A wave run-up measurement method and device based on vision and terrain calibration

Abstract

An embodiment of the present invention discloses a wave run-up measurement method and device based on vision and terrain calibration. The method collects wave image information through a camera, and RTK data calibration is performed manually on site. The wave image information is processed by an algorithm to obtain a bipartite graph of wave characteristics. After the RTK data coordinates of the geodetic coordinate system and the camera image reference coordinate system are converted, the RTK data and the wave characteristic bipartite graph are superimposed to obtain the highest intersection point as the highest water point, that is, the wave run-up point. Due to the coordination of the model and the combination of on-site calibration, this embodiment can calculate the run-up value of the dam in real time. The calculation process is efficient, saves manpower, and has high accuracy.

Description

Technical Field

[0001] The present invention relates to the field of computer processing technology, and in particular to a wave run-up measurement method and device based on vision and terrain calibration. Background Art

[0002] Wave run-up is the vertical height a wave rises along an inclined surface, also known as wave run-up height. When a wave reaches the shore, most of its energy is dissipated as it breaks. Some of this energy is converted into potential energy as the wave climbs higher on the beach. In coastal engineering, beach wave run-up is a major factor influencing beach dune erosion and accumulation. Determining wave height in wave run-up has a significant impact on embankment stability and the property and personal safety of residents along the coast. Traditionally, wave run-up is simulated using wave flumes, wave generators, and tailored embankment construction for different terrain types. This process is time-consuming and labor-intensive. Summary of the Invention

[0003] In response to the above-mentioned defects, an embodiment of the present invention discloses a wave run-up measurement method and device based on vision and terrain calibration, which obtains the wave run-up point through on-site calibration and visual acquisition, making the measurement more accurate.

[0004] A first aspect of an embodiment of the present invention discloses a wave run-up measurement method based on vision and terrain calibration, comprising:

[0005] Collecting a plurality of wave image information and constructing wave training data according to the wave image information;

[0006] Input the wave training data into the pre-trained improved Yolox algorithm model to extract and output the wave feature image from the wave image information;

[0007] Inputting the wave characteristic image into the unet improved algorithm to obtain a wave characteristic bipartite graph;

[0008] Acquire RTK data of the wave scene, establish the camera image reference coordinate system, and project the RTK data in the geodetic coordinate system into the camera image reference coordinate system;

[0009] The RTK data projected into the camera image reference coordinate system is superimposed on the wave characteristic bipartite map to obtain the highest water point.

[0010] As an optional implementation, in the first aspect of the embodiment of the present invention, the improved yolox algorithm model is trained by an improved yolox algorithm, and the improved yolox algorithm includes a loss function VariFocalLoss and a positioning loss α-iou.

[0011] As an optional implementation, in the first aspect of the embodiment of the present invention, the loss function VariFocalLoss is defined as:

[0012]

[0013] Among them, VFL is the loss function VariFocalLoss, p is the predicted classification score, and q is the target IoU score.

[0014] As an optional implementation, in the first aspect of the embodiment of the present invention, the improved Yolox algorithm further includes an attention mechanism.

[0015] As an optional implementation, in the first aspect of the embodiment of the present invention, the unet improved algorithm includes an attention mechanism.

[0016] As an optional implementation manner, in the first aspect of the embodiment of the present invention, projecting the RTK data in the geodetic coordinate system into the camera image reference coordinate system includes:

[0017] Record each wave climbing point based on the RTK data at the wave site, obtain the geographic coordinates of the wave climbing point based on the geodetic coordinate system, and obtain the wave image information corresponding to the current RTK data;

[0018] Obtaining pixel points corresponding to wave height points in the wave image information;

[0019] Acquire the coordinate position of the pixel point based on the camera image reference coordinate system, and establish a mapping relationship according to the coordinate position of the pixel point and the geographical coordinates of the wave climbing point;

[0020] The RTK data in the earth coordinate system is projected into the camera image reference coordinate system according to the mapping relationship.

[0021] As an optional implementation manner, in the first aspect of the embodiment of the present invention, superimposing the RTK data projected into the camera image reference coordinate system with the wave characteristic bipartite map to obtain the highest water point includes:

[0022] The RTK data is an RTK survey line, and a plurality of the RTK survey lines are obtained;

[0023] The intersection point of each RTK survey line and the wave characteristic bipartite graph is obtained, and the highest intersection point is selected as the highest water point.

