Sea wave time-frequency domain observation method based on binocular vision

Abstract

A sea wave time-frequency domain observation method based on binocular vision comprises the steps that a double-camera system is installed at two preset positions, and it is ensured that the double-camera system is aligned with the same sea surface area to synchronously observe sea waves; using a feature point matching technology to complete joint calibration of external parameters according to internal parameters of the camera; the image is filtered and corrected, noise is removed, and motion compensation is carried out according to needs; extracting three-dimensional point cloud data through a stereo matching algorithm, and constructing a three-dimensional form of sea waves; performing time-frequency domain analysis on the sea surface data, extracting sea wave parameters and performing time domain decomposition to comprehensively observe the dynamic characteristics of sea waves. The method overcomes the limitations of the traditional observation means, such as long laying time of the wave buoy, insufficient flexibility, high cost of the X-band wave radar, incapability of accurately obtaining the sea surface waveform and the like, provides technical support for ocean engineering and sea condition early warning, and has remarkable economic benefits and application prospects.

Description

Technical Field

[0001] The present invention relates to the fields of computer vision and ocean observation, and in particular to a method for observing ocean waves in time and frequency domains based on binocular vision. Background Art

[0002] As an important element of the ocean dynamic environment, waves are a complex and random natural phenomenon occurring on the ocean surface. Wave observation is crucial to improving navigation safety, supporting climate research and guiding marine engineering. In this context, in-situ real-time wave information is particularly important, and accurate and reliable real-time sea state data is a key factor for efficient, economical and safe marine operations. Although traditional observation methods such as meteorological satellites and weather balloons can conduct remote sensing monitoring of large areas of sea, their resolution and update frequency are low. Wave buoys are an effective means of monitoring wave changes, but their deployment time is long and their flexibility is insufficient. Shipborne wave radars are usually X-band, which cannot accurately obtain sea surface waveforms, are expensive, and require frequent debugging. With the development trend of large-scale, complex and refined marine operations, traditional observation methods are increasingly unable to meet the needs of safe and efficient operations, bringing huge risks to marine operations with growing costs and scale.

[0003] It should be noted that the information disclosed in the above background technology section is only used for understanding the background of the present application, and therefore may include information that does not constitute prior art known to ordinary technicians in the field. Summary of the invention

[0004] The main purpose of the present invention is to overcome the defects existing in the above-mentioned background technology and provide a method for observing ocean waves in time and frequency domain based on binocular vision.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] A method for observing ocean waves in time and frequency domain based on binocular vision comprises the following steps:

[0007] S1: A dual-camera system is installed at two preset fixed positions, with the dual cameras aimed at the same sea surface area to achieve synchronous observation of wave morphology;

[0008] S2: Based on the determined camera internal parameters, the relative position and posture between the two cameras are calculated using feature point matching technology to complete the joint calibration of external parameters;

[0009] S3: filtering and correcting the collected images, and removing image noise and interference through filtering technology;

[0010] S4: Calculate the disparity map using the matching feature points in the binocular camera system, and convert it into three-dimensional coordinates based on the disparity information. By fusing the point cloud data of multiple viewpoints, construct a sea surface model to reflect the dynamic changes and morphological characteristics of the waves;

[0011] S5: Perform time-frequency domain analysis on the sea surface data collected for a predetermined time period, extract parameters such as frequency, amplitude and phase of the waves, decompose the wave signals in the time domain, identify wave components of different frequencies and amplitudes, and determine the dynamic characteristics of the waves.

