Slope scouring identification method, system and device and storage medium

Abstract

The invention discloses a slope scour identification method, system and device and a storage medium. The method comprises the following steps: acquiring point cloud data of a slope; performing density clustering processing on the point cloud data to determine first position data; performing screening processing on the first position data according to a color space, and determining second position data; secondary density clustering processing is carried out on the second position data, and slope scour data are determined; and surface reconstruction processing is conducted on the slope scouring data, and the area of the scoured slope surface is determined. According to the embodiment of the invention, through density clustering and color space data processing, the accuracy of slope scour identification is improved. The embodiment of the invention can be widely applied to the technical field of image processing.

Description

Technical Field

[0001] The present invention relates to the field of image processing technology, and in particular to a slope scour identification method, system, device and storage medium. Background Art

[0002] Slope scour is a natural hazard phenomenon and a common deformation of the earth's surface. Slope scour identification plays an important role in reducing various losses and establishing early warning systems. In related technologies, point cloud data is often used for slope scour identification. However, due to the influence of terrain and trees, multiple sites need to be established to scan the entire slope. In addition, point cloud data contains a lot of noise. Due to the above reasons, using point cloud alone for scour area identification often has poor recognition results. Summary of the invention

[0003] The purpose of the present invention is to solve one of the technical problems existing in the prior art to at least a certain extent.

[0004] To this end, an object of the present invention is to provide an accurate slope scour identification method, system, device and storage medium.

[0005] In order to achieve the above technical objectives, the technical solutions adopted by the embodiments of the present invention include:

[0006] On the one hand, an embodiment of the present invention provides a slope scour identification method, comprising the following steps: The slope scour identification method of the embodiment of the present invention, the method comprising: obtaining point cloud data of the slope; performing density clustering processing on the point cloud data to determine first position data; screening the first position data according to the color space to determine second position data; performing secondary density clustering processing on the second position data to determine slope scour data; performing surface reconstruction processing on the slope scour data to determine the area of ​​the scour slope surface. The embodiment of the present invention improves the accuracy of slope scour identification through density clustering and color space data processing.

[0007] In addition, the slope scour identification method according to the above embodiment of the present invention may also have the following additional technical features:

[0008] Furthermore, in the slope scour identification method of the embodiment of the present invention, the step of performing density clustering processing on the point cloud data to determine the first position data includes:

[0009] Determine a first threshold value according to the vegetation density and the slope scour density; the first threshold value is the threshold value of the density clustering;

[0010] According to the first threshold, density clustering processing is performed on the point cloud data to determine first position data.

[0011] Further, in one embodiment of the present invention, the screening out the first position data according to the color space to determine the second position data includes:

[0012] Performing HSV color space transformation processing on the first position data to determine spatial data;

[0013] The spatial data is screened according to the H value to determine the second position data.

[0014] Further, in one embodiment of the present invention, performing HSV color space transformation processing on the first position data to determine the spatial data includes:

[0015] Normalizing the three primary color data in the first position data to determine first color data;

[0016] Determine a second color data set according to the size relationship of the first color data;

[0017] Based on the second color data set, spatial data is determined.

[0018] Further, in one embodiment of the present invention, the spatial data is screened according to the H value to determine the second position data, including:

[0019] Determine a second threshold range according to the color characteristics of the slope and the vegetation; the second threshold range is related to the H value;

[0020] According to the relationship between the H value and the second threshold range, the spatial data is screened to determine the second position data.

[0021] Further, in one embodiment of the present invention, performing secondary density clustering processing on the second position data to determine the slope scour data includes:

[0022] According to the vegetation density and slope scour density, the minimum field distance is determined to be 0.2 and the minimum number of points is 100;

[0023] According to the minimum domain distance and the minimum number of points, secondary density clustering processing is performed on the point cloud data to determine the slope scour data.

[0024] Furthermore, in one embodiment of the present invention, the surface reconstruction processing of the slope scour data to determine the area of ​​the scour slope surface includes:

[0025] Downsampling the slope scour data to determine downsampled data;

[0026] The down-sampled data is subjected to surface reconstruction through triangulation to determine the area of ​​the scour slope.