[0024] A second aspect of an embodiment of the present invention discloses a wave run-up measurement device based on vision and terrain calibration, comprising:

[0025] Image acquisition unit: used to collect a number of wave image information and construct wave training data based on the wave image information;

[0026] Wave extraction unit: used to input wave training data into the pre-trained improved yolox algorithm model to extract and output wave feature images from wave image information;

[0027] Feature acquisition unit: used for inputting the wave feature image into the unet improved algorithm to obtain a wave feature bipartite graph;

[0028] Coordinate mapping unit: used to obtain RTK data of the wave scene, establish the camera image reference coordinate system, and project the RTK data in the geodetic coordinate system into the camera image reference coordinate system;

[0029] High value calculation unit: used to superimpose the RTK data projected into the camera image reference coordinate system with the wave characteristic bipartite map to obtain the highest water point.

[0030] A third aspect of an embodiment of the present invention discloses an electronic device, comprising: a memory storing executable program code; a processor coupled to the memory; the processor calling the executable program code stored in the memory to execute the wave runup measurement method based on vision and terrain calibration disclosed in the first aspect of the embodiment of the present invention.

[0031] A fourth aspect of an embodiment of the present invention discloses a computer-readable storage medium storing a computer program, wherein the computer program enables a computer to execute the wave runup measurement method based on vision and terrain calibration disclosed in the first aspect of an embodiment of the present invention.

[0032] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0033] The wave run-up measurement method based on vision and terrain calibration disclosed in the embodiment of the present invention collects wave image information through a camera, and RTK data calibration is performed manually on site. The wave image information is processed by algorithm to obtain a bipartite graph of wave characteristics. After the RTK data coordinates of the geodetic coordinate system and the camera image reference coordinate system are converted, the RTK data and the wave characteristic bipartite graph are superimposed to obtain the highest intersection point as the highest water point, which is also the wave run-up point. Due to the coordination of the model and the combination of on-site calibration, this embodiment can calculate the run-up value of the dam in real time. The calculation process is efficient, saves manpower, and has high accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0035] Figure 1 This is a flow chart of a wave run-up measurement method based on vision and terrain calibration disclosed in an embodiment of the present invention;

[0036] Figure 2 This is a flow chart of another wave run-up measurement method based on vision and terrain calibration disclosed in an embodiment of the present invention;

[0037] Figure 3 This is a flow chart of another wave run-up measurement method based on vision and terrain calibration disclosed in an embodiment of the present invention;

[0038] Figure 4 1 is a schematic structural diagram of a wave height measurement device based on vision and terrain calibration provided by an embodiment of the present invention;

[0039] Figure 5 is a structural diagram of an electronic device provided by an embodiment of the present invention;

[0040] Figure 6 is a normalized computation graph provided by an embodiment of the present invention;

[0041] Figure 7 This is a diagram of the composition of an FRN provided by an embodiment of the present invention;

[0042] Figure 8 This is a schematic diagram of wave runup measurement provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0043] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0044] It should be noted that the terms "first," "second," "third," "fourth," etc. in the description and claims of the present invention are used to distinguish different objects rather than to describe a specific order. The terms "including" and "having," as well as any variations thereof, in the embodiments of the present invention, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such process, method, product, or apparatus.

[0045] The embodiment of the present invention discloses a wave run-up measurement method, device, electronic device and storage medium based on vision and terrain calibration. Wave image information is collected by a camera, and RTK data calibration is performed manually on site. The wave image information is processed by an algorithm to obtain a bipartite graph of wave characteristics. After the RTK data coordinates of the geodetic coordinate system and the camera image reference coordinate system are converted, the RTK data and the wave characteristic bipartite graph are superimposed to obtain the highest intersection point as the highest water point, that is, the wave run-up point. Due to the coordination of the model and the combination of on-site calibration, this embodiment can calculate the run-up value of the dam in real time. The calculation process is efficient, saves manpower, and has high accuracy.

