Square pipe culvert boundary contour extraction method and device based on sonar point cloud, and medium

Abstract

The invention discloses a square pipe culvert boundary contour extraction method and device based on sonar point cloud, and a medium, and the method comprises the steps: carrying out the coordinate conversion processing of each frame of intensity information in initial sonar collection data, obtaining corresponding section point cloud data, and carrying out the central point noise removal processing, and obtaining the overall boundary point cloud data; performing DBSCAN clustering filtering processing on the overall boundary point cloud data to obtain target boundary point cloud data; performing neighborhood analysis on the target boundary point cloud data based on the local density of all the points to obtain a plurality of boundary points; determining a target boundary point from all the boundary points based on a preset angle set; and performing linear interpolation processing on all the target boundary points to obtain a target boundary contour for the square pipe culvert. According to the method, the target boundary contour of the square pipe culvert is obtained by performing coordinate conversion, denoising, clustering and other processing on the initial sonar data, and compared with a manual acquisition scheme, the accuracy of the boundary contour of the pipe culvert can be effectively improved.

Description

Technical Field

[0001] The present application relates to but is not limited to the field of point cloud data technology, and in particular to a method, device, and medium for extracting the boundary contour of a square culvert based on sonar point cloud. Background Art

[0002] With the continuous development of underwater detection technology, underwater sonar equipment is increasingly used in culvert detection. In related technologies, underwater unmanned submersibles are used to carry out cross-sectional sonar surveys of the current status of culverts. By extracting point cloud boundaries from the collected sonar data, it can help accurately identify the surface of the structure, thereby achieving real-time assessment of the underwater environment. The existing method is to obtain the point cloud image through the acquisition equipment, and then manually mark the point cloud image to obtain the culvert boundary contour, which cannot guarantee the accuracy of the culvert boundary contour. Summary of the invention

[0003] The embodiments of the present application provide a method, device, and medium for extracting the boundary contour of a square culvert based on sonar point cloud, which can improve the accuracy of the boundary contour of the culvert.

[0004] In a first aspect, an embodiment of the present application provides a method for extracting a square culvert boundary contour based on a sonar point cloud, comprising: Acquire initial sonar acquisition data in the square culvert, wherein the initial sonar acquisition data includes intensity information of multiple frames of different scanning sections; Performing coordinate conversion processing on the intensity information of each frame to obtain cross-sectional point cloud data corresponding to the intensity information of each frame; The cross-section point cloud data is processed to remove center point noise to obtain overall boundary point cloud data; Performing DBSCAN clustering filtering on the overall boundary point cloud data to obtain target boundary point cloud data; Based on the local density of all points in the target boundary point cloud data, performing neighborhood analysis on the target boundary point cloud data to obtain a plurality of boundary points; Determine a target boundary point from all the boundary points based on a preset angle set, wherein the preset angle set includes a plurality of line angles, each of the line angles corresponds to at least one boundary point, the target boundary point is the boundary point that is farthest from the point cloud center in the target boundary point cloud data at the same line angle, and the line angle is the line angle between the boundary point and the point cloud center in the target boundary point cloud data; Linear interpolation processing is performed on all the target boundary points to obtain a target boundary contour for the square culvert.

[0005] In some embodiments, the cross-section point cloud data is processed to remove the center point noise to obtain the overall boundary point cloud data, including: Determine the number of point cloud points in the cross-section point cloud data and the first position of each point; Calculate the second position of the point cloud center of the cross-section point cloud data based on each of the first positions and the number of point cloud points; Based on the second position and all the first positions, calculating the Euclidean distance between each point in the cross-section point cloud data and the center of the point cloud; Points corresponding to the Euclidean distance less than the first distance threshold are deleted from the cross-section point cloud data to obtain the overall boundary point cloud data.

[0006] In some embodiments, the second position of the point cloud center of the cross-section point cloud data is calculated based on each of the first positions and the number of point cloud points, and is obtained according to the following formula: ; in, For the second position, is the first position, is the number of points in the point cloud, i is the index of each point in the cross-section point cloud data.

