An optimization method, device, electronic device and storage medium for semantic segmentation of overhead line point clouds

Abstract

The present invention discloses an optimization method, device, electronic device, and storage medium for semantic segmentation of overhead line point clouds. The method includes: obtaining an original point cloud to be segmented; performing feature extraction and downsampling on the original point cloud to generate a depth point cloud; clustering the depth point cloud to generate a plurality of clusters, and using the cluster centers of the clusters as plane poles; if the number of plane poles is not zero, calculating the distance from each point in the original point cloud to each plane pole, and dividing the original point cloud according to the distance to generate a plurality of sub-point clouds; updating each sub-point cloud in turn to the current point cloud, performing a point cloud semantic segmentation operation to generate a corresponding segmentation result; using the segmentation result of each sub-point cloud as the segmentation result of the original point cloud; if the number of plane poles is zero, updating the original point cloud to the current point cloud, and performing a point cloud semantic segmentation operation to generate a segmentation result of the original point cloud. The implementation of the present invention can improve the accuracy of semantic segmentation of overhead line point clouds.

Description

Technical Field

[0001] The present invention relates to the field of power grid operation and maintenance technology, and in particular to an optimization method, device, electronic device and storage medium for semantic segmentation of overhead line point clouds. Background Art

[0002] With the rapid development of LiDAR technology, point cloud data is increasingly being used in overhead line inspections. Point cloud semantic segmentation assigns specific semantic categories to each point in the point cloud data, such as ground, tower, and conductor, to facilitate subsequent analysis and processing. In overhead line inspections, accurately segmenting the ground point cloud is crucial for tasks such as calculating safe distances and detecting tower tilt. This segmentation provides reliable data support for the management and maintenance of power facilities, ensuring line safety and efficiency.

[0003] However, existing technologies often fail to effectively distinguish between planar and non-planar point clouds when processing point cloud data, instead mixing them. This significantly reduces the accuracy of point cloud semantic segmentation. Planar point clouds typically have more regular and uniform features, while non-planar point clouds may contain more noise and complex structures. Without a clear distinction between these two types of point clouds, the segmentation algorithm will be interfered with by the mixed features, making it difficult to accurately identify different objects and features. In addition, noise and outliers in non-planar point clouds will further affect the quality of the overall data, leading to unreliable segmentation results. Summary of the Invention

[0004] The present invention provides an optimization method, device, electronic device, and storage medium for semantic segmentation of overhead line point clouds. The present invention can effectively distinguish between planar point clouds and non-planar point clouds, thereby improving the accuracy of semantic segmentation of overhead line point clouds.

[0005] An embodiment of the present invention provides an optimization method for semantic segmentation of an overhead line point cloud, comprising: obtaining an original point cloud of the overhead line to be segmented.

[0006] Perform feature extraction and downsampling operations on the original point cloud to generate a depth point cloud.

[0007] The depth point cloud is clustered to generate a plurality of clusters, and the cluster center of each cluster is set as a plane pole.

[0008] Determine the number of plane poles. If the number of plane poles is not zero, calculate the distance from each point in the original point cloud to each plane pole, divide the original point cloud according to the distance, and generate several sub-point clouds; update each sub-point cloud to the current point cloud in turn, perform point cloud semantic segmentation operation, and generate corresponding segmentation results; use the segmentation result of each sub-point cloud as the segmentation result of the original point cloud.

[0009] If the number of plane poles is 0, the original point cloud is updated to the current point cloud, and the point cloud semantic segmentation operation is performed to generate the segmentation result of the original point cloud.

[0010] The point cloud semantic segmentation operation includes:

[0011] Perform plane fitting on the current point cloud to generate the point cloud plane equation.

[0012] The current point cloud is divided by the point cloud plane equation to generate a planar point cloud and a non-planar point cloud; the planar point cloud is segmented by the point cloud plane equation to obtain a planar point cloud segmentation result.