[0027] On the other hand, an embodiment of the present invention provides a slope scour identification system, including:

[0028] The first module is used to obtain the point cloud data of the slope;

[0029] The second module is used to perform density clustering processing on the point cloud data to determine the first position data;

[0030] A third module is used to screen out the first position data according to the color space to determine the second position data;

[0031] A fourth module is used to perform secondary density clustering processing on the second position data to determine slope scour data;

[0032] The fifth module is used to perform surface reconstruction processing on the slope scour data to determine the area of ​​the scour slope.

[0033] On the other hand, an embodiment of the present invention provides a slope scour identification device, comprising:

[0034] at least one processor;

[0035] at least one memory for storing at least one program;

[0036] When the at least one program is executed by the at least one processor, the at least one processor implements the above-mentioned slope scour identification method.

[0037] On the other hand, an embodiment of the present invention provides a storage medium storing a program executable by a processor. When the program executable by the processor is executed by the processor, it is used to implement the above-mentioned slope scour identification method.

[0038] The slope scour identification method of the embodiment of the present invention comprises: obtaining point cloud data of the slope; performing density clustering processing on the point cloud data to determine first position data; performing screening processing on the first position data according to the color space to determine second position data; performing secondary density clustering processing on the second position data to determine slope scour data; performing surface reconstruction processing on the slope scour data to determine the area of ​​the scour slope surface. The embodiment of the present invention improves the accuracy of slope scour identification through density clustering and color space data processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the embodiments of the present invention or the drawings of related technical solutions in the prior art are introduced below. It should be understood that the drawings introduced below are only for the convenience of clearly describing some embodiments of the technical solutions of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0040] Figure 1 A schematic diagram of a flow chart of an embodiment of a slope scour identification method provided by the present invention;

[0041] Figure 2 A schematic flow chart of another embodiment of the slope scour identification method provided by the present invention;

[0042] Figure 3 A schematic diagram of a flow chart of an embodiment of density clustering processing provided by the present invention;

[0043] Figure 4 A schematic diagram of a process flow of an embodiment of the HSV segmentation point cloud process provided by the present invention;

[0044] Figure 5 A schematic diagram of the effect of an embodiment of the slope scour identification process provided by the present invention;

[0045] Figure 6 A schematic diagram of the effect of another embodiment of the slope scour identification process provided by the present invention;

[0046] Figure 7 A schematic diagram of the effect of another embodiment of the slope scour identification process provided by the present invention;

[0047] Figure 8 A schematic structural diagram of an embodiment of a slope scour identification system provided by the present invention;

[0048] Fig. 9 A schematic structural diagram of an embodiment of a slope scour identification device provided by the present invention. DETAILED DESCRIPTION

[0049] The embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and are not to be construed as limitations of the present invention. For the step numbers in the following embodiments, they are only provided for the convenience of explanation, and the order between the steps is not limited in any way, and the execution order of each step in the embodiment can be adaptively adjusted according to the understanding of those skilled in the art.

[0050] Slope scour is a natural hazard phenomenon and a common deformation of the earth's surface. It mainly occurs in mountainous areas, hills, foundation pits, coasts and other areas. This situation is generally caused by heavy rains that form wave pits and damage protective vegetation. Landslides not only cause casualties and property losses in nearby areas, but also damage infrastructure such as roads and dams within their coverage, leading to traffic paralysis, stagnation, river interruption and other losses. Therefore, it is of great significance to identify the scour area around the road and estimate its scour area.

[0051] Slope scour identification plays an important role in reducing various losses and establishing early warning systems. Currently, the commonly used slope scour identification methods include: macro geological observation, geodetic measurement, gravity measurement, precision liquid static leveling, GPS monitoring, and drone remote sensing monitoring. Although these methods have their own advantages, they still have disadvantages such as complex operation and low accuracy.