[0007] In some embodiments, the overall boundary point cloud data is subjected to DBSCAN clustering filtering to obtain target boundary point cloud data, including: When a neighborhood of a preset radius of a reference point in the overall boundary point cloud data includes at least a preset number of points, a point in the neighborhood whose distance from the reference point is less than the preset radius is determined as a core point, wherein the reference point is any point in the overall boundary point cloud data; Based on the preset radius, the preset number and each of the core points, a neighborhood expansion is performed on each of the core points to obtain a corresponding point cloud cluster, wherein the distance between the points other than the core point in each of the point cloud clusters and the core point is less than the preset radius, and the number of points other than the core point in each of the point cloud clusters is greater than or equal to the preset number; The points other than the point cloud cluster and the reference point in the overall boundary point cloud data are deleted to obtain the target boundary point cloud data.

[0008] In some embodiments, based on the local density of all points in the target boundary point cloud data, a neighborhood analysis is performed on the target boundary point cloud data to obtain a plurality of boundary points, including: Calculate the reference number of neighboring points of any point in the target boundary point cloud data within the range of a second distance threshold; Calculating the local density based on the second distance threshold, a third position of any point in the target boundary point cloud data, a fourth position of each neighboring point of any point in the target boundary point cloud data, and the reference quantity; The points in the target boundary point cloud data whose local density is lower than a preset density threshold are determined as the boundary points.

[0009] In some embodiments, the local density is calculated based on the second distance threshold, the third position of any point in the target boundary point cloud data, the fourth position of each adjacent point of any point in the target boundary point cloud data, and the reference quantity, and is obtained according to the following formula: ; in, is the position of the point in the target boundary point cloud data, is the local density corresponding to the points in the target boundary point cloud data, Ⅱ () is the indicator function, is the second distance threshold, for The adjacent points of the corresponding point, is the reference quantity, i for The index of the corresponding point, j for The index of the corresponding point.

[0010] In some embodiments, the connection angle is calculated according to the following formula: ; in, is the angle of the connecting line, ( ) is the coordinate of the boundary point in the plane, ( ) are the coordinates of the point cloud center in the target boundary point cloud data.

[0011] In some embodiments, linear interpolation is performed on all the target boundary points to obtain the target boundary contour for the square culvert, which is obtained according to the following formula: ; in, is the position corresponding to the target boundary point, is the interpolation parameter, Used to indicate arrive The linear interpolation ratio of is the position of a point linearly inserted in the target boundary contour except the target boundary point.

[0012] In a second aspect, an embodiment of the present application provides an electronic device comprising at least one control processor and a memory for communicating with the at least one control processor; the memory stores instructions executable by the at least one control processor, and the instructions are executed by the at least one control processor so that the at least one control processor can execute the square culvert boundary contour extraction method based on sonar point cloud as described in the first aspect.

[0013] In a third aspect, an embodiment of the present application further provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are used to execute the method for extracting the boundary contour of a square culvert based on sonar point cloud as described in the first aspect.

[0014] The embodiment of the present application provides a method, device, and medium for extracting the boundary contour of a square culvert based on sonar point clouds. The method comprises: obtaining initial sonar acquisition data in the square culvert, wherein the initial sonar acquisition data comprises intensity information of multiple frames of different scanning sections; performing coordinate conversion processing on the intensity information of each frame to obtain cross-sectional point cloud data corresponding to the intensity information of each frame; performing center point noise removal processing on the cross-sectional point cloud data to obtain overall boundary point cloud data; performing DBSCAN clustering filtering processing on the overall boundary point cloud data to obtain target boundary point cloud data; and performing a clustering filtering process based on all points in the target boundary point cloud data. Local density, neighborhood analysis is performed on the target boundary point cloud data to obtain multiple boundary points; the target boundary point is determined from all the boundary points based on a preset angle set, wherein the preset angle set includes multiple line angles, each line angle corresponds to at least one boundary point, and the target boundary point is the boundary point farthest from the point cloud center in the target boundary point cloud data at the same line angle, and the line angle is the line angle between the boundary point and the point cloud center in the target boundary point cloud data; linear interpolation is performed on all the target boundary points to obtain the target boundary contour for the square culvert. According to the scheme provided in the embodiment of the present application, the target boundary contour of the square culvert is obtained by performing coordinate transformation, denoising, clustering and other processing on the initial sonar data. Compared with the manually obtained scheme, the accuracy of the culvert boundary contour can be effectively improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a flowchart of the steps of a method for extracting the boundary contour of a square culvert based on sonar point cloud provided by an embodiment of the present application; Figure 2 It is a structural diagram of an electronic device provided in another embodiment of the present application. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0017] It is understood that although the functional modules are divided in the device schematic diagram and the logical order is shown in the flow chart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flow chart. The terms "first", "second", etc. in the specification, claims or the above drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0018] First, some nouns involved in this application are analyzed: Sonar (Sound Navigation And Ranging, Sonar): is a technology that uses sound waves to detect, locate and measure objects. It is widely used in underwater navigation, detection and communication. It calculates distance, depth and direction by emitting sound waves and receiving reflected signals. Sonar can be divided into active sonar and passive sonar. Active sonar actively emits sound waves and analyzes echoes, while passive sonar only receives external sounds and does not actively emit signals. Sonar has important application value in marine research, submarine navigation, fishing and military fields.