[0046] Example 1

[0047] See also Figure 1 , Figure 1 It is a flow chart of the wave height measurement method based on vision and terrain calibration disclosed in an embodiment of the present invention. Among them, the execution subject of the method described in the embodiment of the present invention is an execution subject composed of software and / or hardware, and the execution subject can receive relevant information by wired or / and wireless means, and can send certain instructions. Of course, it can also have certain processing functions and storage functions. The execution subject can control multiple devices, such as a remote physical server or cloud server and related software, or it can be a local host or server and related software that performs related operations on a device placed somewhere. In some scenarios, multiple storage devices can also be controlled, and the storage devices can be placed in the same place or different places as the devices. For example Figure 1 As shown, the wave run-up measurement method based on vision and terrain calibration includes the following steps:

[0048] 101. Collect some wave image information, and construct wave training data based on the wave image information.

[0049] In the embodiment, the application scenario is usually in a dam, etc. When waves come towards the dam and hit the side wall of the dam, a camera prepared in advance can be used to capture images of the wave conditions, which can include pictures and videos. Constructing wave training data based on the wave image information means that the images captured by the camera need to be sorted. For example, if the wave image information is in the form of a video image, several pictures with wave images can be cropped from the video data as wave training data. In another example, if the wave image information is in the form of pictures, the wave image information is sorted, and the sorting includes filtering out duplicate pictures, removing blurred pictures, etc.

[0050] 102. Input the wave training data into the pre-trained improved yolox algorithm model to extract and output the wave feature image from the wave image information.

[0051] In this embodiment, the wave training data is also the model input data. The Yolox algorithm is a common target detection algorithm. Beyond image classification, there are more complex tasks, including target detection, which the Yolox algorithm can perform, identifying objects in an image and giving their locations. In this embodiment, inputting wave training data into a pre-trained model can output a wave feature image.

[0052] The improved yolox algorithm model is trained by the improved yolox algorithm, which includes the loss function VariFocalLoss and the positioning loss α-iou. The loss function VariFocalLoss is defined as:

[0053]

[0054] Where VFL is the loss function VariFocalLoss, p is the predicted classification score, and q is the object Intersection over Union (IoU) score. For positive samples, q is the IoU between the predicted bounding box and the ground-truth box; for negative samples, q is 0. This loss function VariFocalLoss is based on the loss function FocalLoss. Focal Loss treats positive and negative samples identically, while VariFocalLoss treats them unequally.

[0055] The improved yolox algorithm also includes an attention mechanism. In the improved yolox algorithm, the attention mechanism is introduced into its backbone network.

[0056] 103. Input the wave feature image into the unet improved algorithm to obtain a wave feature bipartite graph.

[0057] The UNET algorithm is a classic semantic segmentation algorithm. In the embodiment, the UNET algorithm is improved, and the attention mechanism is also introduced into the improved UNET algorithm. Before the encoder and decoder features are spliced, an SE attention module is used to readjust the encoder features. In the improved UNET algorithm, Relu is replaced with leaky Relu to alleviate the gradient vanishing problem, and the BN layer is replaced with the FRN layer to solve the problem of normalization not relying on the batch, thereby improving accuracy. Normalizing the training data helps optimize the model. For deep models, the BN layer is the most commonly used method to normalize the intermediate feature layer in deep models, which helps model training. The BN layer performs normalization by calculating the mean and variance on each channel of all samples in the batch.

[0058] The above normalization is calculated as follows Figure 6 Taking the CNN model as an example, the dimension of the intermediate features is [B, H, W, C]. BN first calculates the mean μ and variance σ in the (NH, W) dimension, and then performs standard normalization on each channel (C dimension). Finally, the normalized features are scaled and shifted. Here, γ and β are learnable parameters (the parameter size is C). The FRN layer consists of two parts: Filtered Response Normalization (FRN) and Thresholded Linear Unit (TLU), as shown in Figure 2. Figure 7 Indicates that the operation of FRN is in the (H, W) dimension, that is, each channel of each sample is normalized separately, x is an N-dimensional (HxW) vector, and v2 is the average value of the quadratic norm of x. ∈ is a very small positive constant, which can be 1e-6 to prevent division by zero. After normalization, scaling and translation transformations are also required. Here, γ and β are also learnable parameters (the parameter size is C). FRN lacks the operation of de-meaning, which may cause the normalized result to arbitrarily offset 0. If the FRN is followed by a ReLU activation layer, many zero values may be generated, which is not conducive to model training and performance. In order to solve this problem, the thresholded ReLU, namely TLU, is used after FRN, except that τ is a learnable parameter. In the embodiment, focalloss is used as the nuet loss function to solve the imbalance problem between wave and background categories. The improved unet can obtain a binary image of the final segmentation of the wave height.