[0013] The non-planar point cloud is input into the preset point cloud semantic segmentation neural network for segmentation to obtain the segmentation result of the non-planar point cloud.

[0014] The segmentation results of the planar point cloud and the segmentation results of the non-planar point cloud are used as the segmentation results of the current point cloud.

[0015] Furthermore, the feature extraction and downsampling operations are performed on the original point cloud to generate a depth point cloud, including:

[0016] Input the original point cloud into the multi-layer perceptron to perform feature extraction and generate the first feature point cloud;

[0017] Performing a downsampling operation on the first feature point cloud to generate a first downsampled point cloud;

[0018] Input the first downsampled point cloud into a multilayer perceptron for feature extraction to generate a second feature point cloud;

[0019] The second feature point cloud is downsampled to generate a second downsampled point cloud; and the second downsampled point cloud is used as the depth point cloud.

[0020] Furthermore, the original point cloud is divided according to the distance to generate a plurality of sub-point clouds, including:

[0021] For each point in the original point cloud, select the plane pole with the smallest distance to it as the target pole of the point;

[0022] The original point cloud is divided according to the target poles to generate several sub-point clouds.

[0023] Furthermore, performing plane fitting on the current point cloud to generate a point cloud plane equation includes:

[0024] Repeat the point cloud plane fitting operation until the proportion of inliers is less than the preset convergence threshold, and generate the point cloud plane equation;

[0025] The point cloud plane fitting operation includes:

[0026] Randomly select several points from the current point cloud as fitting points;

[0027] According to the fitting points, the parameters of the preset plane equation are solved to generate a specific plane equation;

[0028] Generate the first point cloud plane according to the specific plane equation;

[0029] Calculate the distances of all points in the current point cloud except the fitting point to the first point cloud plane, and compare each distance with a preset first distance threshold;

[0030] Recording points whose distance from the first point cloud plane is less than a preset first distance threshold as inliers;

[0031] Calculate the ratio of the number of inliers to the total number of points in the current point cloud to obtain the inlier ratio;

[0032] Compare the proportion of inliers with the preset convergence threshold;

[0033] When the proportion of inliers is less than a preset convergence threshold, the specific plane equation is used as the point cloud plane equation.

[0034] When the proportion of inliers exceeds the preset convergence threshold, several new points are randomly selected as fitting points.

[0035] Furthermore, dividing the current point cloud by the point cloud plane equation to generate a planar point cloud and a non-planar point cloud includes:

[0036] generating a second point cloud plane according to the point cloud plane equation;

[0037] Calculating the distance between each point of the current point cloud and the second point cloud plane, and comparing each distance with a preset first distance threshold;

[0038] combining points whose distance from the second point cloud plane is less than a preset first distance threshold as a plane point cloud;

[0039] The points whose distance from the second point cloud plane is not less than a preset first distance threshold are combined as a non-planar point cloud.

[0040] Based on the above method embodiments, the present invention provides corresponding device embodiments.

[0041] An embodiment of the present invention provides an optimization device for semantic segmentation of overhead line point clouds, comprising: an original point cloud acquisition module, a depth point cloud generation module, a plane pole generation module, a sub-point cloud generation module, a sub-point cloud segmentation module, an original point cloud segmentation module, and a point cloud semantic segmentation operation module.

[0042] The original point cloud acquisition module is used to acquire the original point cloud of the overhead line to be segmented.

[0043] The depth point cloud generation module is used to perform feature extraction and downsampling operations on the original point cloud to generate a depth point cloud.

[0044] The plane pole generation module is used to cluster the depth point cloud to generate a plurality of clusters, and set the cluster center of each cluster as a plane pole.

[0045] The sub-point cloud generation module is used to calculate the distance from each point in the original point cloud to each plane pole when the number of plane poles is not 0, divide the original point cloud according to the distance, and generate a plurality of sub-point clouds.