[0052] Laser radar, a new remote sensing technology, has gradually developed. A point in a laser radar point cloud usually contains three-dimensional coordinate information, which is also the three elements of X, Y, and Z that we often talk about, and sometimes also contains color information, reflection intensity information, echo number information, etc. Laser radar has the following characteristics: working around the clock and actively acquiring data; good concealment, strong anti-active interference ability, and a wide range of data acquisition; strong laser penetration ability; small field workload; high point cloud accuracy and accurate spatial coordinate information. Therefore, the point cloud data obtained by laser radar is often also applicable to various aspects such as resource exploration, urban planning, agricultural development, water conservancy projects, environmental monitoring, mining surveying, tunnel surveying, highway road surveying, cable monitoring, and ocean depth measurement. However, due to the influence of terrain and trees, multiple sites need to be established to scan the entire slope. In addition, the point cloud data contains a lot of noise. Due to the above reasons, it is difficult to meet the needs of many applications to use point clouds alone for scour area identification.

[0053] Based on this, an embodiment of the present application proposes a slope scour identification method, aiming to improve the slope scour identification effect.

[0054] The slope scour identification method and system proposed in the embodiments of the present invention will be described in detail below with reference to the accompanying drawings. First, the slope scour identification method proposed in the embodiments of the present invention will be described with reference to the accompanying drawings.

[0055] A slope scour identification method is provided in an embodiment of the present invention. The slope scour identification method in the embodiment of the present invention can be applied to a terminal, a server, or software running in a terminal or a server. The terminal can be a tablet computer, a laptop computer, a desktop computer, etc., but is not limited thereto. The server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The slope scour identification method in the embodiment of the present invention mainly includes the following steps:

[0056] S100: Obtaining point cloud data of the slope;

[0057] S200: performing density clustering processing on the point cloud data to determine first position data;

[0058] S300: Screening the first position data according to the color space to determine second position data;

[0059] S400: performing secondary density clustering processing on the second position data to determine slope scour data;

[0060] S500: Performing surface reconstruction processing on the slope scour data to determine the area of ​​the scour slope surface.

[0061] The method provided in the embodiment of the present application can semi-automatically remove the influence of the road surface and vegetation to achieve efficient slope scour analysis. The present invention is suitable for image processing operations based on the fusion of laser radar point cloud and visible light image to identify and calculate the area of ​​slope scour. In the process of slope scour identification, the point cloud image is segmented and identified based on HSV fusion density clustering, and the vegetation, road surface and some infrastructure outside the scour part are effectively removed. The secondary density filtering method is subsequently applied to completely extract the slope part, and the scour area is estimated through Delaunay triangulation.

[0062] Optionally, in one embodiment of the present invention, performing density clustering processing on the point cloud data to determine the first position data includes:

[0063] Determine a first threshold value according to the vegetation density and the slope scour density; the first threshold value is the threshold value of the density clustering;

[0064] According to the first threshold, density clustering processing is performed on the point cloud data to determine first position data.

[0065] In some possible implementations, the first threshold may be adjusted according to actual needs, and this application does not make any specific limitation thereto.

[0066] Optionally, in one embodiment of the present invention, the screening out the first position data according to the color space to determine the second position data includes:

[0067] Performing HSV color space transformation processing on the first position data to determine spatial data;

[0068] The spatial data is screened according to the H value to determine the second position data.

[0069] Optionally, in one embodiment of the present invention, performing HSV color space transformation processing on the first position data to determine the spatial data includes:

[0070] Normalizing the three primary color data in the first position data to determine first color data;

[0071] Determine a second color data set according to the size relationship of the first color data;

[0072] Based on the second color data set, spatial data is determined.

[0073] Optionally, in one embodiment of the present invention, screening the spatial data according to the H value to determine the second position data includes:

[0074] Determine a second threshold range according to the color characteristics of the slope and the vegetation; the second threshold range is related to the H value;

[0075] According to the relationship between the H value and the second threshold range, the spatial data is screened to determine the second position data.

[0076] In some possible implementations, the second threshold range is the H value range, which can be set according to actual needs.