[0012] Furthermore, in step S1, the camera is arranged on the shore, offshore platform or moving floating structure; when the camera is arranged on the moving floating structure, the motion state information of the camera is collected by the GNSS / IMU motion acquisition system while observing the wave shape, and in S3, a motion compensation algorithm is introduced to adjust the motion compensation parameters in the image in real time to ensure the accuracy and consistency of the image data

[0013] Furthermore, step S2 specifically includes:

[0014] Determine the internal parameters of the camera, including focal length, principal point coordinates, etc.;

[0015] Identify and extract feature points in images captured by dual cameras;

[0016] Find corresponding feature point pairs in two images;

[0017] Using the matched feature points, the relative position and posture between the two cameras are calculated;

[0018] By minimizing the reprojection error, the camera's internal and external parameters are optimized to ensure the accuracy of 3D reconstruction;

[0019] Integrate internal and external parameters to complete accurate camera calibration.

[0020] Furthermore, step S4 specifically includes:

[0021] Generate a disparity map using matching feature points in a binocular camera system;

[0022] Convert disparity information into 3D coordinates by fusing point cloud data from multiple viewpoints;

[0023] Construct an accurate sea surface model to reflect the dynamic changes and morphological characteristics of waves;

[0024] Establish a cost function to quantify the matching quality in image coordinates and depth;

[0025] Determine the optimal depth estimate by minimizing the cost function to find the best depth value;

[0026] Evaluate the matching quality between two point sets to ensure the accuracy and consistency of 3D reconstruction by minimizing the point set matching cost.

[0027] Furthermore, step S5 specifically includes:

[0028] Collect and analyze sea surface data for a certain period of time;

[0029] The time-frequency analysis method is applied to extract the key parameters of the ocean waves, including frequency, amplitude and phase, among which the inter-frame power spectral density (PSD) is used to estimate the wave spectral density;

[0030] Calculate ocean wave parameters such as significant wave height and meaningful wave height;

[0031] Decompose the wave signal in the time domain to identify wave components of different frequencies and amplitudes;

[0032] Assess the impact of waves on floating bodies.

[0033] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the binocular vision-based ocean wave time-frequency domain observation method.

[0034] A computer program product comprises a computer program, which implements the method when executed by a processor.

[0035] In some embodiments, the binocular vision-based ocean wave time-frequency domain observation method of the present invention comprises the following steps:

[0036] Step 1: Select two separate fixed positions to install the cameras, and ensure that the baseline distance between the cameras is appropriate to obtain sufficient parallax information. The cameras should be aimed at the same sea surface area, kept horizontal or slightly tilted downward to avoid interference from the sky or excessive background.

[0037] Step 2: Perform joint calibration of external parameters based on the determined internal parameters. By extracting matching feature points in the same scene, the relative position and posture between the two cameras are calculated. The entire calibration process can be implemented by automated software, covering feature point extraction, matching, and optimization of internal and external parameters, automatically completing accurate calibration of the camera.

[0038] Step 3: Filter and correct the collected images, and remove noise and unnecessary interference by applying appropriate filtering techniques to improve image quality. If the observation system is arranged on a moving floating structure, a compensation algorithm can be introduced according to the motion signal to adjust the motion compensation parameters in the image in real time to ensure the accuracy and consistency of the final image. This process provides high-quality basic data for subsequent three-dimensional reconstruction and time-frequency domain analysis. Through the GNSS / IMU motion acquisition system, the six-degree-of-freedom real-time motion of the floating body is introduced into the binocular acquisition system for motion compensation, thereby realizing dynamic observation of the target sea area.

[0039] Step 4: Calculate the disparity map using the matching feature points in the binocular camera system, and then convert it into three-dimensional coordinates based on the disparity information. By fusing the point cloud data from multiple viewpoints, an accurate sea surface model is constructed to fully reflect the dynamic changes and morphological characteristics of the waves.

[0040] Step 5: Collect sea surface data for a certain period of time. Through time-frequency analysis methods, we can extract information such as the frequency, amplitude, and phase of the waves, deeply understand its dynamic characteristics, and calculate wave parameters such as effective wave height. By decomposing the wave signal in the time domain, we can identify wave components of different frequencies and amplitudes, thereby gaining a deeper understanding of the impact of waves on floating bodies. The wave parameter estimates obtained through time-frequency analysis include key indicators such as effective wave height, peak wavelength, and wave direction. These parameters, as frequency domain statistical indicators, can be used to conduct in-depth analysis of the evolution of the sea surface within a certain period of time. At the same time, time domain analysis involves the decomposition of wave signals to further understand and describe the dynamic characteristics of waves.