[0046] The sub-point cloud segmentation module is used to update each sub-point cloud to the current point cloud in sequence when generating several sub-point clouds, perform point cloud semantic segmentation operations, and generate corresponding segmentation results; and use the segmentation results of each sub-point cloud as the segmentation results of the original point cloud.

[0047] The original point cloud segmentation module is used to update the original point cloud to the current point cloud if the number of plane poles is 0, perform the point cloud semantic segmentation operation, and generate the segmentation result of the original point cloud.

[0048] The point cloud semantic segmentation operation module is used to perform plane fitting on the current point cloud to generate a point cloud plane equation; divide the current point cloud by the point cloud plane equation to generate a planar point cloud and a non-planar point cloud; segment the planar point cloud by the point cloud plane equation to obtain a segmentation result of the planar point cloud; input the non-planar point cloud into a preset point cloud semantic segmentation neural network for segmentation to obtain a segmentation result of the non-planar point cloud; and use the segmentation results of the planar point cloud and the non-planar point cloud as the segmentation results of the current point cloud.

[0049] Based on the above method embodiment, the present invention provides a corresponding electronic device embodiment.

[0050] An embodiment of the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for optimizing semantic segmentation of overhead line point clouds as described in any one of the above-mentioned method embodiments can be implemented.

[0051] Based on the above method embodiment, the present invention provides a corresponding storage medium embodiment.

[0052] An embodiment of the present invention provides a storage medium having a computer program stored thereon. When the computer program is executed by a processor, the optimization method for semantic segmentation of overhead line point clouds described in any one of the above method embodiments can be implemented.

[0053] Compared with the prior art, the present invention has the following beneficial effects:

[0054] Embodiments of the present invention provide an optimization method, apparatus, electronic device, and storage medium for semantic segmentation of overhead power line point clouds. The method obtains a raw point cloud to be segmented; performs feature extraction and downsampling on the raw point cloud to generate a depth point cloud; and clusters the depth point cloud to form several clusters. The center of each cluster is set as a plane vertex. The plane vertex serves as a reference point to facilitate subsequent distance calculation and region partitioning, ensuring that each sub-point cloud is clustered within a specific feature region. The distance from the raw point cloud to the plane vertex is calculated, and the raw point cloud is divided into multiple sub-point clouds, ensuring that each sub-point cloud is more closely clustered within a specific feature region. For each sub-point cloud, plane fitting is further employed to extract its planar and non-planar components, enabling different segmentation methods to be used for the planar and non-planar point clouds. Planar point clouds are segmented according to a regular pattern based on plane equations, while non-planar point clouds are segmented using a pre-defined point cloud semantic segmentation neural network. This ensures segmentation accuracy when processing complex structures, enabling more targeted data processing of regions with different characteristics and avoiding the loss of segmentation accuracy caused by mixed processing of point clouds with different characteristics. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a flowchart of an optimization method for semantic segmentation of overhead line point clouds provided by one embodiment of the present invention.

[0056] Figure 2 It is a flowchart of a point cloud semantic segmentation operation provided by one embodiment of the present invention.

[0057] Figure 3 It is a structural diagram of an optimization device for semantic segmentation of overhead line point clouds provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0058] 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.

[0059] like Figure 1As shown, an embodiment of the present invention provides an optimization method for semantic segmentation of overhead line point clouds, comprising at least the following steps:

[0060] Step S1, obtaining the original point cloud of the overhead line to be segmented;

[0061] Specifically, overhead lines are scanned using laser scanners or other sensors to capture 3D spatial information of their surroundings. This process typically involves a comprehensive scan of a specific area to ensure complete point cloud data, including the line itself and surrounding supporting structures, vegetation, and other relevant objects. The resulting raw point cloud data is high-density and high-precision, accurately reflecting the spatial position and shape of the overhead lines and providing a foundation for subsequent instance segmentation.

[0062] Step S2: perform feature extraction and downsampling operations on the original point cloud to generate a depth point cloud.