[0019] DBSCAN (Density-Based Spatial Clustering of Applications with Noise): is a density-based clustering algorithm that is used to divide data sets into clusters with similar densities and can effectively identify noise points. Unlike traditional K-means clustering, DBSCAN does not require a preset number of clusters, but defines the density of a cluster through two parameters: neighborhood radius eps and minimum number of samples min_samples. The algorithm determines whether a point is a core point by checking whether the neighborhood of each point contains enough points. If it is a core point, all points in its neighborhood are classified into the same cluster. If a point cannot be classified into any cluster, it is marked as a noise point. DBSCAN can discover clusters of arbitrary shapes and has good noise processing capabilities, but its performance is sensitive to parameter settings, especially when dealing with clusters of different densities.

[0020] Point cloud local density: refers to the distribution of other points in the neighborhood of a given point. The local density of a point is usually measured by calculating the number or distance of points within a certain radius around the point. Points with higher local density are usually located in dense areas, while points with lower density may be located in borders or sparse areas. This metric is often used to distinguish core points, border points, and noise points in point clouds, and is particularly important in clustering and denoising processes.

[0021] With the continuous development of underwater detection technology, underwater sonar equipment is increasingly used in culvert detection. In related technologies, underwater unmanned submersibles are used to carry out cross-sectional sonar surveys of the current status of culverts. By extracting point cloud boundaries from the collected sonar data, it can help accurately identify the surface of the structure, thereby achieving real-time assessment of the underwater environment. The existing method is to obtain the point cloud image through the acquisition equipment, and then manually mark the point cloud image to obtain the culvert boundary contour, which cannot guarantee the accuracy of the culvert boundary contour.

[0022] In order to solve the above-mentioned problems, the embodiments of the present application provide a method, device, and medium for extracting the boundary contour of a square culvert based on sonar point cloud, wherein the method comprises: obtaining initial sonar acquisition data in the square culvert, wherein the initial sonar acquisition data comprises intensity information of multiple frames of different scanning sections; performing coordinate conversion processing on the intensity information of each frame to obtain cross-sectional point cloud data corresponding to the intensity information of each frame; performing center point noise removal processing on the cross-sectional point cloud data to obtain overall boundary point cloud data; performing DBSCAN clustering filtering processing on the overall boundary point cloud data to obtain target boundary point cloud data; based on the target boundary point cloud data The local density of all points in the target boundary point cloud data is calculated, and a neighborhood analysis is performed on the target boundary point cloud data to obtain multiple boundary points; the target boundary point is determined from all the boundary points based on a preset angle set, wherein the preset angle set includes multiple line angles, each line angle corresponds to at least one boundary point, and the target boundary point is the boundary point that is farthest from the point cloud center in the target boundary point cloud data at the same line angle, and the line angle is the line angle between the boundary point and the point cloud center in the target boundary point cloud data; all the target boundary points are linearly interpolated to obtain the target boundary contour for the square culvert. According to the solution provided in the embodiment of the present application, the target boundary contour of the square culvert is obtained by performing coordinate transformation, denoising, clustering and other processing on the initial sonar data. Compared with the manually obtained solution, the accuracy of the culvert boundary contour can be effectively improved.

[0023] The embodiments of the present application are further described below in conjunction with the accompanying drawings.