[0041] The present invention has the following beneficial effects:

[0042] The present invention proposes a method for observing ocean waves in time and frequency domain based on binocular vision. The method synchronously observes the ocean wave morphology through a dual camera system, and uses feature point matching technology to complete the accurate calibration of the internal and external parameters of the camera. The method extracts three-dimensional point cloud data through a stereo matching algorithm, reconstructs the sea surface, generates the three-dimensional morphology of the ocean wave, performs time and frequency domain analysis on the reconstructed sea surface data, calculates the sea state parameters, and performs time domain wave decomposition, thereby realizing comprehensive observation and analysis of the ocean wave motion characteristics. The method not only improves the accuracy and real-time performance of ocean wave observation, but also reduces the observation cost and enhances flexibility and adaptability. The binocular vision technology obtains depth information by calculating parallax, so that the system can generate a high-resolution three-dimensional dynamic model of the ocean wave, which better reflects the instantaneous changes of the ocean wave. Compared with traditional wave buoys and X-band wave radars, the method has higher flexibility and adaptability, can obtain higher quality point cloud data, and realize more accurate ocean wave reconstruction. In addition, the system is easy to integrate and has low hardware cost, which can greatly reduce the equipment investment and maintenance cost in offshore operations and improve economic benefits. The binocular vision system also has the ability to adjust under different marine environments and operational requirements to meet the increasingly complex needs of modern marine operations. By processing and analyzing data in real time, this method improves the accuracy and efficiency of observation results, provides reliable technical support for marine engineering and sea condition warning, and promotes the further development of marine research and applications.

[0043] The embodiments of the present invention also have the following significant advantages:

[0044] (1) Improve the accuracy and real-time performance of wave observation: By using binocular vision technology to calculate depth information, a high-resolution three-dimensional dynamic model of waves can be generated. This precise data can better reflect the instantaneous changes of waves, improving the timeliness and accuracy of observation.

[0045] (2) Reduce observation costs: Compared with traditional observation methods, the hardware cost of the binocular vision system is lower and easier to integrate. This means that equipment investment and maintenance costs can be greatly reduced in offshore operations, thereby improving economic benefits.

[0046] (3) Enhanced flexibility and adaptability: Compared with fixed observation equipment, binocular vision systems have higher flexibility and can be adjusted and applied in different marine environments and operational requirements. This flexibility can meet the increasingly complex needs of modern marine operations.

[0047] Other beneficial effects of the embodiments of the present invention will be further described below. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 The present invention is a flowchart of a method for observing ocean waves in time and frequency domains based on binocular vision.

[0049] Figure 2 Schematic diagram of the binocular system arrangement according to an embodiment of the present invention.

[0050] Figure 3 Schematic diagram of the coordinate transformation process of the binocular system according to an embodiment of the present invention.

[0051] Figure 4 Schematic diagram of the ocean wave spectrum observed according to an embodiment of the present invention. DETAILED DESCRIPTION

[0052] The following is a detailed description of the embodiments of the present invention. It should be emphasized that the following description is only exemplary and is not intended to limit the scope and application of the present invention.

[0053] The present invention proposes a method for observing ocean waves in the time and frequency domain based on binocular vision, which mainly includes: (1) arranging a dual camera system to collect sea surface images; (2) combining the internal and external parameters of the camera to automatically extract the feature points of the shooting content and perform joint calibration to ensure the accurate calibration of the camera; (3) filtering and correcting the collected images, and correcting the image deviation caused by the motion according to the motion compensation algorithm of the floating structure; (4) extracting three-dimensional point cloud data through a stereo matching algorithm, reconstructing the sea surface, and generating the three-dimensional shape of the ocean waves; (5) performing time-frequency domain analysis on the reconstructed sea surface data, calculating the sea state parameters, and performing time-domain wave decomposition, thereby realizing comprehensive observation and analysis of the motion characteristics of the ocean waves. Non-contact ocean wave observation based on binocular vision has the advantage of low cost, and through accurate time-frequency domain analysis and three-dimensional reconstruction, it can comprehensively observe and quantify the dynamic characteristics of the ocean waves, providing reliable technical support for marine engineering and sea state warning.

