
[0068] (3) Calculate the grid position: Let the coordinates of the grid be... Then the position information of each grid in the Cartesian coordinate system is:
[0069]
[0070] In the formula, i and j represent the column and row of the grid, respectively. The coordinates of the grid center point can be expressed as:
[0071]
[0072] (4) Mapping grid data: The discrete water volume scattering intensity data is mapped to each grid to form regular water image intensity values. Each grid data corresponds to an intensity value, but the number of volume scattering intensity data mapped to each grid is different, so interpolation processing is required for the data in the grid.
[0073] 120: Dynamic brightness allocation
[0074] Meshization results in a small grayscale range and low contrast in sonar water images, leading to poor image quality. To address this, it's necessary to enhance sonar image contrast by expanding the grayscale range, thereby improving image clarity. Dynamic brightness allocation, through analysis of sonar image samples, dynamically optimizes the brightness range of the entire sonar image to improve contrast, ensuring image detail integrity while maintaining high computational speed.
[0075] Dynamic brightness allocation dynamically maps the intensity values of the water image after gridding. By optimizing the mapping function, the intensity values are mapped to a linear grayscale range of 0 to 255, making full use of the grayscale range and avoiding image saturation and noise amplification. The mapping function expression is as follows:
[0076]
[0077] In the formula, I is the mapped output value; The volume scattering intensity value of the water body data after gridding and mapping is denoted as ; H is the average value of the top 1% intensity values in the volume scattering intensity data. Setting H can reduce the influence of the mapping of discrete isolated peak values; L is the minimum value of the volume scattering intensity data. These are parameters that can be dynamically adjusted.
[0078] In this embodiment of the application, by analyzing sonar image samples, the grayscale range of the sonar image is enlarged, which can effectively enhance image contrast and suppress noise interference.
[0079] 130: Farneback optical flow method.
[0080] Ship wakes exist in the water as bubble clusters and dissipate slowly. This process causes variations in intensity distribution across multiple frames of sonar water images. For sonar image sequences where pixels vary over time and pixel values correlate between adjacent frames, optical flow can determine the motion information at each pixel location.
[0081] Farneback is a gradient-based method with two fundamental assumptions: first, that the image gradient is constant; and second, that the local optical flow is constant. Since sonar images are typically two-dimensional grayscale images, the grayscale values of pixels in a sonar image can be viewed as a function of a two-dimensional variable. . With the pixel of interest Construct a local coordinate system centered on the function. Expanding the binomial, we get:
[0082]
[0083] Here, x represents a two-dimensional column vector. ; use A to represent Matrix; denoted by b matrix; These are the binomial expansion coefficients. It's important to note that each pixel corresponds to a set of coefficients.
[0084] Then by pixel Centered on a square neighborhood of size 2n+1, the coefficients are estimated using the least squares method based on the values and coordinates of the pixels within the neighborhood. The distance from the center pixel... Pixels that are closer together have a greater correlation and therefore receive a larger weight; this process is called polynomial expansion. If the pixel moves by d in the next frame of the sonar image, the polynomial should change accordingly.
[0085] Pixels Original position
[0086]
[0087] After moving d, the position is
[0088]
[0089] in,
[0090]
[0091] like If the matrix is non-singular, then we can derive the following from the second equation above:
[0092]
[0093] However, in reality, this condition may not be met, so we need to approximate the average and let:
[0094]
[0095] The basic constraints can then be derived.
[0096]
[0097] Finally, the displacement is optimized by constructing an objective function based on the neighborhood of each pixel:
[0098]
[0099] In the formula, It is the weight function of the neighborhood I of the pixel.
[0100] 140: Solve for information entropy.
[0101] Divide the velocity vector direction into n regions. Calculate the information entropy of each region.
[0102] Dividing the velocity vector direction into n regions, the i-th region is: The distribution of each velocity vector in each region is statistically analyzed. The magnitudes of the velocity vectors in the same region are summed, and then divided by the sum of the magnitudes of all velocity vectors to obtain the probability distribution. That is:
[0103]
[0104] in, This represents the magnitude of the k-th velocity vector in the i-th region.
[0105] Therefore, the information entropy can be calculated using the following formula.
[0106]
[0107] Information entropy itself has the characteristic of resisting random noise, and can effectively distinguish the movement of wake bubbles from random disturbances in the water body (such as water flow and impurity movement), reducing the probability of false detection and missed detection. This application calculates information entropy by optical flow vector, and identifies the emergence, development and dissipation process of wake by the change law of information entropy, thus completing wake detection.