[0024] refer to Figure 1 , Figure 1The present invention provides a method for extracting the boundary contour of a square pipe culvert based on sonar point cloud, which includes but is not limited to the following steps: Step S10, obtaining initial sonar acquisition data in the square culvert, wherein the initial sonar acquisition data includes intensity information of multiple frames of different scanning sections; Step S20, performing coordinate conversion processing on each frame of intensity information to obtain cross-sectional point cloud data corresponding to the intensity information of each frame; Step S30, removing the center point noise from the cross-section point cloud data to obtain the overall boundary point cloud data; Step S40, performing DBSCAN clustering filtering processing on the overall boundary point cloud data to obtain target boundary point cloud data; Step S50, performing neighborhood analysis on the target boundary point cloud data based on the local density of all points in the target boundary point cloud data to obtain a plurality of boundary points; Step S60, determining a target boundary point from all boundary points based on a preset angle set, wherein the preset angle set includes a plurality of line angles, each line angle corresponds to at least one boundary point, the target boundary point is a boundary point that is farthest from a point cloud center in the target boundary point cloud data corresponding to the same line angle, and the line angle is a line angle between the boundary point and the point cloud center in the target boundary point cloud data; Step S70: performing linear interpolation processing on all target boundary points to obtain a target boundary contour for the square culvert.

[0025] It can be understood that after obtaining the initial sonar acquisition data of the square culvert, the present application performs automated multi-stage processing on the initial sonar acquisition data, thereby obtaining the target boundary contour for the square culvert, providing an accurate and effective data basis for underwater culvert detection. Specifically, the present embodiment removes the center point noise and DBSCAN clustering filter processing on the cross-section point cloud data after coordinate conversion, so that the boundary point cloud data is more concentrated, and the target boundary point cloud data is obtained; then, the outermost target boundary points are extracted from the target boundary point cloud data in combination with the local density of the points and the preset angle set (i.e., angle grouping); finally, the target boundary points are linearly interpolated, and the boundary contour formed by the target boundary points is smoothed, so that the point cloud data is gradually simplified and refined to obtain a more accurate target boundary contour. Compared with the manually acquired solution, the present application can accurately and efficiently extract the pipeline boundary contour, providing accurate and efficient technical support for practical applications such as pipeline detection.

[0026] It can be understood that for step S60 in this embodiment, the target boundary point is determined from all the boundary points based on the preset angle set. The preset angle set corresponds to multiple connecting angles. This embodiment groups all the boundary points based on the connecting angle. Specifically, the boundary points located on the reference straight line corresponding to the connecting angle can be classified into the same angle group, or a reference area can be established with the reference straight line corresponding to the connecting angle and the preset angle, and the boundary points located in the reference area can be classified into the same angle group. It can be known that in any angle group, the point farthest from the center of the point cloud is the farthest boundary point, and the farthest boundary point is more in line with the target boundary contour to be obtained in this embodiment. Therefore, determining the point farthest from the center of the point cloud among the boundary points corresponding to each angle group as the target boundary point can provide an effective data basis for the subsequent acquisition of accurate target boundary contours.

[0027] It should be noted that the connection angle of this embodiment is calculated according to the following formula: ; in, is the line angle, ( ) is the coordinate of the boundary point in the plane, ( ) is the coordinate of the point cloud center in the target boundary point cloud data.

[0028] Specifically, in some embodiments, Figure 1 Step S30 includes but is not limited to the following steps: Step S31, determining the number of point cloud points in the cross-section point cloud data and the first position of each point; Step S32, calculating a second position of the point cloud center of the cross-section point cloud data based on each first position and the number of point cloud points; Step S33, based on the second position and all the first positions, calculating the Euclidean distance between each point in the cross-section point cloud data and the center of the point cloud; Step S34, deleting points corresponding to Euclidean distances less than a first distance threshold from the cross-section point cloud data to obtain overall boundary point cloud data.

[0029] It should be noted that, in this embodiment, the second position of the point cloud center of the cross-section point cloud data is calculated based on each first position and the number of point cloud points, and is obtained according to the following formula: ; in, For the second position, For the first position, is the number of point cloud points, i It is the index of each point in the cross-section point cloud data.