[0054] See also Figure 1 The embodiment of the present invention provides a method for observing ocean waves in time and frequency domains based on binocular vision, comprising the following steps:

[0055] Step S1: Install the dual camera system at two preset fixed positions, ensure that the baseline distance between the cameras is appropriate to obtain sufficient parallax information, and aim the cameras at the same sea surface area to achieve synchronous observation of the wave form. The dual camera system can be arranged on the shore, offshore platform or moving floating structure. When the camera is arranged on a moving floating structure, the motion state information of the camera is collected through the GNSS / IMU motion acquisition system while observing the wave form.

[0056] Step S2: Based on the determined camera internal parameters, the relative position and posture between the two cameras are calculated using feature point matching technology to complete the joint calibration of the external parameters.

[0057] Step S2 specifically includes: determining the internal parameters of the camera, including focal length, principal point coordinates, etc.; identifying and extracting feature points in the images captured by the dual cameras; finding corresponding feature point pairs in the two images; using the matched feature points to calculate the relative position and posture between the two cameras; optimizing the internal and external parameters of the camera by minimizing the reprojection error to ensure the accuracy of 3D reconstruction; integrating the internal and external parameters to complete the precise calibration of the camera.

[0058] Step S3: Filter and correct the collected images, and use filtering technology to remove image noise and interference. When the camera is placed on a moving floating structure, a motion compensation algorithm is also introduced to adjust the motion compensation parameters in the image in real time to ensure the accuracy and consistency of the image data.

[0059] Step S4: Calculate the disparity map using the matching feature points in the binocular camera system, and convert it into three-dimensional coordinates based on the disparity information. By fusing the point cloud data of multiple viewpoints, construct a sea surface model to reflect the dynamic changes and morphological characteristics of the waves.

[0060] In a preferred embodiment, step S4 specifically includes: generating a disparity map using matching feature points in a binocular camera system; converting disparity information into three-dimensional coordinates by fusing point cloud data from multiple viewpoints; constructing an accurate sea surface model to reflect the dynamic changes and morphological characteristics of waves; establishing a cost function to quantify the matching quality under image coordinates and depth; determining the optimal depth estimate by minimizing the cost function to find the best depth value; evaluating the matching quality between two point sets to ensure the accuracy and consistency of three-dimensional reconstruction by minimizing the point set matching cost.

[0061] Step S5: Perform time-frequency domain analysis on the sea surface data collected for a certain period of time, extract parameters such as frequency, amplitude and phase of the waves, decompose the wave signal in the time domain, identify wave components of different frequencies and amplitudes, and determine the dynamic characteristics of the waves.

[0062] Specifically, sea surface data for a certain period of time is collected and analyzed, and the key parameters of waves, including frequency, amplitude and phase, are extracted using time-frequency analysis methods, where the spectral density can be estimated using the inter-frame power spectral density (PSD); wave parameters, such as effective wave height and significant wave height, are calculated; and wave signals are decomposed in the time domain to identify wave components of different frequencies and amplitudes, so as to evaluate the impact of waves on floating bodies.

[0063] Compared with traditional methods such as wave buoy methods, the method of the present invention has higher flexibility and adaptability in observing the dynamic characteristics of waves. At the same time, compared with the commonly used X-band wave radar, the use of binocular vision technology can obtain higher quality point cloud data, thereby achieving more accurate wave reconstruction. It can not only be applied to shore and offshore platforms, but also integrate motion sensors and compensation algorithms for use on moving floating structures. Through real-time data processing and analysis, the accuracy and efficiency of observation results are improved. The method is low-cost and simple to arrange, and is easy to implement.

