A Time-Frequency Domain Observation Method for Ocean Waves Based on Binocular Vision

Abstract

A binocular vision-based time-frequency domain observation method for ocean waves includes: installing a dual-camera system at two preset locations, ensuring it is aligned with the same sea surface area for synchronous wave observation; using feature point matching technology to jointly calibrate extrinsic parameters based on camera intrinsic parameters; filtering and correcting the images to remove noise and perform motion compensation as needed; extracting three-dimensional point cloud data using a stereo matching algorithm to construct the three-dimensional morphology of the waves; and performing time-frequency domain analysis on the sea surface data to extract wave parameters and perform time-domain decomposition to comprehensively observe the dynamic characteristics of the waves. This method overcomes the limitations of traditional observation methods, such as the long deployment time and lack of flexibility of wave buoys, and the high cost and inability to accurately acquire sea surface waveforms of X-band wave radar. It provides technical support for marine engineering and sea condition early warning, and has significant economic benefits and application prospects.

Description

Technical Field

[0001] This invention relates to the fields of computer vision and ocean observation, and in particular to a time-frequency domain observation method for ocean waves based on binocular vision. Background Technology

[0002] Ocean waves, as a crucial element of the marine dynamic environment, are a complex and random natural phenomenon occurring on the ocean surface. Ocean wave observation is vital for improving navigation safety, supporting climate research, and guiding marine engineering. In this context, in-situ real-time ocean wave information is particularly important; accurate and reliable real-time sea state data is a key factor for efficient, economical, and safe maritime operations. Traditional observation methods, such as meteorological satellites and weather balloons, can remotely monitor large areas of the ocean, but 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 typically X-band, unable to accurately acquire sea surface waveforms, and are costly and require frequent adjustments. With the trend towards larger, more complex, and more sophisticated maritime operations, traditional observation methods are increasingly unable to meet the demands for safe and efficient operations, posing significant risks to the ever-increasing costs and scale of maritime operations.

[0003] It should be noted that the information disclosed in the background section above is only for understanding the background of this application, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0004] The main objective of this invention is to overcome the deficiencies in the aforementioned background technology and provide a time-frequency domain observation method for ocean waves based on binocular vision.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A time-frequency domain observation method for ocean waves based on binocular vision includes the following steps:

[0007] S1: Install a dual-camera system at two preset fixed locations, with both cameras aimed at the same sea surface area, to achieve synchronous observation of wave patterns;

[0008] S2: Based on the determined camera intrinsic parameters, use feature point matching technology to calculate the relative position and attitude between the two cameras, and complete the joint calibration of the extrinsic parameters;

[0009] S3: Filter and correct the acquired image to remove image noise and interference through filtering techniques;

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

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

[0012] Furthermore, in step S1, the camera is deployed on the shore, an offshore platform, or a moving floating structure. When the camera is deployed on a moving floating structure, the motion status information of the camera is acquired through a GNSS / IMU motion acquisition system while observing the wave morphology. In step S3, a motion compensation algorithm is introduced to adjust the motion compensation parameters in the image in real time, ensuring the accuracy and consistency of the image data.

[0013] Furthermore, step S2 specifically includes:

[0014] Determine the camera's internal parameters, including focal length and principal point coordinates;

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

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

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

[0018] By minimizing reprojection errors and optimizing the camera's intrinsic and extrinsic parameters, the accuracy of 3D reconstruction can be ensured.

[0019] By integrating internal and external parameters, the camera can be accurately calibrated.

[0020] Furthermore, step S4 specifically includes:

[0021] Disparity maps are generated using matching feature points in a binocular camera system;

[0022] Parallax information is converted into three-dimensional 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 ocean waves;

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

[0025] By minimizing the cost function, the optimal depth estimate is determined, thereby finding the best depth value;

[0026] Evaluate the matching quality between two point sets and 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 over a certain period of time;

[0029] Key parameters of ocean waves, including frequency, amplitude, and phase, are extracted using time-frequency analysis methods, with the spectral density estimated using inter-frame power spectral density (PSD).

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

[0031] By decomposing ocean wave signals in the time domain, wave components of different frequencies and amplitudes can be identified.

[0032] Assess the impact of ocean waves on the floating body.

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

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

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

[0036] Step 1: Select two fixed locations that are separated from each other to install cameras, ensuring that the baseline distance between the cameras is appropriate to obtain sufficient parallax information. The cameras should be aimed at the same area of ​​the sea surface, kept horizontal or slightly tilted downwards to avoid interference from the sky or too much background.