[0108] 150: Wake Flow Detection Judgment
[0109] Wakes are identified based on the characteristics of information entropy changes: When there is no wake, there is no obvious bubble movement in the water, the optical flow field is randomly distributed, and the information entropy value is high; when a wake appears, the bubbles exhibit regular movement (such as overall diffusion and directional buoyancy), the optical flow field distribution tends to be orderly, and the information entropy value decreases rapidly; when the wake dissipates, the bubbles gradually diffuse and dissolve, the disorder of the optical flow field recovers, and the information entropy value gradually rises back to the level when there is no wake. This pattern enables accurate detection, range positioning, and life cycle determination of wakes.
[0110] In summary, this embodiment utilizes the entropy change of the optical flow field induced by the movement of wake bubbles for detection. It is not limited by the intensity of the wake echo and can detect both high-concentration near-field wakes and accurately identify sparse far-field wakes, adapting to the detection needs of different sea states and wake conditions. It solves the problem that existing technologies based on sonar echo amplitude detection of wakes are prone to failure in scenarios with sparse wakes and low echo intensity.
[0111] Figure 2 This is another flowchart illustrating the wake detection method based on information entropy. For example... Figure 2 As shown, it includes the following steps:
[0112] 210: Acquire discrete water body data in the beam domain.
[0113] For example, in this embodiment of the application, a multibeam sonar is used to transmit sound waves underwater and receive the scattered echo signal of wake bubbles in the water body to obtain discrete water body data in the beam domain (such as core parameters including volume scattering intensity).
[0114] 220: The discrete water body data is processed into a grid to obtain a sonar water body image.
[0115] In one possible implementation, the discrete water body data is processed into a grid to obtain a sonar water body image, including: determining the imaging grid range of the sonar water body data based on the beam opening angle range and the number of data sampling points; determining the grid spacing according to preset conditions; calculating the position of each grid; and mapping the discrete water body volume scattering intensity data to each grid to obtain the sonar water body image after grid processing.
[0116] 230: Optical flow estimation is performed on at least two adjacent sonar water images in the sonar water image to obtain the corresponding optical flow field. The optical flow field includes the motion direction information and motion amplitude information of each pixel between the adjacent sonar water images. The distribution characteristics of the optical flow vector correspond to the motion characteristics of the wake bubble.
[0117] In one possible implementation, before performing optical flow estimation on at least two adjacent frames of the sonar water image, the method further includes dynamically allocating brightness to the sonar water image, including: dynamically mapping the intensity values of the sonar water image after gridding; and mapping the intensity values of the sonar water image to a linear grayscale range of 0 to 255 by optimizing the mapping function.
[0118] 240: Determine the direction angle and magnitude of the velocity vector based on the velocity vector of each pixel in the optical flow field between adjacent sonar image frames.
[0119] In one possible implementation, the range of vector direction angle values is divided into n direction regions, and then the velocity vector is assigned to the corresponding direction region based on the direction angle of each velocity vector.
[0120] 250: Divide the velocity vector direction into n directional regions; where n is an integer greater than 1.
[0121] 260: Calculate the information entropy of the velocity vector in each directional region.
[0122] In one possible implementation, calculating the information entropy of the velocity vectors in each directional region includes: dividing the velocity vector directions into n regions; summing the magnitudes of the velocity vectors in the same region and dividing by the sum of the magnitudes of the total velocity vectors to obtain the distribution probability. The information entropy is calculated using the following formula.
[0123]
[0124] 270: The wake detection result is obtained based on the information entropy.
[0125] In one possible implementation, identifying the wake based on the information entropy includes: identifying the wake according to the change characteristics of the information entropy; when the information entropy value is high, determining that there is no wake in the sonar image sequence to be detected, and outputting the wake detection result; when the information entropy value decreases rapidly, determining that a wake appears in the sonar image sequence to be detected, and outputting the wake detection result; when the information entropy value gradually rises, determining that the wake in the sonar image sequence to be detected has dissipated, and outputting the wake detection result.
[0126] In this embodiment, information entropy is combined with the motion characteristics of wake bubbles. The motion disorder of wake bubbles is quantified by the entropy variable of the optical flow field, which breaks through the limitation of the prior art that only uses the echo amplitude and provides a brand-new approach to wake detection.
[0127] To verify the feasibility and effectiveness of the wake detection method based on information entropy proposed in this invention, an actual sea trial was conducted. The method of this invention was used to process the wake generated by the test vessel. The specific experimental process and results are as follows:
[0128] (1) Experimental preparation: Select a ship (about 20m in length and about 10 knots in speed) and conduct the experiment in calm sea area (sea state 2, no obvious water flow interference); use multibeam sonar (beam opening angle 1°×1°) to collect wake echo data, with a detection distance of 3m-200m.