[0064] The following further describes specific embodiments of the present invention and examples of algorithm implementation thereof.

[0065] A method for observing ocean waves in time and frequency domain based on binocular vision comprises the following steps:

[0066] Step 1: Select two separate fixed positions to install the cameras, and ensure that the baseline distance between the cameras is appropriate to obtain sufficient parallax information. The cameras should be aimed at the same sea surface area, kept horizontal or slightly tilted downward to avoid interference from the sky or excessive background.

[0067] Binocular vision uses visual difference to establish depth relationship. The specific relationship is as follows

[0068]

[0069] Formula (1) describes how the three-dimensional coordinates are projected onto the two-dimensional image plane (such as Figure 3 ), u and v represent the horizontal and vertical pixel coordinates on the image plane, respectively; x and y are the coordinates in the three-dimensional world coordinate system. x and d y are the x-axis and y-axis scaling factors related to the camera intrinsic parameters, while u0 and v0 are the horizontal and vertical center point coordinates of the image coordinate system. The homogeneous form of the coordinates and the matrix are expressed as formula (2).

[0070]

[0071] By introducing the wave coordinate system, a stable reference benchmark is provided for binocular vision. The camera calibration is performed using the conversion relationship between the wave coordinate system and the world coordinate system and the image coordinate system. During the calibration process, the three-dimensional points on the wave surface are converted from the wave coordinate system to the image coordinate system, so as to more accurately calculate the internal and external parameters of the camera.

[0072] Step 2: Perform joint calibration of external parameters based on the determined internal parameters. By extracting matching feature points in the same scene, the relative position and posture between the two cameras are calculated. The entire calibration process can be implemented by automated software, covering feature point extraction, matching, and optimization of internal and external parameters, automatically completing accurate calibration of the camera.

[0073] Calibration involves mapping from a 3D world coordinate system to a 2D image coordinate system. Homogeneous coordinates can simplify calculations by introducing an extra dimension, combining multiple transformations such as translation, rotation, and scaling into a single matrix multiplication.

[0074]

[0075] Formula (3) can represent a homogeneous coordinate transformation process, where (X, Y, Z) is a point in the world coordinate system and (x', y') is a projection in the image coordinate system. K is the intrinsic parameter matrix, R is the rotation matrix, and t is the translation vector. Reprojection error is a commonly used metric in camera calibration and stereo vision to evaluate the difference between the projection of a 3D point and the actual observed image point. It reflects the accuracy of the calibration model and the quality of 3D reconstruction, as shown in Formula (5).

[0076]

[0077] Where p i is the point observed in the actual image, is the point calculated by reprojecting the 3D point according to the camera model, and N is the number of points. The reprojection error can be used to evaluate the accuracy of the camera calibration. If the error is small, it means that the calibration is more accurate and the model can better reflect the real projection characteristics of the camera. During the calibration process, by minimizing the reprojection error, the internal and external parameters of the camera can be optimized to ensure that the reconstructed 3D scene is as consistent as possible with the observed image.

[0078] Step 3: Filter and correct the collected images, and remove noise and unnecessary interference by applying appropriate filtering techniques to improve image quality. If the observation system is arranged on a moving floating structure, a compensation algorithm can be introduced according to the motion signal to adjust the motion compensation parameters in the image in real time to ensure the accuracy and consistency of the final image. This process provides high-quality basic data for subsequent 3D reconstruction and time-frequency domain analysis.

[0079] Step 4: Calculate the disparity map using the matching feature points in the binocular camera system, and then convert it into three-dimensional coordinates based on the disparity information. By fusing the point cloud data from multiple viewpoints, an accurate sea surface model is constructed to fully reflect the dynamic changes and morphological characteristics of the waves. This process includes:

[0080] A cost function is constructed to quantify the quality of the match given image coordinates (x, y) and depth d.