[0037] Step 2: Based on the determined intrinsic parameters, perform joint calibration of extrinsic parameters. By extracting matching feature points in the same scene, the relative position and pose between the two cameras are calculated. The entire calibration process can be automated, covering feature point extraction, matching, and optimization of intrinsic and extrinsic parameters, automatically completing the accurate calibration of the cameras.

[0038] Step 3: Filter and correct the acquired images. Applying appropriate filtering techniques removes noise and unnecessary interference, thereby improving image quality. If the observation system is deployed on a moving floating structure, a compensation algorithm can be introduced based on the motion signal to adjust the motion compensation parameters in the image in real time, ensuring the accuracy and consistency of the final image. This process provides high-quality foundational data for subsequent 3D reconstruction and time-frequency domain analysis. Using a 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, thus enabling dynamic observation of the target sea area.

[0039] Step 4: Calculate the disparity map using matching feature points from the binocular camera system, and then convert it into 3D coordinates based on the disparity information. By fusing point cloud data from multiple viewpoints, an accurate sea surface model is constructed, fully reflecting the dynamic changes and morphological characteristics of ocean waves.

[0040] Step 5: Collect sea surface data for a certain period. Using time-frequency analysis, information such as wave frequency, amplitude, and phase can be extracted to gain a deeper understanding of their dynamic characteristics and calculate wave parameters such as significant wave height. By performing time-domain decomposition of the wave signal, wave components of different frequencies and amplitudes can be identified, thus providing a deeper understanding of the impact of waves on the floating body. The wave parameter estimates obtained through time-frequency analysis include key indicators such as significant wave height, peak wavelength, and wave direction. These parameters, as frequency-domain statistical indicators, can be used for in-depth analysis of sea surface evolution over a certain period. Simultaneously, time-domain analysis involves the decomposition of wave signals to further understand and describe the dynamic characteristics of the waves.

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

[0042] This invention proposes a binocular vision-based time-frequency domain observation method for ocean waves. This method uses a dual-camera system to synchronously observe wave morphology and employs feature point matching technology to accurately calibrate the camera's intrinsic and extrinsic parameters. A stereo matching algorithm extracts 3D point cloud data for sea surface reconstruction, generating a 3D wave morphology. Time-frequency domain analysis is performed on the reconstructed sea surface data to calculate sea state parameters and perform time-domain wave decomposition, thus achieving comprehensive observation and analysis of wave motion characteristics. This method not only improves the accuracy and real-time performance of wave observation but also reduces observation costs and enhances flexibility and adaptability. Binocular vision technology acquires depth information by calculating parallax, enabling the system to generate high-resolution 3D dynamic models of ocean waves, better reflecting their instantaneous changes. Compared to traditional wave buoys and X-band wave radar, this method offers greater flexibility and adaptability, obtains higher-quality point cloud data, and achieves more accurate wave reconstruction. Furthermore, the system is easy to integrate, has low hardware costs, and can significantly reduce equipment investment and maintenance costs in offshore operations, improving economic efficiency. The binocular vision system also possesses the ability to adapt to different marine environments and operational requirements, meeting the increasingly complex needs of modern marine operations. Through real-time data processing and analysis, this method improves the accuracy and efficiency of observation results, providing reliable technical support for marine engineering and sea condition early warning, and promoting 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 utilizing depth information calculations based on binocular vision technology, high-resolution three-dimensional dynamic models 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) Reduced observation costs: Compared with traditional observation methods, binocular vision systems have lower hardware costs and are easier to integrate. This means that equipment investment and maintenance costs can be significantly reduced in offshore operations, thereby improving economic efficiency.

[0046] (3) Enhanced flexibility and adaptability: Compared to fixed observation equipment, binocular vision systems offer greater flexibility, enabling adjustments and applications to suit different marine environments and operational needs. This flexibility meets the increasingly complex demands of modern marine operations.

[0047] Other beneficial effects of the embodiments of the present invention will be further described below. Attached Figure Description

[0048] Figure 1 This is a flowchart of a binocular vision-based time-frequency domain observation method for ocean waves according to an embodiment of the present invention.

[0049] Figure 2 This is a schematic diagram of the binocular system layout according to an embodiment of the present invention.