[0129] (2) Experimental process: The ship sailed at a constant speed, and the multibeam sonar continuously collected water echo data. A total of 2,000 frames of sonar images were collected. The actual data wake started to appear around frame 250 (when the ship sailed into the sonar detection range, the wake bubbles entered the detection area) and dissipated around frame 1,750 (when the wake bubbles gradually diffused and dissolved, exceeding the detection range or the concentration was lower than the detection threshold).
[0130] (3) Experimental results:
[0131] Figure 3 This is a schematic diagram of the data information entropy processing result provided in the embodiments of this application. For example... Figure 3 As shown, the data information entropy processing result (horizontal axis is frame number, vertical axis is information entropy value) is derived from... Figure 3 It can be seen that before the wake appears (frames 1-250), the information entropy value remains at a high level (approximately 2.6-2.8 bits). At this time, there are no wake bubbles in the water, and the optical flow field distribution is random with high disorder. After the wake appears (frames 250-300), the information entropy decreases rapidly, dropping below 2.0 bits. This is because the wake bubbles enter the detection area and exhibit regular movement, causing the optical flow field distribution to become more ordered and the disorder to decrease. During the period when the wake is stable (frames 300-1700), the information entropy remains at a low level (approximately 1.8-2.0 bits). At this time, the motion state of the wake bubbles is relatively stable, and the disorder of the optical flow field remains constant. As time progresses, the wake gradually dissipates (frames 1700-1750), and the information entropy gradually increases to the level of the information entropy when there is no wake (approximately 2.6 bits). At this time, the wake bubbles basically dissipate, and the optical flow field returns to a random distribution.
[0132] The decrease in information entropy is related to the area of the wake in the acoustic image. The larger the wake area, the more obvious the regularity of the wake bubble's movement, the lower the disorder of the optical flow field, and the greater the decrease in information entropy, i.e., the smaller the information entropy. Since the size of the wake rapidly increases to its maximum in the initial stage, but does not reach its maximum value instantaneously (the wake bubble needs a certain amount of time to diffuse and form a stable bubble cluster), the information entropy does not drop sharply after the wake appears, but rather shows a gradual decreasing trend, consistent with the experimental results.
[0133] (4) Experimental conclusion: This experiment verifies that the wake detection method based on information entropy is feasible and can accurately identify the emergence, stabilization and dissipation process of the wake. It has high detection accuracy and can still identify the wake by the change of information entropy in the wake sparse stage (1700-1750 frames, the wake area decreases and the concentration decreases). It solves the problem that the existing amplitude detection method fails in the wake sparse scenario and has good practicality and engineering application value.
[0134] Figure 4 This is a schematic diagram of a module for a wake detection method based on information entropy. (Example) Figure 4 As shown, a wake detection device 400 based on information entropy includes a processing module for acquiring discrete water body data in the beam domain; performing gridding mapping on the discrete water body data to obtain a sonar water body image; an estimation module for performing optical flow estimation on at least two adjacent sonar water body images in the sonar water body image to obtain a corresponding optical flow field, wherein the optical flow field includes motion direction information and motion amplitude information of each pixel point between the adjacent sonar water body images; the distribution characteristics of the optical flow vector correspond to the motion characteristics of the wake bubble; an information entropy calculation module for determining the direction angle and amplitude of the velocity vector based on the velocity vector of each pixel point in the optical flow field between adjacent sonar image frames; dividing the velocity vector direction into n directional regions; wherein n is an integer greater than 1; calculating the information entropy of the velocity vector in each directional region; and a result acquisition module for obtaining the wake detection result based on the information entropy.
[0135] According to another embodiment, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the processor executes the program, it implements the information entropy-based wake detection method as described in the above technical solution.
[0136] According to another embodiment, a computer-readable storage medium is also provided, on which a computer program is stored, which, when executed in a computer, causes the computer to perform a wake detection method based on information entropy.
[0137] Those skilled in the art will recognize that, in one or more of the examples above, the functions described in this invention can be implemented using hardware, software, firmware, or any combination thereof. When implemented in software, these functions can be stored in a computer-readable medium or transmitted as one or more instructions or code on a computer-readable medium.
[0138] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made on the basis of the technical solution of the present invention should be included within the scope of protection of the present invention.