[0063] In a preferred embodiment, the feature extraction and downsampling operations are performed on the original point cloud to generate a depth point cloud, including:

[0064] The original point cloud is input into the multi-layer perceptron for feature extraction to generate the first feature point cloud.

[0065] A downsampling operation is performed on the first feature point cloud to generate a first downsampled point cloud.

[0066] The first downsampled point cloud is input into a multilayer perceptron for feature extraction to generate a second feature point cloud.

[0067] The second feature point cloud is downsampled to generate a second downsampled point cloud; and the second downsampled point cloud is used as the depth point cloud.

[0068] In a specific implementation, the original point cloud data is input into a multi-layer perceptron (MLP) for processing. Through its multiple hidden layers, the MLP effectively captures the spatial relationships and feature information of each point in the point cloud, thereby enabling feature extraction. The MLP generates a first feature point cloud. This feature point cloud contains richer geometric and topological information, reflecting the feature distribution and interrelationships of each point in the original point cloud. The first feature point cloud is then downsampled to reduce the point cloud density and computational complexity. Downsampling methods can employ random sampling, uniform sampling, or importance sampling to generate the first downsampled point cloud. This step helps reduce computational complexity while preserving important information. The first downsampled point cloud is then re-input into the MLP for feature extraction, generating a second feature point cloud. This additional feature extraction further enhances the point cloud's representational capabilities and captures higher-level feature information. The second feature point cloud is then downsampled to generate a second downsampled point cloud. This process also aims to reduce data volume, making subsequent processing more efficient. The second downsampled point cloud is used as the final depth point cloud. This deep point cloud not only contains data that has undergone multiple feature extractions and optimizations, but also effectively retains important structural information in the original point cloud, providing a good foundation for subsequent instance segmentation and analysis.

[0069] Step S3: clustering the depth point cloud to generate a number of clusters, and setting the cluster center of each cluster as a plane pole;

[0070] First, select a suitable clustering algorithm based on the characteristics of the depth point cloud and application requirements. Optional clustering algorithms include K-means, DBSCAN, and Mean Shift. Input the depth point cloud data into the selected clustering algorithm for processing. During the clustering process, the algorithm divides the points into several clusters based on the distance or similarity between the points in the point cloud. Each cluster represents a group of points that are close to each other in space and have similar features. After clustering is completed, the cluster center is calculated for each cluster. The cluster center can be obtained by averaging the coordinates of all points in the cluster. The cluster center represents the main feature position of the cluster and can effectively summarize the spatial distribution of points in the cluster. The cluster center of each cluster is set as the plane pole. These plane poles will serve as key reference points in subsequent processing to provide support for the subsequent point cloud semantic segmentation.

[0071] Step S4: Determine the number of plane poles. If the number of plane poles is not zero, calculate the distance from each point in the original point cloud to each plane pole, divide the original point cloud according to the distance, and generate several sub-point clouds; update each sub-point cloud to the current point cloud in turn, perform the point cloud semantic segmentation operation, and generate the corresponding segmentation result; use the segmentation result of each sub-point cloud as the segmentation result of the original point cloud. If the number of plane poles is zero, update the original point cloud to the current point cloud, perform the point cloud semantic segmentation operation, and generate the segmentation result of the original point cloud;

[0072] First, the number of detected plane poles is checked to determine the subsequent processing flow. If the number of detected plane poles is not zero, it indicates that the input point cloud contains uneven terrain. In this case, the distance from each point in the original point cloud to each plane pole is calculated. The original point cloud is then divided into several sub-point clouds based on these distances, so that the detailed features in each area can be processed separately. Each sub-point cloud is then used as the current point cloud for processing, and the point cloud semantic segmentation operation is performed on it to obtain the corresponding segmentation result. Finally, the segmentation results of each sub-point cloud are combined to form the overall segmentation result of the original point cloud.