[0059] 104. Obtain RTK data of the wave site, establish a camera image reference coordinate system, and project the RTK data in the geodetic coordinate system into the camera image reference coordinate system.

[0060] A geodetic coordinate system is a coordinate system established using a reference ellipsoid as a reference surface in geodetic surveying. The location of a ground point is expressed in geodetic longitude, latitude, and altitude. Establishing a geodetic coordinate system involves selecting an ellipsoid, positioning it, and determining geodetic starting data. A camera image reference coordinate system, on the other hand, is established using the camera as a reference point. Through field measurements, one person measures the actual height using RTK, while another person records the corresponding pixel position on the camera to establish a pixel-to-measured height conversion.

[0061] 105. The RTK data projected into the camera image reference coordinate system is superimposed on the wave characteristic bipartite map to obtain the highest water point.

[0062] Example 2

[0063] See also Figure 2 , Figure 2 This is a flow chart of another wave height measurement method based on vision and terrain calibration disclosed in an embodiment of the present invention, such as Figure 2 As shown, the wave run-up measurement method includes:

[0064] 201. Collect some wave image information, and construct wave training data based on the wave image information.

[0065] 202. Input the wave training data into the pre-trained improved Yolox algorithm model to extract and output the wave feature image from the wave image information.

[0066] 203. Input the wave feature image into the unet improved algorithm to obtain a wave feature bipartite graph.

[0067] 204. Obtain RTK data of the wave scene and establish a camera image reference coordinate system.

[0068] 205. Record each wave climbing point according to the RTK data at the wave site, obtain the geographic coordinates of the wave climbing point according to the geodetic coordinate system, and obtain wave image information corresponding to the current RTK data.

[0069] 206. Obtain pixel points corresponding to wave high points in the wave image information.

[0070] 207. Obtain the coordinate position of the pixel point based on the camera image reference coordinate system, and establish a mapping relationship according to the coordinate position of the pixel point and the geographic coordinates of the wave climbing point.

[0071] 208. Project the RTK data in the earth coordinate system into the camera image reference coordinate system according to the mapping relationship.

[0072] 209. The RTK data projected into the camera image reference coordinate system is superimposed on the wave characteristic bipartite map to obtain the highest water point.

[0073] Example 3

[0074] See also Figure 3 , Figure 3 This is a flow chart of another wave height measurement method based on vision and terrain calibration disclosed in an embodiment of the present invention, such as Figure 3 As shown, the wave run-up measurement method includes:

[0075] 301. Collect some wave image information, and construct wave training data based on the wave image information.

[0076] 302. Input the wave training data into a pre-trained improved yolox algorithm model to extract and output a wave feature image from the wave image information.

[0077] 303. Input the wave feature image into the unet improved algorithm to obtain a wave feature bipartite graph.

[0078] 304. Acquire RTK data of the wave site, establish a camera image reference coordinate system, and project the RTK data in the geodetic coordinate system into the camera image reference coordinate system.

[0079] 305. The RTK data is an RTK survey line, and a plurality of the RTK survey lines are obtained.

[0080] 306. Obtain the intersection point between each of the RTK survey lines and the wave characteristic bipartite graph, and select the highest intersection point as the highest water point.

[0081] Combine Figure 8 , Figure 8 The figure shows a schematic diagram of wave run-up measurement using a dam as an example. A represents the boundary line when the wave reaches the side wall of the dam, which is also the wave characteristic bipartite map. B is the RTK survey line. The more RTK survey lines there are, the more accurate the final result will be. Point C represents the highest point after the RTK survey line and the wave characteristic bipartite map are superimposed. This point is used as the highest water point of the wave run-up.

[0082] Example 4

[0083] See also Figure 4 , Figure 4 Schematic diagram of the structure of the wave height measurement device based on vision and terrain calibration disclosed in the embodiment of the present invention. Figure 4As shown, the wave height measurement device based on vision and terrain calibration may include an image acquisition unit 401, a wave extraction unit 402, a feature acquisition unit 403, a coordinate mapping unit 404 and a high-value calculation unit 405, wherein the image acquisition unit 401 is used to acquire a number of wave image information and construct wave training data based on the wave image information; the wave extraction unit 402 is used to input the wave training data into a pre-trained improved Yolox algorithm model to extract and output a wave feature image from the wave image information; the feature acquisition unit 403 is used to input the wave feature image into the UNET improved algorithm to obtain a wave feature bipartite map; the coordinate mapping unit 404 is used to acquire RTK data of the wave site, establish a camera image reference coordinate system, and project the RTK data in the geodetic coordinate system into the camera image reference coordinate system; the high-value calculation unit 405 is used to superimpose the RTK data projected into the camera image reference coordinate system with the wave feature bipartite map to obtain the highest water point.