[0030] It should be noted that, in this embodiment, based on the second position and all the first positions, the Euclidean distance between each point in the cross-section point cloud data and the point cloud center is calculated, and is obtained according to the following formula:

[0031] in, It is the Euclidean distance between each point in the cross-section point cloud data and the center of the point cloud.

[0032] It can be understood that, in this embodiment, by calculating the position of the point cloud center in the cross-sectional point cloud data, that is, the second position, and calculating the Euclidean distance between each point in the cross-sectional point cloud data and the point cloud center, the points corresponding to the Euclidean distances less than the first distance threshold are deleted from the cross-sectional point cloud data, that is, the points that are too close to the center are removed. The points that are too close to the center can be considered to be far away from the boundary and cannot be points of the boundary contour, so that the invalid area can be removed, and then the overall boundary point cloud data can be obtained. It can also be selected with the smallest distance. n Points are removed to obtain the overall boundary point cloud data. n The value of is 50.

[0033] Specifically, in some embodiments, Figure 1 Step S40 includes but is not limited to the following steps: Step S41, when the neighborhood of the preset radius of the reference point in the overall boundary point cloud data includes at least a preset number of points, a point in the neighborhood whose distance from the reference point is less than the preset radius is determined as a core point, wherein the reference point is any point in the overall boundary point cloud data; Step S42, based on the preset radius, the preset number and each core point, the neighborhood of each core point is expanded to obtain a corresponding point cloud cluster, wherein the distance between the points other than the core point in each point cloud cluster and the core point is less than the preset radius, and the number of points other than the core point in each point cloud cluster is greater than or equal to the preset number; Step S43, deleting points other than the point cloud clusters and reference points in the overall boundary point cloud data to obtain target boundary point cloud data.

[0034] It can be understood that this embodiment uses the DBSCAN clustering filter processing algorithm to remove noise points, separate discrete points, and extract dense areas from the overall boundary point cloud data. Specifically, a preset radius ε and a preset number min_samples (i.e., the minimum number of points) are used as clustering parameters, wherein the preset radius ε determines the neighborhood range between points, and min_samples sets the minimum number of points required in a cluster. If a point p_i contains at least min_samples points in its neighborhood, and the distance between these points and p_i is less than ε, then the point is considered to be a core point. Next, based on the preset radius, preset number, and each core point, the neighborhood of each core point is expanded until all neighboring points are classified into the same cluster. If the number of points in the neighborhood of a point in the overall boundary point cloud data is less than min_samples, and the point does not have enough neighboring points to be classified into a cluster, it will be marked as a noise point (labeled as -1). These points are also the points in the overall boundary point cloud data other than the point cloud cluster and the reference point. The noise points in the overall boundary point cloud data are deleted to obtain clearer and more concentrated target boundary point cloud data, reduce the interference of noise points, and reduce the overall point cloud data volume, further improving the efficiency of obtaining the target boundary contour.

[0035] It should be noted that, in this embodiment, the value of the preset radius ε is 0.5, and the value of the preset number min_samples is 5.

[0036] Specifically, in some embodiments, Figure 1 Step S50 includes but is not limited to the following steps: Step S51, calculating the reference number of neighboring points of any point in the target boundary point cloud data within the range of the second distance threshold; Step S52, calculating the local density based on the second distance threshold, the third position of any point in the target boundary point cloud data, the fourth position of each adjacent point of any point in the target boundary point cloud data, and the reference quantity; Step S53: determining points in the target boundary point cloud data whose local density is lower than a preset density threshold as boundary points.

[0037] It should be noted that the local density is calculated based on the second distance threshold, the third position of any point in the target boundary point cloud data, the fourth position of each adjacent point of any point in the target boundary point cloud data, and the reference quantity, and is obtained according to the following formula: ; in, is the location of the point in the target boundary point cloud data, is the local density corresponding to the points in the target boundary point cloud data, Ⅱ () is the indicator function, is the second distance threshold, for The adjacent points of the corresponding point, is the reference quantity, i for The index of the corresponding point, j for The index of the corresponding point.

[0038] It should be noted that, in this embodiment, the value of the number of adjacent points for calculating the local density, that is, the value of the reference number, is 15, and the value of the preset density threshold is 0.08.