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

[0051] Figure 4 This is a schematic diagram of the wave spectrum observed in an embodiment of the present invention. Detailed Implementation

[0052] The embodiments of the present invention will be described in detail below. It should be emphasized that the following description is merely exemplary and not intended to limit the scope and application of the present invention.

[0053] This invention proposes a binocular vision-based time-frequency domain observation method for ocean waves, mainly including: (1) deploying a dual-camera system to acquire sea surface images; (2) combining the camera's intrinsic and extrinsic parameters to automatically extract feature points of the captured content and perform joint calibration to ensure accurate camera calibration; (3) filtering and correcting the acquired images, and correcting image deviations caused by motion according to the motion compensation algorithm for floating structures; (4) extracting three-dimensional point cloud data through a stereo matching algorithm, reconstructing the sea surface, and generating the three-dimensional shape of ocean waves; (5) performing time-frequency domain analysis on the reconstructed sea surface data, calculating sea state parameters, and performing time-domain wave decomposition, thereby achieving comprehensive observation and analysis of ocean wave motion characteristics. This non-contact ocean wave observation based on binocular vision has the advantage of low cost, and through precise time-frequency domain analysis and three-dimensional reconstruction, it comprehensively observes and quantifies the dynamic characteristics of ocean waves, providing reliable technical support for marine engineering and sea state early warning.

[0054] See Figure 1 This invention provides a method for observing ocean waves in the time and frequency domain based on binocular vision, comprising the following steps:

[0055] Step S1: Install a dual-camera system at two pre-set fixed locations, ensuring an appropriate baseline distance between the cameras to acquire sufficient parallax information. The cameras should be aimed at the same sea surface area to achieve simultaneous observation of wave patterns. The dual-camera system can be deployed on the shore, an offshore platform, or a moving floating structure. When the cameras are deployed on a moving floating structure, motion status information of the cameras is acquired simultaneously with wave pattern observation via a GNSS / IMU motion acquisition system.

[0056] Step S2: Based on the determined camera intrinsic parameters, use feature point matching technology to calculate the relative position and attitude between the two cameras, and complete the joint calibration of the extrinsic parameters.

[0057] Step S2 specifically includes: determining the camera's internal parameters, 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 pose between the two cameras; optimizing the camera's internal and external parameters by minimizing reprojection error to ensure the accuracy of 3D reconstruction; and integrating the internal and external parameters to complete the precise calibration of the camera.

[0058] Step S3: Filter and correct the acquired images, using filtering techniques to remove image noise and interference. When the camera is positioned on a moving floating structure, a motion compensation algorithm is also introduced to adjust the motion compensation parameters in the image in real time, ensuring 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 point cloud data from 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 the 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 ocean 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 by minimizing the point set matching cost to ensure the accuracy and consistency of the three-dimensional reconstruction.

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

[0062] Specifically, sea surface data of a certain duration is collected and analyzed, and key parameters of the waves, including frequency, amplitude, and phase, are extracted using time-frequency analysis methods. The spectral density can be estimated using inter-frame power spectral density (PSD). Wave parameters, such as significant wave height and meaningful wave height, are calculated. By decomposing the wave signal in the time domain, wave components of different frequencies and amplitudes can be identified, and the impact of the waves on the floating body can be assessed.

[0063] Compared to traditional methods such as wave buoy methods, the method of this invention offers greater flexibility and adaptability in observing the dynamic characteristics of ocean waves. Furthermore, compared to 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 be applied not only to shore and offshore platforms but also integrated with motion sensors and compensation algorithms for use on moving floating structures. Real-time data processing and analysis improve the accuracy and efficiency of observation results. This method is low-cost, simple to deploy, and easy to implement.

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

[0065] A time-frequency domain observation method for ocean waves based on binocular vision specifically includes the following steps:

[0066] Step 1: Select two fixed locations that are separated from each other to install cameras, ensuring that the baseline distance between the cameras is appropriate to obtain sufficient parallax information. The cameras should be aimed at the same area of ​​the sea surface, kept horizontal or slightly tilted downwards to avoid interference from the sky or too much background.

[0067] Binocular vision uses parallax to establish depth relationships, specifically as follows:

[0068]

[0069] Equation (1) describes how three-dimensional coordinates are projected onto a two-dimensional image plane (e.g., 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 These are the x-axis and y-axis scaling factors related to the camera's intrinsic parameters, while u0 and v0 are the horizontal and vertical center coordinates of the image coordinate system. The coordinates are expressed in homogeneous form and as a matrix as equation (2).