[0077] Optionally, in one embodiment of the present invention, performing secondary density clustering processing on the second position data to determine the slope scour data includes:

[0078] According to the vegetation density and slope scour density, the minimum field distance is determined to be 0.2 and the minimum number of points is 100;

[0079] According to the minimum domain distance and the minimum number of points, secondary density clustering processing is performed on the point cloud data to determine the slope scour data.

[0080] In some possible implementations, this implementation is an illustrative example, and this application does not limit the specific values ​​of the minimum field example and the minimum number of points.

[0081] Optionally, in one embodiment of the present invention, performing surface reconstruction processing on the slope scour data to determine the area of ​​the scour slope surface includes:

[0082] Downsampling the slope scour data to determine downsampled data;

[0083] The down-sampled data is subjected to surface reconstruction through triangulation to determine the area of ​​the scour slope.

[0084] Specifically, Figure 1 and Figure 2 In one embodiment shown, the slope scour identification method of this embodiment includes the following steps:

[0085] S201, obtaining a point cloud image.

[0086] The slope point cloud image of this embodiment is taken by a laser radar point cloud drone aerial photography payload, and its effective point cloud data rate is 240,000 points / second. This embodiment uses three sets of drone high-resolution image fusion point cloud data as samples to test the proposed method.

[0087] S202: Estimate the scour slope position based on density filtering, remove part of the vegetation, and estimate the slope position.

[0088] This step S202 is before the post-processing of the point cloud. In order to accurately estimate the flushed part, it is necessary to remove the non-flushed part, such as vegetation, road surface and some infrastructure. Due to the different geometric shapes of the slopes, there is no universal mountain vegetation filtering algorithm. For vegetation with high crowns, it is difficult for the lidar pulse to penetrate. In addition, sparse vegetation will cause confusion between the ground and vegetation points. These problems make it difficult to filter vegetation well. According to the principle of different densities of crowns and flushed areas, refer to Figure 3 As shown, this embodiment uses density clustering to separate some vegetation by density difference, selects 0.2 as the minimum area distance, and selects 100 as the minimum number of points to estimate the approximate position of the slope. The result is as follows Figure 5 shown.

[0089] S203, segmenting the processed point cloud image based on HSV to remove the road surface and some other infrastructure.

[0090] In step S203, the road is segmented by HSV. Since the density of the road surface part is similar to that of the slope scouring part, the point cloud Dbscan clustering method alone cannot well extract the complete scouring part. Therefore, the point cloud image after the first density clustering is processed twice by HSV to achieve the purpose of removing the road surface. The result is as Figure 6 shown.

[0091] Further, as shown in Figure 4 , step S203 specifically includes:

[0092] S2031. Perform HSV color space transformation on the point cloud image.

[0093] In this embodiment, the formula for HSV color space transformation is as follows:

[0094]

[0095] V = Dmax;

[0096] Among them, R', G', and B' represent the normalized R, G, and B. Dmax represents the maximum value of each normalized color channel, and Δ represents the difference between this maximum value and the corresponding minimum value Dmin. The calculations are as follows:

[0097] R' = R / 255

[0098] G' = G / 255

[0099] B' = B / 255

[0100] Dmax = max(R', G', B')

[0101] Dmin = min(R', G', B')

[0102] Δ = Dmax - Dmin;

[0103] S2032. Segment the road surface based on the HSV values.

[0104] In this embodiment, when segmenting the road surface based on the HSV values, since the colors of the slope and vegetation are mostly green and yellow, only the point clouds with the H range of yellow and green are retained, and the numerical range is 20 < H < 90. The remaining point clouds are all filtered out to achieve the effect of segmenting and removing the road surface. The result is as Figure 6 shown.

[0105] S204. Determine the scouring slope position based on density filtering, remove the remaining vegetation, and extract the complete scouring slope.