[0084] In the embodiment, the improved yolox algorithm model is trained by the improved yolox algorithm, and the improved yolox algorithm includes a loss function VariFocalLoss and a positioning loss α-iou. In addition, the loss function VariFocalLoss is defined as:

[0085]

[0086] Among them, VFL is the loss function VariFocalLoss, p is the predicted classification score, and q is the target IoU score.

[0087] Furthermore, the improved Yolox algorithm also includes an attention mechanism. The improved UNET algorithm includes an attention mechanism. The attention mechanism originates from the study of human vision. In cognitive science, due to the bottleneck of information processing, humans will selectively focus on part of all information while ignoring other visible information. The above mechanism is usually referred to as the attention mechanism. Different parts of the human retina have different degrees of information processing capabilities, that is, acuity, and only the fovea centralis has the strongest acuity. In order to make rational use of limited visual information processing resources, humans need to select a specific part of the visual area and then focus on it. For example, when people read, usually only a small number of words to be read will be paid attention to and processed. In summary, the attention mechanism has two main aspects: deciding which part of the input needs to be paid attention to; allocating limited information processing resources to important parts. In an embodiment, the attention mechanism allocates more resources to focus on wave feature extraction, processing, etc.

[0088] The coordinate mapping unit 404 projects the RTK data in the earth coordinate system into the camera image reference coordinate system. Specifically, it includes a data recording module, a pixel acquisition module, a mapping establishment module, and a projection module. The data recording module records each wave height point based on the RTK data at the wave site, acquires the geographic coordinates of the wave height point based on the earth coordinate system, and obtains the wave image information corresponding to the current RTK data. The pixel acquisition module acquires the pixel points corresponding to the wave height points in the wave image information. The mapping establishment module acquires the coordinate positions of the pixel points based on the camera image reference coordinate system and establishes a mapping relationship based on the coordinate positions of the pixel points and the geographic coordinates of the wave height points. The projection module projects the RTK data in the earth coordinate system into the camera image reference coordinate system based on the mapping relationship.

[0089] The high value calculation unit 405 specifically includes a survey line acquisition module and an intersection acquisition module, wherein the survey line acquisition module is used to obtain several RTK survey lines, and the RTK data is the RTK survey line; the intersection acquisition module is used to obtain the intersection of each RTK survey line and the wave characteristic bipartite graph, and the highest intersection point is selected as the highest water point.

[0090] Example 5

[0091] See also Figure 5 , Figure 5 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of the present invention. The electronic device can be a computer, a server, etc. Of course, in certain circumstances, it can also be a smart device such as a mobile phone, a tablet computer, a monitoring terminal, and an image acquisition device with processing functions. Figure 5 As shown, the electronic device may include:

[0092] A memory 501 storing executable program code;

[0093] a processor 502 coupled to the memory 501;

[0094] The processor 502 calls the executable program code stored in the memory 501 to execute part or all of the steps in the wave runup measurement method based on vision and terrain calibration in the first embodiment.

[0095] An embodiment of the present invention discloses a computer-readable storage medium storing a computer program, wherein the computer program enables a computer to execute some or all of the steps in the wave runup measurement method based on vision and terrain calibration in embodiment 1.

[0096] An embodiment of the present invention further discloses a computer program product, wherein when the computer program product is run on a computer, the computer is enabled to execute some or all of the steps in the wave runup measurement method based on vision and terrain calibration in embodiment one.

[0097] An embodiment of the present invention also discloses an application publishing platform, wherein the application publishing platform is used to publish a computer program product. When the computer program product is run on a computer, the computer executes some or all of the steps in the wave runup measurement method based on vision and terrain calibration in embodiment one.

[0098] In various embodiments of the present invention, it should be understood that the size of the serial numbers of the processes does not necessarily mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0099] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one place or distributed across multiple network units. Some or all of these units may be selected based on actual needs to achieve the objectives of this embodiment.

[0100] In addition, the functional units in the embodiments of the present invention may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The integrated unit may be implemented in the form of hardware or software functional units.