[0081] C(x,y,d)=w(x,y,d)*C0(x,y,d) (6)

[0082] dp =arg minC(x,y,d)(d∈D) (7)

[0083] C0 is the base cost, which represents the matching error without weight adjustment, and w(x, y, d) is the weight function used to adjust the influence of different depths d. In depth estimation, the goal is to find the best depth value by minimizing the cost function. The optimal depth estimation process is shown in Equation (7), which is to find the depth that minimizes the cost function C in the candidate depth set D.

[0084]

[0085] Formula (8) is used to evaluate the matching quality between two point sets P and Q'. i and the corresponding point q mi The squared distances between the two are summed up to get the total cost, and the best match is found by minimizing this cost. Then, by minimizing the point set matching cost, the error between the reconstructed 3D points and the actual observed image is minimized, and accurate and consistent results are obtained in image analysis and 3D reconstruction.

[0086] Step 5: Collect sea surface data for a certain period of time. Through time-frequency analysis methods, we can extract information such as the frequency, amplitude, and phase of the waves, gain a deeper understanding of their dynamic characteristics, and calculate wave parameters such as effective wave height. By decomposing the wave signal in the time domain, we can identify wave components of different frequencies and amplitudes, thereby gaining a deeper understanding of the impact of waves on floating bodies.

[0087] The spectral density estimation is usually estimated using the inter-frame power spectral density (PSD) (e.g. Figure 4 ):

[0088]

[0089] In formula (9), T is the observation time window, h(n) is the time series data of sea surface height, and N is the number of data points. s It is an important parameter of the wave spectrum and can be calculated by the following formula:

[0090]

[0091] In summary, the time-frequency domain observation method of ocean waves based on binocular vision of the present invention synchronously observes the ocean wave morphology through a dual camera system, and uses feature point matching technology to complete the precise calibration of the internal and external parameters of the camera, extracts three-dimensional point cloud data through a stereo matching algorithm, reconstructs the sea surface, generates the three-dimensional morphology of the ocean wave, performs time-frequency domain analysis on the reconstructed sea surface data, calculates the sea condition parameters, and performs time-domain wave decomposition, thereby realizing comprehensive observation and analysis of the motion characteristics of the ocean wave. This method not only improves the accuracy and real-time performance of ocean wave observation, but also reduces the observation cost and enhances flexibility and adaptability. The binocular vision system calculates depth information through the parallax of two cameras, and has the advantages of low hardware cost, high processing efficiency and easy system integration. The present invention uses binocular vision technology for real-time ocean observation, and can reconstruct the three-dimensional dynamic model of real-time ocean waves through point clouds, and then analyze its time-domain dynamic characteristics and frequency-domain statistical parameters. This method significantly improves the accuracy and real-time nature of wave information acquisition, overcomes the limitations of traditional observation methods, such as the long deployment time and lack of flexibility of wave buoys, the high cost of X-band wave radars, and the inability to accurately obtain sea surface waveforms. It provides strong technical support for marine engineering and sea condition warnings, ensures the safety of various marine operations and navigation, and has significant economic benefits and application prospects.

[0092] An embodiment of the present invention further provides a storage medium for storing a computer program, which at least performs the above method when executed.

[0093] An embodiment of the present invention further provides a control device, comprising a processor and a storage medium for storing a computer program; wherein the processor is configured to execute at least the method described above when executing the computer program.

[0094] An embodiment of the present invention further provides a processor, wherein the processor executes a computer program and at least executes the method described above.

[0095] The storage medium can be implemented by any type of non-volatile storage device, or a combination thereof. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic random access memory (FRAM), a ferromagnetic random access memory (FRAM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM); the magnetic surface memory can be a disk memory or a tape memory. The storage medium described in the embodiments of the present invention is intended to include, but is not limited to, these and any other suitable types of memory.