[0106] Step S204 is to extract the complete slope surface. After the point cloud image has been clustered by Dbscan for the first time and the HSV road surface has been removed, the slope surface of the scour part has been basically extracted. However, there are still some vegetation on the edge of the scour part, so it is still necessary to remove the remaining vegetation through density filtering. Therefore, the secondary density filtering method is used to remove the residual vegetation to achieve the purpose of completely separating the scour slope surface. The minimum domain distance is selected as 0.2 and the minimum number of points is selected as 100. The results are as follows: Figure 7 shown.

[0107] S205, triangulate the scour slope surface, calculate the scour area, and output the scour slope surface result to complete the scour slope surface identification.

[0108] In step S205, the scour area is calculated. First, the scour part point cloud image is downsampled. Since the point cloud captured by the laser radar is a dense point cloud with a large point cloud density, direct 3D reconstruction will result in the appearance of some non-existent faces. The downsampled point cloud image is used to reconstruct the slope surface using Delaunay triangulation, and then the sum of the triangular facets is calculated.

[0109] Furthermore, step S205 specifically includes:

[0110] S2051. Calculate the scour area.

[0111] In this embodiment, the formula for calculating the area is as follows:

[0112]

[0113] Where A is the area of ​​each triangular mesh, r is the triangular mesh number, S is the total area of ​​the plant leaf, and n is the total number of triangular meshes.

[0114] Exemplarily, this experiment uses an Intel core i5-12400F CPU@2.5GHz*6 processor, a PC running Windows 10, a graphics card of 1 NVIDIA GeForce RTX 3060Ti, and programming languages ​​of Python and C++.

[0115] For experimental content and result analysis: Figure 5 , Figure 6 and Figure 7 An example of the results of the point cloud image processing process is given, Figure 5 Some vegetation and infrastructure were removed. Figure 6 Removed the road surface, Figure 7 The residual vegetation and residual infrastructure were removed, confirming the accuracy of the slope scour identification method of the present invention.

[0116] The above three sets of experimental results show that this embodiment can accurately identify the slope scour area, extract the slope scour part in the point cloud and calculate the scour area, and the estimated scour area is 1197.78 square meters. By comparing the scour area identified and segmented by the method of the present invention with the scour area marked manually, the calculated structural similarity SSIM value is 0.91

[0117] Secondly, refer to the attached Figure 8 A slope scour identification system proposed according to an embodiment of the present invention is described.

[0118] Figure 8 Schematic diagram of the structure of a slope scour identification system according to an embodiment of the present invention. The system specifically includes:

[0119] The first module 810 is used to obtain point cloud data of the slope;

[0120] The second module 820 is used to perform density clustering processing on the point cloud data to determine the first position data;

[0121] A third module 830 is used to perform screening processing on the first position data according to the color space to determine second position data;

[0122] The fourth module 840 is used to perform secondary density clustering processing on the second position data to determine slope scour data;

[0123] The fifth module 850 is used to perform surface reconstruction processing on the slope scour data to determine the area of ​​the scour slope.

[0124] It can be seen that the contents of the above method embodiments are all applicable to the present system embodiments, the functions specifically implemented by the present system embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0125] Reference Fig. 9 , an embodiment of the present invention provides a slope scour identification device, comprising:

[0126] at least one processor 910;

[0127] At least one memory 920, used to store at least one program;

[0128] When the at least one program is executed by the at least one processor, the at least one processor implements the slope scour identification method.

[0129] Similarly, the contents of the above method embodiments are all applicable to the present device embodiments. The functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0130] An embodiment of the present invention further provides a computer-readable storage medium, in which a program executable by a processor is stored. When the program executable by the processor is executed by the processor, it is used to execute the above-mentioned slope scour identification method.

[0131] Similarly, the contents of the above method embodiments are all applicable to the present storage medium embodiments. The functions specifically implemented by the present storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0132] In some selectable embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the boxes can sometimes be executed in reverse order. In addition, the embodiment presented and described in the flow chart of the present invention is provided by way of example, for the purpose of providing a more comprehensive understanding of technology. The disclosed method is not limited to the operation and logic flow presented herein. Selectable embodiments are expected, wherein the order of various operations is changed and the sub-operation of a part for which is described as a larger operation is performed independently.