[0070]

[0071] By introducing a wave coordinate system, a stable reference benchmark is provided for binocular vision. Camera calibration is performed using the transformation relationships between the wave coordinate system, the world coordinate system, and the image coordinate system. During the calibration process, the three-dimensional points on the wave surface are transformed from the wave coordinate system to the image coordinate system, thereby more accurately calculating the camera's intrinsic and extrinsic parameters.

[0072] Step 2: Based on the determined intrinsic parameters, perform joint calibration of extrinsic parameters. By extracting matching feature points in the same scene, the relative position and pose between the two cameras are calculated. The entire calibration process can be automated, covering feature point extraction, matching, and optimization of intrinsic and extrinsic parameters, automatically completing the accurate calibration of the cameras.

[0073] Calibration involves mapping from a three-dimensional world coordinate system to a two-dimensional image coordinate system. Homogeneous coordinates, by introducing an additional dimension, simplify computation by combining various transformations such as translation, rotation, and scaling into a single matrix multiplication.

[0074]

[0075] Equation (3) can represent a homogeneous coordinate transformation process, where (X,Y,Z) are points in the world coordinate system, and (x',y') are projections 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, used 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 Equation (5).

[0076]

[0077] In the formula p i These are points observed in the actual image. These are points calculated by reprojecting from the camera model into 3D points, where N is the number of points. Reprojection error can be used to evaluate the accuracy of camera calibration. A smaller error indicates more accurate calibration, meaning the model reflects the camera's true projection characteristics well. During calibration, minimizing reprojection error optimizes the camera's intrinsic and extrinsic parameters, ensuring the reconstructed 3D scene matches the observed image as closely as possible.

[0078] Step 3: Filter and correct the acquired images. Applying appropriate filtering techniques removes noise and unnecessary interference, thereby improving image quality. If the observation system is deployed on a moving floating structure, a compensation algorithm can be introduced based on the motion signal to adjust the motion compensation parameters in the image in real time, ensuring the accuracy and consistency of the final image. This process provides high-quality foundational data for subsequent 3D reconstruction and time-frequency domain analysis.

[0079] Step 4: Calculate the disparity map using matching feature points from the binocular camera system, and then convert it into 3D coordinates based on the disparity information. By fusing point cloud data from multiple viewpoints, an accurate sea surface model is constructed, fully reflecting the dynamic changes and morphological characteristics of ocean waves. This process includes:

[0080] Establish a cost function to quantify the matching quality at a given image coordinate (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, representing the matching error without weight adjustment, and w(x,y,d) is the weighting function used to adjust the impact of different depths d. In depth estimation, the goal is to find the optimal depth value by minimizing this cost function. The optimal depth estimation process is shown in Equation (7), which involves finding 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'. This is achieved by calculating the matching quality between point p. i and corresponding point q mi The total cost is obtained by summing the squared distances between the points, and the optimal 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, resulting in accurate and consistent results in image analysis and 3D reconstruction.

[0086] Step 5: Collect sea surface data for a certain period of time. Using time-frequency analysis, information such as the frequency, amplitude, and phase of the waves can be extracted to gain a deeper understanding of their dynamic characteristics and calculate wave parameters such as significant wave height. By performing time-domain decomposition on the wave signal, wave components of different frequencies and amplitudes can be identified, thereby gaining a deeper understanding of the impact of waves on the floating body.

[0087] Spectral density estimation typically uses inter-frame power spectral density (PSD) for estimation (e.g.) Figure 4 ):

[0088]

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

[0090]

[0091] In summary, this invention's binocular vision-based time-frequency domain observation method for ocean waves synchronously observes wave morphology using a dual-camera system. It utilizes feature point matching technology to accurately calibrate the cameras' intrinsic and extrinsic parameters, extracts 3D point cloud data through a stereo matching algorithm, reconstructs the sea surface, and generates the 3D wave morphology. Time-frequency domain analysis is then performed on the reconstructed sea surface data to calculate sea state parameters and perform time-domain wave decomposition, thereby achieving comprehensive observation and analysis of wave motion characteristics. This method not only improves the accuracy and real-time performance of wave observation but also reduces observation costs and enhances flexibility and adaptability. The binocular vision system calculates depth information through the parallax of two cameras, offering advantages such as low hardware cost, high processing efficiency, and ease of system integration. This invention utilizes binocular vision technology for real-time ocean observation, reconstructing a 3D dynamic model of real-time ocean waves from point clouds, and then analyzing its time-domain dynamic characteristics and frequency-domain statistical parameters. This method significantly improves the accuracy and real-time performance of wave information acquisition, overcoming the limitations of traditional observation methods, such as the long deployment time and lack of flexibility of wave buoys, as well as the high cost and inability to accurately acquire sea surface waveforms of X-band wave radar. It provides strong technical support for marine engineering and sea condition early warning, ensures the safety of various maritime operations and navigation, and has significant economic benefits and application prospects.