[0039] It can be understood that this embodiment uses the calculation of the local density of multiple points in the target boundary point cloud data to identify the boundary points based on whether it is lower than the preset density threshold. It can be known that in the target boundary point cloud data, the points whose local density is lower than the preset density threshold are more in line with the requirements corresponding to the target boundary contour. Therefore, after calculating the local density based on the second distance threshold, the third position of any point in the target boundary point cloud data, the fourth position of each adjacent point of any point in the target boundary point cloud data, and the reference number, the points in the target boundary point cloud data whose local density is lower than the preset density threshold are determined as boundary points.

[0040] Specifically, in this embodiment, in step S70, linear interpolation processing is performed on all target boundary points to obtain the target boundary contour for the square culvert, which is obtained according to the following formula: ; in, is the position corresponding to the target boundary point, is the interpolation parameter, Used to indicate arrive The linear interpolation ratio of The position of the point in the target boundary contour that is linearly interpolated except for the target boundary point.

[0041] It can be understood that after obtaining the target boundary points, linear interpolation processing is performed on the target boundary points, that is, a certain number of points are inserted between adjacent target boundary points to further smooth the contour and obtain boundary point cloud data after smoothing the contour, so that the outer boundary contour of the culvert is smoother and more accurate.

[0042] It should be noted that, in this embodiment, the number of insertion points between adjacent boundary points is 10.

[0043] like Figure 2 As shown, Figure 2 2 is a structural diagram of a control device provided in an embodiment of the present application. The present invention also provides a control device 200, including: The processor 210 may be implemented by a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application; The memory 220 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 220 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 220, and the processor 210 calls and executes the method for extracting the boundary contour of a square culvert based on sonar point cloud in the embodiment of this application; Input / output interface 230, used to implement information input and output; Communication interface 240, used to realize communication interaction between the apparatus and other devices, which can be realized by wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.); bus 250 , which transmits information between the various components of the device (e.g., processor 210 , memory 220 , input / output interface 230 , and communication interface 240 ); The processor 210 , the memory 220 , the input / output interface 230 , and the communication interface 240 are connected to each other in communication within the device via the bus 250 .

[0044] In addition, an embodiment of the present application further provides an electronic device, including the control device 200 of the above embodiment.

[0045] In addition, an embodiment of the present application further provides a storage medium, which is a computer-readable storage medium and stores a computer program. When the computer program is executed by a processor, the above-mentioned square culvert boundary contour extraction method based on sonar point cloud is implemented.

[0046] As a non-transient computer-readable storage medium, the memory can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage devices. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. The device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and are implemented to be located in one place, or may also be distributed to multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment.

[0047] It will be appreciated by those skilled in the art that all or some of the steps and systems in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or transient medium). As known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media typically include computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0048] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the above-mentioned implementation mode. Technical personnel familiar with the field can also make various equivalent deformations or substitutions under the shared conditions without violating the spirit of the present invention. These equivalent deformations or substitutions are all included in the scope defined by the claims of the present invention.

Claims

1. A method for extracting the boundary contour of a square culvert based on sonar point cloud, characterized in that: include: Acquire initial sonar acquisition data in the square culvert, wherein the initial sonar acquisition data includes intensity information of multiple frames of different scanning sections; Performing coordinate conversion processing on the intensity information of each frame to obtain cross-sectional point cloud data corresponding to the intensity information of each frame; The cross-section point cloud data is processed to remove center point noise to obtain overall boundary point cloud data; Performing DBSCAN clustering filtering on the overall boundary point cloud data to obtain target boundary point cloud data; Based on the local density of all points in the target boundary point cloud data, performing neighborhood analysis on the target boundary point cloud data to obtain a plurality of boundary points; Determine a target boundary point from all the boundary points based on a preset angle set, wherein the preset angle set includes a plurality of line angles, each of the line angles corresponds to at least one boundary point, the target boundary point is the boundary point that is farthest from the point cloud center in the target boundary point cloud data corresponding to the same line angle, and the line angle is the line angle between the boundary point and the point cloud center in the target boundary point cloud data; Linear interpolation processing is performed on all the target boundary points to obtain a target boundary contour for the square culvert.