[0133] In addition, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise specified, one or more of the functions and / or features can be integrated into a single physical device and / or software module, or one or more functions and / or features can be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the present invention. More specifically, in view of the properties, functions and internal relationships of the various functional modules in the device disclosed herein, the actual implementation of the module will be understood within the conventional skills of the engineer. Therefore, those skilled in the art can implement the present invention set forth in the claims without excessive experimentation using ordinary techniques. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0134] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several programs to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0135] The logic and / or steps represented in the flowchart or otherwise described herein, for example, may be considered as an ordered list of executable programs for implementing the logical functions, and may be embodied in any computer-readable medium for use by a program execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch and execute a program from a program execution system, device or apparatus), or in conjunction with such program execution systems, devices or apparatuses. For purposes of this specification, a "computer-readable medium" may be any device that can contain, store, communicate, propagate or transmit a program for use by a program execution system, device or apparatus, or in conjunction with such program execution systems, devices or apparatuses.

[0136] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0137] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable program execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0138] In the above description of this specification, the description with reference to the terms "one embodiment / example", "another embodiment / example" or "certain embodiments / examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0139] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

[0140] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the embodiments. Those skilled in the art may make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of the present invention.

Claims

1. A slope scour identification method, characterized in that: The following steps are involved: Obtain point cloud data of the slope; Performing density clustering processing on the point cloud data to determine first position data; Screening the first position data according to the color space to determine the second position data; Performing secondary density clustering processing on the second position data to determine slope scour data; The slope scour data is subjected to surface reconstruction processing to determine the area of ​​the scour slope surface.

2. The slope scour identification method according to claim 1, characterized in that: The performing density clustering processing on the point cloud data to determine the first position data includes: Determine a first threshold value according to the vegetation density and the slope scour density; the first threshold value is the threshold value of the density clustering; According to the first threshold, density clustering processing is performed on the point cloud data to determine first position data.

3. The slope scour identification method according to claim 1, characterized in that: The step of screening the first position data according to the color space to determine the second position data includes: Performing HSV color space transformation processing on the first position data to determine spatial data; The spatial data is screened according to the H value to determine the second position data.

4. The slope scour identification method according to claim 3, characterized in that: The performing HSV color space transformation processing on the first position data to determine the spatial data includes: Normalizing the three primary color data in the first position data to determine first color data; Determine a second color data set according to the size relationship of the first color data; Based on the second color data set, spatial data is determined.

5. The slope scour identification method according to claim 3, characterized in that: The spatial data is screened according to the H value to determine the second position data, including: Determine a second threshold range according to the color characteristics of the slope and the vegetation; the second threshold range is related to the H value; According to the relationship between the H value and the second threshold range, the spatial data is screened to determine the second position data.

6. The slope scour identification method according to claim 1, characterized in that: The performing secondary density clustering processing on the second position data to determine the slope scour data includes: According to the vegetation density and slope scour density, the minimum field distance is determined to be 0.2 and the minimum number of points is 100; According to the minimum domain distance and the minimum number of points, secondary density clustering processing is performed on the point cloud data to determine the slope scour data.

7. The slope scour identification method according to claim 1, characterized in that: The performing surface reconstruction processing on the slope scour data to determine the area of ​​the scour slope surface includes: Downsampling the slope scour data to determine downsampled data; The down-sampled data is subjected to surface reconstruction through triangulation to determine the area of ​​the scour slope.

8. A slope scour identification system, characterized in that: include: The first module is used to obtain the point cloud data of the slope; The second module is used to perform density clustering processing on the point cloud data to determine the first position data; A third module is used to screen out the first position data according to the color space to determine the second position data; A fourth module is used to perform secondary density clustering processing on the second position data to determine slope scour data; The fifth module is used to perform surface reconstruction processing on the slope scour data to determine the area of ​​the scour slope.

9. A slope scour identification device, characterized in that: include: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the slope scour identification method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a program executable by a processor, characterized in that: The processor-executable program is used to implement the slope scour identification method according to any one of claims 1 to 7 when executed by the processor.

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