[0096] In the several embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0097] The units described above 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 distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0098] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0099] Those skilled in the art can understand that: all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, etc. Various media that can store program codes.

[0100] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0101] The methods disclosed in the several method embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new method embodiments.

[0102] The features disclosed in several product embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new product embodiments.

[0103] The features disclosed in several method or device embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new method embodiments or device embodiments.

[0104] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art of the present invention, several equivalent substitutions or obvious variations can be made without departing from the concept of the present invention, and the performance or use is the same, which should be regarded as belonging to the protection scope of the present invention.

Claims

1. A method for observing ocean waves in time and frequency domain based on binocular vision, characterized in that: The following steps are involved: S1: A dual-camera system is installed at two preset fixed positions, with the dual cameras aimed at the same sea surface area to achieve synchronous observation of wave morphology; S2: Based on the determined camera internal parameters, the relative position and posture between the two cameras are calculated using feature point matching technology to complete the joint calibration of external parameters; S3: filtering and correcting the collected images, and removing image noise and interference through filtering technology; S4: Calculate the disparity map using the matching feature points in the binocular camera system, and convert it into three-dimensional coordinates based on the disparity information. By fusing the point cloud data of multiple viewpoints, construct a sea surface model to reflect the dynamic changes and morphological characteristics of the waves; S5: Perform time-frequency domain analysis on the sea surface data collected for a predetermined time period, extract parameters such as frequency, amplitude and phase of the waves, decompose the wave signals in the time domain, identify wave components of different frequencies and amplitudes, and determine the dynamic characteristics of the waves.

2. The method for observing ocean waves in time and frequency domain based on binocular vision according to claim 1, characterized in that: In step S1, the camera is arranged on the shore, offshore platform or moving floating structure; when the camera is arranged on the moving floating structure, the motion state information of the camera is collected through the GNSS / IMU motion acquisition system while observing the wave shape, and in S3, a motion compensation algorithm is introduced to adjust the motion compensation parameters in the image in real time to ensure the accuracy and consistency of the image data.

3. The method for observing ocean waves in time and frequency domain based on binocular vision according to claim 1 or 2, characterized in that: Step S2 specifically includes: Determine the internal parameters of the camera, including focal length, principal point coordinates, etc.; Identify and extract feature points in images captured by dual cameras; Find corresponding feature point pairs in two images; Using the matched feature points, the relative position and posture between the two cameras are calculated; By minimizing the reprojection error, the camera's internal and external parameters are optimized to ensure the accuracy of 3D reconstruction; Integrate internal and external parameters to complete accurate camera calibration.

4. The method for observing ocean waves in time and frequency domain based on binocular vision according to any one of claims 1 to 3, characterized in that: Step S4 specifically includes: Generate a disparity map using matching feature points in a binocular camera system; Convert disparity information into 3D coordinates by fusing point cloud data from multiple viewpoints; Construct an accurate sea surface model to reflect the dynamic changes and morphological characteristics of waves; Establish a cost function to quantify the matching quality in image coordinates and depth; Determine the optimal depth estimate by minimizing the cost function to find the best depth value; Evaluate the matching quality between two point sets to ensure the accuracy and consistency of 3D reconstruction by minimizing the point set matching cost.

5. The method for observing ocean waves in time and frequency domain based on binocular vision according to any one of claims 1 to 4, characterized in that: Step S5 specifically includes: Collect and analyze sea surface data for a certain period of time; The time-frequency analysis method is applied to extract the key parameters of the ocean waves, including frequency, amplitude and phase, among which the inter-frame power spectral density (PSD) is used to estimate the wave spectral density; Calculate ocean wave parameters such as significant wave height and meaningful wave height; Decompose the wave signal in the time domain to identify wave components of different frequencies and amplitudes; Assess the impact of waves on floating bodies.

6. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for observing ocean waves in time and frequency domains using binocular vision as described in any one of claims 1 to 5 is implemented.

7. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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