Claims
1. A wake detection method based on information entropy, characterized in that, Includes the following steps: Acquire discrete water body data in the beam domain; The discrete water body data is gridded to obtain a sonar water body image; Optical flow estimation is performed on at least two adjacent sonar water images to obtain the corresponding optical flow field. The optical flow field includes the motion direction information and motion amplitude information of each pixel between the adjacent sonar water images. The distribution characteristics of the optical flow vector correspond to the motion characteristics of the wake bubble. The direction angle and magnitude of the velocity vector are determined based on the velocity vector of each pixel in the optical flow field between adjacent sonar image frames; The velocity vector direction is divided into n directional regions; where n is an integer greater than 1. Calculate the information entropy of the velocity vector in each directional region; The wake detection result is obtained based on the information entropy.
2. The method according to claim 1, characterized in that, The process of gridding the discrete water body data to obtain a sonar water image includes: The range of the sonar water body data imaging grid is determined based on the beam opening angle range and the number of data sampling points; Determine the grid spacing according to preset conditions; Calculate the position of each grid cell; The discrete water volume scattering intensity data is mapped to each grid to obtain a sonar water image after gridding.
3. The method according to any one of claims 1 or 2, characterized in that, Before performing optical flow estimation on at least two adjacent frames of the sonar water imagery, the method further includes dynamically assigning brightness to the sonar water imagery, including: Dynamic mapping processing is performed on the intensity values of sonar water images after gridding and mapping. The intensity values of the sonar water image are mapped to a linear grayscale range of 0 to 255 by optimizing the mapping function.
4. The method according to any one of claims 1 or 3, characterized in that, The calculation of the information entropy of the velocity vector in each directional region includes: Divide the velocity vector direction into n regions; The probability distribution is obtained by summing the magnitudes of the velocity vectors in the same region and dividing by the sum of the magnitudes of the total velocity vectors. ; The information entropy is calculated using the following formula; 。 5. The method according to any one of claims 1 or 4, characterized in that, The method of identifying the wake based on the information entropy includes: Wake flows are identified based on the characteristics of information entropy changes. When the information entropy value is high, it is determined that there is no wake flow in the sonar image sequence to be detected, and the wake flow detection result is output. When the information entropy value decreases rapidly, it is determined that a wake appears in the sonar image sequence to be detected, and the wake detection result is output. As the information entropy value gradually increases, it is determined that the wake dissipates in the sonar image sequence to be detected, and the wake detection result is output.
6. The method according to any one of claims 1 or 5, characterized in that, Optical flow estimation is performed on at least two adjacent frames of sonar water images, including: Treating the grayscale values of pixels in a sonar image as a function of two-dimensional variables ; Construct a local coordinate system centered on a preset pixel; Functions with respect to two-dimensional variables Perform a binomial expansion; the binomial expansion includes: selecting a square neighborhood of size 2n+1 centered on the preset pixel, and using the least squares method to estimate the coefficients using the values and coordinates of the pixels in the neighborhood; Based on the original position and the moved position of the preset pixel, the constraint conditions are obtained; The preset pixel displacement is optimized by constructing an objective function based on the pixel neighborhood.
7. A wake detection device based on information entropy, characterized in that, include: The processing module is used to acquire discrete water body data in the beam domain; The discrete water body data is gridded to obtain a sonar water body image; An estimation module is used to perform optical flow estimation on at least two adjacent sonar water images in the sonar water image to obtain the corresponding optical flow field. The optical flow field includes the motion direction information and motion amplitude information of each pixel between the adjacent sonar water images. The distribution characteristics of the optical flow vector correspond to the motion characteristics of the wake bubble. The information entropy calculation module is used to determine the direction angle and magnitude of the velocity vector based on the velocity vector of each pixel in the optical flow field between adjacent sonar image frames; divide the velocity vector direction into n directional regions; where n is an integer greater than 1; and calculate the information entropy of the velocity vector in each directional region. The result acquisition module is used to obtain the wake detection result based on the information entropy.
8. The apparatus according to claim 7, characterized in that, The information entropy calculation module is specifically used for: Divide the velocity vector direction into n regions; The probability distribution is obtained by summing the magnitudes of the velocity vectors in the same region and dividing by the sum of the magnitudes of the total velocity vectors. ; The information entropy is calculated using the following formula; 。 9. An electronic device, characterized in that, The device includes a memory and one or more processors, wherein the memory stores a computer program that, when executed by the one or more processors, implements a wake detection method based on information entropy as described in any one of claims 1 to 6.
10. A computer storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by one or more processors, implements a wake detection method based on information entropy as described in any one of claims 1 to 6.