[0092] This invention also provides a storage medium for storing a computer program, which, when executed, performs at least the methods described above.

[0093] This invention also provides a control device, including a processor and a storage medium for storing a computer program; wherein the processor executes the computer program by performing at least the method described above.

[0094] This invention also provides a processor that executes a computer program, at least performing the methods described above.

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

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

[0097] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0098] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0099] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0100] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

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

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

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

[0104] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various equivalent substitutions or obvious modifications can be made without departing from the concept of the present invention, and all such modifications, achieving the same performance or application, should be considered within the scope of protection of the present invention.

Claims

1. A time-frequency domain observation method for ocean waves based on binocular vision, characterized in that, Includes the following steps: S1: Install a dual-camera system at two preset fixed locations, with both cameras aimed at the same sea surface area, to achieve synchronous observation of wave patterns; S2: Based on the determined camera intrinsic parameters, use feature point matching technology to calculate the relative position and attitude between the two cameras, and complete the joint calibration of the extrinsic parameters; S3: Filter and correct the acquired image to remove image noise and interference through filtering techniques; S4: Calculate the disparity map using matching feature points in the binocular camera system, and convert it into three-dimensional coordinates based on the disparity information. By fusing point cloud data from multiple viewpoints, construct a sea surface model to reflect the dynamic changes and morphological characteristics of ocean waves. Step S4 specifically includes: Disparity maps are generated using matching feature points in a binocular camera system; Parallax information is converted into three-dimensional coordinates by fusing point cloud data from multiple viewpoints; Construct an accurate sea surface model to reflect the dynamic changes and morphological characteristics of ocean waves; Establish a cost function to quantify the matching quality under image coordinates and depth; By minimizing the cost function, the optimal depth estimate is determined, thereby finding the best depth value; Evaluate the matching quality between two point sets and ensure the accuracy and consistency of 3D reconstruction by minimizing the point set matching cost; S5: Perform time-frequency domain analysis on the collected sea surface data for a predetermined duration, extract the frequency, amplitude, and phase parameters of the waves, and identify wave components of different frequencies and amplitudes by decomposing the wave signal in the time domain to determine the dynamic characteristics of the waves. Step S5 specifically includes: Collect and analyze sea surface data over a certain period of time; Time-frequency analysis methods are used to extract key parameters of ocean waves, including frequency, amplitude, and phase. Among them, inter-frame power spectral density is used to estimate wave spectral density. Calculate wave parameters, including significant wave height and meaningful wave height; By decomposing ocean wave signals in the time domain, wave components of different frequencies and amplitudes can be identified. Assess the impact of ocean waves on the floating body.

2. The time-frequency domain observation method for ocean waves based on binocular vision as described in claim 1, characterized in that, In step S1, the camera is deployed on the shore, an offshore platform, or a moving floating structure. When the camera is deployed on a moving floating structure, the motion status information of the camera is collected through the GNSS / IMU motion acquisition system while observing the wave pattern. In step 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 time-frequency domain observation method for ocean waves based on binocular vision as described in claim 1 or 2, characterized in that, Step S2 specifically includes: Determine the camera's internal parameters, including focal length and principal point coordinates; Identify and extract feature points from images captured by dual cameras; Find corresponding feature point pairs in the two images; Using the matched feature points, the relative position and pose between the two cameras are calculated; By minimizing reprojection errors and optimizing the camera's intrinsic and extrinsic parameters, the accuracy of 3D reconstruction can be ensured. By integrating internal and external parameters, the camera can be accurately calibrated.

4. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the binocular vision-based time-frequency domain observation method for ocean waves as described in any one of claims 1 to 3.

5. A computer program product, comprising a computer program, characterized in that, The computer program, when executed by a processor, implements the method as described in any one of claims 1 to 3.

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