[0073] If the number of plane extreme points is 0, it means that the input point cloud ground is flat. In this case, the original point cloud is directly updated to the current point cloud, and a one-time point cloud semantic segmentation operation is performed to obtain the complete segmentation result of the original point cloud.

[0074] In an optional embodiment, dividing the original point cloud according to the distance to generate a plurality of sub-point clouds includes:

[0075] For each point in the original point cloud, select the plane pole with the smallest distance to it as the target pole of the point;

[0076] The original point cloud is divided according to the target poles to generate several sub-point clouds.

[0077] In practice, for each point in the original point cloud, the distance between it and all plane poles is calculated. Usually, the Euclidean distance is used as the metric, and the calculation formula is:

[0078]

[0079] in, p is a point in the original point cloud, c is a certain plane pole;

[0080] By comparing the distances between all plane poles and the current point, the plane pole with the smallest distance is selected as the target pole for that point. For efficient processing, spatial data structures (such as KD trees) can be used to accelerate the nearest neighbor search and reduce calculation time. Each point and its corresponding target pole are associated to form a one-to-one mapping relationship. Each point in the original point cloud has a specific target pole, which allows the information in the point cloud to be structured for subsequent processing. The original point cloud is divided according to the target pole, and points with the same target pole are grouped into the same sub-point cloud, thereby generating several sub-point clouds.

[0081] like Figure 2 As shown, in a preferred embodiment, the point cloud semantic segmentation operation includes:

[0082] Step S4.1: Perform plane fitting on the current point cloud to generate the point cloud plane equation.

[0083] Step S4.2: Divide the current point cloud by the point cloud plane equation to generate a planar point cloud and a non-planar point cloud; segment the planar point cloud by the point cloud plane equation to obtain a planar point cloud segmentation result.

[0084] Step S4.3: Input the non-planar point cloud into the preset point cloud semantic segmentation neural network for segmentation to obtain the segmentation result of the non-planar point cloud.

[0085] Step S4.4: Use the segmentation results of the planar point cloud and the segmentation results of the non-planar point cloud as the current point cloud segmentation results.

[0086] It should be noted that the plane fitting of the current point cloud to generate the point cloud plane equation includes:

[0087] Repeat the point cloud plane fitting operation until the proportion of inliers is less than the preset convergence threshold, and generate the point cloud plane equation;

[0088] The point cloud plane fitting operation includes:

[0089] Randomly select several points from the current point cloud as fitting points;

[0090] According to the fitting points, the parameters of the preset plane equation are solved to generate a specific plane equation;

[0091] Generate the first point cloud plane according to the specific plane equation;

[0092] Calculate the distances of all points in the current point cloud except the fitting point to the first point cloud plane, and compare each distance with a preset first distance threshold;

[0093] Recording points whose distance from the first point cloud plane is less than a preset first distance threshold as inliers;

[0094] Calculate the ratio of the number of inliers to the total number of points in the current point cloud to obtain the inlier ratio;

[0095] Compare the proportion of inliers with the preset convergence threshold;

[0096] When the proportion of inliers is less than a preset convergence threshold, the specific plane equation is used as the point cloud plane equation.

[0097] When the proportion of inliers exceeds the preset convergence threshold, several new points are randomly selected as fitting points.

[0098] In one embodiment, dividing the current point cloud by the point cloud plane equation to generate a planar point cloud and a non-planar point cloud includes:

[0099] generating a second point cloud plane according to the point cloud plane equation;

[0100] Calculating the distance between each point of the current point cloud and the second point cloud plane, and comparing each distance with a preset first distance threshold;

[0101] combining points whose distance from the second point cloud plane is less than a preset first distance threshold as a plane point cloud;

[0102] The points whose distance from the second point cloud plane is not less than a preset first distance threshold are combined as a non-planar point cloud.

[0103] Select a suitable plane model based on the characteristics of the point cloud. In one embodiment, the plane model can be in the form of a parameterized plane equation:

[0104] Ax+By+Cz+D=0

[0105] Among them, A, B, C and D are the parameters to be fitted, and x, y, z are the coordinates of the point cloud.