[0101] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-accessible memory. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a memory and includes several requests for causing a computer device (which can be a personal computer, server, or network device, specifically a processor in the computer device) to execute some or all of the steps of the method described in each embodiment of the present invention.

[0102] In the embodiments provided herein, it should be understood that "B corresponding to A" means that B is associated with A and B can be determined based on A. However, it should also be understood that determining B based on A does not mean determining B based solely on A; B can also be determined based on A and / or other information.

[0103] Those skilled in the art will appreciate that some or all of the steps in the various methods of the embodiments may be performed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, including a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0104] The above is a detailed introduction to the wave runup measurement method, device, electronic device and storage medium based on vision and terrain calibration disclosed in the embodiments of the present invention. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the ideas of the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A wave run-up measurement method based on vision and terrain calibration, characterized in that: include: Collecting a plurality of wave image information and constructing wave training data according to the wave image information; Input the wave training data into the pre-trained improved Yolox algorithm model to extract and output the wave feature image from the wave image information; Inputting the wave characteristic image into the unet improved algorithm to obtain a wave characteristic bipartite graph; Acquire RTK data of the wave scene, establish the camera image reference coordinate system, and project the RTK data in the geodetic coordinate system into the camera image reference coordinate system; The RTK data projected into the camera image reference coordinate system is superimposed on the wave characteristic bipartite map to obtain the highest water point; the superimposition of the RTK data projected into the camera image reference coordinate system and the wave characteristic bipartite map to obtain the highest water point includes: The RTK data is an RTK survey line, and a plurality of the RTK survey lines are obtained; The intersection point of each RTK survey line and the wave characteristic bipartite graph is obtained, and the highest intersection point is selected as the highest water point.

2. The wave run-up measurement method according to claim 1, characterized in that: The improved yolox algorithm model is trained by the improved yolox algorithm, and the improved yolox algorithm includes a loss function VariFocalLoss and a positioning loss α-iou.

3. The wave run-up measurement method according to claim 2, characterized in that: The loss function VariFocalLoss is defined as: Among them, VFL is the loss function VariFocalLoss, p is the predicted classification score, and q is the target IoU score.

4. The wave run-up measurement method according to claim 2, characterized in that: The improved yolox algorithm also includes an attention mechanism.

5. The wave run-up measurement method according to claim 1, characterized in that: The improved unet algorithm includes an attention mechanism.

6. The wave run-up measurement method according to claim 1, characterized in that: The projecting of the RTK data in the earth coordinate system into the camera image reference coordinate system includes: Record each wave climbing point based on the RTK data at the wave site, obtain the geographic coordinates of the wave climbing point based on the geodetic coordinate system, and obtain the wave image information corresponding to the current RTK data; Obtaining pixel points corresponding to wave height points in the wave image information; Acquire the coordinate position of the pixel point based on the camera image reference coordinate system, and establish a mapping relationship according to the coordinate position of the pixel point and the geographical coordinates of the wave climbing point; The RTK data in the earth coordinate system is projected into the camera image reference coordinate system according to the mapping relationship.

7. A wave height measurement device based on vision and terrain calibration, characterized in that: include: Image acquisition unit: used to collect a number of wave image information and construct wave training data based on the wave image information; Wave extraction unit: used to input wave training data into the pre-trained improved yolox algorithm model to extract and output wave feature images from wave image information; Feature acquisition unit: used for inputting the wave feature image into the unet improved algorithm to obtain a wave feature bipartite graph; Coordinate mapping unit: used to obtain RTK data of the wave scene, establish the camera image reference coordinate system, and project the RTK data in the geodetic coordinate system into the camera image reference coordinate system; A high-value calculation unit is used to superimpose the RTK data projected into the camera image reference coordinate system with the wave characteristic bipartite map to obtain the highest water point; the superimposition of the RTK data projected into the camera image reference coordinate system with the wave characteristic bipartite map to obtain the highest water point includes: The RTK data is an RTK survey line, and a plurality of the RTK survey lines are obtained; The intersection point of each RTK survey line and the wave characteristic bipartite graph is obtained, and the highest intersection point is selected as the highest water point.

8. An electronic device, characterized in that: include: a memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the wave runup measurement method based on vision and terrain calibration according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, wherein the computer program enables a computer to execute the wave runup measurement method based on vision and terrain calibration according to any one of claims 1 to 6.

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