2. The method for extracting the boundary contour of a square culvert based on sonar point cloud according to claim 1 is characterized in that: The cross-section point cloud data is processed to remove the center point noise to obtain the overall boundary point cloud data, including: Determine the number of point cloud points in the cross-section point cloud data and the first position of each point; Calculate the second position of the point cloud center of the cross-section point cloud data based on each of the first positions and the number of point cloud points; Based on the second position and all the first positions, calculating the Euclidean distance between each point in the cross-section point cloud data and the center of the point cloud; Points corresponding to the Euclidean distance less than the first distance threshold are deleted from the cross-section point cloud data to obtain the overall boundary point cloud data.

3. The method for extracting the boundary contour of a square culvert based on sonar point cloud according to claim 2 is characterized in that: The second position of the point cloud center of the cross-section point cloud data is calculated based on each of the first positions and the number of point cloud points, and is obtained according to the following formula: ； in, For the second position, is the first position, is the number of points in the point cloud, i is the index of each point in the cross-section point cloud data.

4. The method for extracting the boundary contour of a square culvert based on sonar point cloud according to claim 3 is characterized in that: The overall boundary point cloud data is subjected to DBSCAN clustering filtering processing to obtain target boundary point cloud data, including: When a neighborhood of a preset radius of a reference point in the overall boundary point cloud data includes at least a preset number of points, a point in the neighborhood whose distance from the reference point is less than the preset radius is determined as a core point, wherein the reference point is any point in the overall boundary point cloud data; Based on the preset radius, the preset number and each of the core points, a neighborhood expansion is performed on each of the core points to obtain a corresponding point cloud cluster, wherein the distance between the points other than the core point in each of the point cloud clusters and the core point is less than the preset radius, and the number of points other than the core point in each of the point cloud clusters is greater than or equal to the preset number; The points other than the point cloud cluster and the reference point in the overall boundary point cloud data are deleted to obtain the target boundary point cloud data.

5. The method for extracting the boundary contour of a square culvert based on sonar point cloud according to claim 1 is characterized in that: Based on the local density of all points in the target boundary point cloud data, a neighborhood analysis is performed on the target boundary point cloud data to obtain a plurality of boundary points, including: Calculate the reference number of neighboring points of any point in the target boundary point cloud data within the range of a second distance threshold; Calculating the local density based on the second distance threshold, a third position of any point in the target boundary point cloud data, fourth positions of each neighboring point of any point in the target boundary point cloud data, and the reference quantity; The points in the target boundary point cloud data whose local density is lower than a preset density threshold are determined as the boundary points.

6. The method for extracting the boundary contour of a square culvert based on sonar point cloud according to claim 5 is characterized in that: The local density is calculated based on the second distance threshold, the third position of any point in the target boundary point cloud data, the fourth position of each adjacent point of any point in the target boundary point cloud data, and the reference quantity, and is obtained according to the following formula: ； in, is the position of the point in the target boundary point cloud data, is the local density corresponding to the points in the target boundary point cloud data, Ⅱ () is the indicator function, is the second distance threshold, for The adjacent points of the corresponding point, is the reference quantity, i for The index of the corresponding point, j for The index of the corresponding point.

7. The method for extracting the boundary contour of a square culvert based on sonar point cloud according to claim 1, characterized in that: The connection angle is calculated according to the following formula: ； in, is the angle of the connecting line, ( ) is the coordinate of the boundary point in the plane, ( ) are the coordinates of the point cloud center in the target boundary point cloud data.

8. The method for extracting the boundary contour of a square culvert based on sonar point cloud according to claim 1, characterized in that: Linear interpolation is performed on all the target boundary points to obtain the target boundary contour for the square culvert, which is obtained according to the following formula: ； in, is the position corresponding to the target boundary point, is the interpolation parameter, Used to indicate arrive The linear interpolation ratio of is the position of a point linearly inserted in the target boundary contour except the target boundary point.

9. An electronic device, characterized in that: It includes at least one control processor and a memory for communicating with the at least one control processor; the memory stores instructions that can be executed by the at least one control processor, and the instructions are executed by the at least one control processor so that the at least one control processor can execute the square culvert boundary contour extraction method based on sonar point cloud as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the method for extracting the boundary contour of a square culvert based on sonar point cloud as described in any one of claims 1 to 8.

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