[0106] The parameters of the plane equation are estimated using an iterative algorithm using a randomly selected set of points. This process continuously adjusts the model parameters to minimize the sum of the distances from the points to the fitted plane, thereby generating an accurate plane equation. For example, three points (x1, y1, z1), (x2, y2, z2), and (x3, y3, z3) are randomly selected and the plane parameters A, B, C, and D are solved using the following system of equations:

[0107] Then, the distance from other points to the plane is calculated. If the distance is less than the set threshold, the point is considered to conform to the plane model. In this solution, the threshold value is selected as 0.3 meters.

[0108]

[0109] Then, the distance from other points to the plane is calculated. If the distance is less than the set threshold, the point is considered to conform to the plane model. In this solution, the threshold value is selected as 0.3 meters.

[0110] Specifically, according to the point cloud plane equation, for each point, its distance to the plane is calculated. If the distance is less than the set threshold, the point is considered a planar point; otherwise, it is considered a non-planar point. According to the above classification, the current point cloud is divided into two subsets: planar point cloud and non-planar point cloud. The planar point cloud contains all points that match the plane equation, while the non-planar point cloud contains all points that fail to conform to the plane model. The planar point cloud is further processed using the point cloud plane equation to generate the segmentation result of the planar point cloud. For non-planar point clouds, the non-planar point cloud is used as input and fed into a pre-trained point cloud semantic segmentation neural network. The network will output the segmentation result of the non-planar point cloud.

[0111] Based on the above method embodiments, the present invention provides corresponding device embodiments.

[0112] like Figure 3 As shown, an embodiment of the present invention provides an optimization device for semantic segmentation of overhead line point clouds, including: an original point cloud acquisition module 101, a depth point cloud generation module 102, a plane pole generation module 103, a sub-point cloud generation module 104, a sub-point cloud segmentation module 105, an original point cloud segmentation module 106 and a point cloud semantic segmentation operation module 107.

[0113] The original point cloud acquisition module 101 is used to acquire the original point cloud of the overhead line to be segmented;

[0114] The depth point cloud generation module 102 is used to perform feature extraction and downsampling operations on the original point cloud to generate a depth point cloud;

[0115] The plane pole generating module 103 is used to cluster the depth point cloud to generate a plurality of clusters, and set the cluster center of each cluster as a plane pole;

[0116] The sub-point cloud generating module 104 is configured to calculate the distance between each point in the original point cloud and each plane pole when the number of plane poles is not zero, and to divide the original point cloud according to the distance to generate a plurality of sub-point clouds;

[0117] The sub-point cloud segmentation module 105 is used to update each sub-point cloud to the current point cloud in sequence when generating multiple sub-point clouds, perform point cloud semantic segmentation operations, and generate corresponding segmentation results; and use the segmentation results of each sub-point cloud as the segmentation results of the original point cloud;

[0118] The original point cloud segmentation module 106 is configured to update the original point cloud to the current point cloud if the number of plane extreme points is 0, perform a point cloud semantic segmentation operation, and generate a segmentation result of the original point cloud;

[0119] The point cloud semantic segmentation operation module 107 is used to perform plane fitting on the current point cloud to generate a point cloud plane equation; divide the current point cloud by the point cloud plane equation to generate a planar point cloud and a non-planar point cloud; segment the planar point cloud by the point cloud plane equation to obtain a segmentation result of the planar point cloud; input the non-planar point cloud into a preset point cloud semantic segmentation neural network for segmentation to obtain a segmentation result of the non-planar point cloud; and use the segmentation results of the planar point cloud and the non-planar point cloud as the segmentation results of the current point cloud.

[0120] In a preferred embodiment, the depth point cloud generation module includes: a first feature point cloud generation unit, a first down-sampling point cloud generation unit, a second feature point cloud generation unit, and a second down-sampling point cloud generation unit;

[0121] The first feature point cloud generating unit is used to input the original point cloud into the multi-layer perceptron to perform feature extraction and generate a first feature point cloud;

[0122] The first down-sampling point cloud generating unit is configured to perform a down-sampling operation on the first feature point cloud to generate a first down-sampling point cloud;

[0123] The second feature point cloud generating unit is configured to input the first downsampled point cloud into a multilayer perceptron to perform feature extraction and generate a second feature point cloud;

[0124] The second down-sampling point cloud generating unit is configured to perform a down-sampling operation on the second feature point cloud to generate a second down-sampling point cloud; and use the second down-sampling point cloud as a depth point cloud.

[0125] It should be noted that the embodiments of the device described above correspond to the above-mentioned embodiments of the present invention, and can implement any of the methods described above in the present invention. In addition, the embodiments of the above-mentioned device are merely schematic, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the drawings of the embodiment of the device provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which can be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement it without paying any creative work.

[0126] Based on the above method embodiment of the present invention, a corresponding electronic device embodiment is provided.

[0127] An embodiment of the present invention provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the optimization method for semantic segmentation of overhead line point clouds described in any one of the present inventions is implemented, or when the processor executes the computer program, the functions of the modules in the above-mentioned device embodiments are implemented.

[0128] Exemplarily, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in the terminal device.

[0129] The terminal device may be a computing device such as a desktop computer, a notebook computer, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0130] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, connecting various parts of the entire terminal device using various interfaces and lines.

[0131] The memory can be used to store the computer programs and / or modules, and the processor implements various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created based on the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0132] Based on the above method embodiment, the present invention provides a corresponding storage medium embodiment;

[0133] Another embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute any of the above-mentioned optimization methods for semantic segmentation of overhead line point clouds of the present invention.

[0134] The above-mentioned storage medium is a computer-readable storage medium, and the computer program includes computer program code, which may be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0135] In the description of this specification, the reference terms "one embodiment," "some embodiments," "example," "specific example," or "some examples" mean 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 application. Moreover, the specific features, structures, materials, or characteristics described may be combined in any appropriate manner in any one or more embodiments or examples. In addition, those skilled in the art may combine and integrate different embodiments or examples described in this specification, as well as features of different embodiments or examples, unless they are mutually inconsistent.

[0136] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. An optimization method for semantic segmentation of overhead line point clouds, characterized in that: include: Obtain the original point cloud of the overhead line to be segmented; Perform feature extraction and downsampling operations on the original point cloud to generate a depth point cloud; Clustering the depth point cloud to generate a plurality of clusters, and setting the cluster center of each cluster as a plane pole; Determine the number of plane poles. If the number of plane poles is not zero, calculate the distance from each point in the original point cloud to each plane pole, divide the original point cloud according to the distance, and generate several sub-point clouds; update each sub-point cloud to the current point cloud in turn, perform point cloud semantic segmentation operations, and generate corresponding segmentation results; use the segmentation results of each sub-point cloud as the segmentation results of the original point cloud; If the number of plane extreme points is 0, update the original point cloud to the current point cloud, perform the point cloud semantic segmentation operation, and generate the segmentation result of the original point cloud; The point cloud semantic segmentation operation includes: Perform plane fitting on the current point cloud to generate the point cloud plane equation; Dividing the current point cloud by the point cloud plane equation to generate a planar point cloud and a non-planar point cloud; segmenting the planar point cloud by the point cloud plane equation to obtain a planar point cloud segmentation result; Input the non-planar point cloud into the preset point cloud semantic segmentation neural network for segmentation to obtain the segmentation result of the non-planar point cloud; The segmentation results of the planar point cloud and the segmentation results of the non-planar point cloud are used as the segmentation results of the current point cloud.

2. The optimization method for semantic segmentation of overhead line point clouds according to claim 1, characterized in that: The feature extraction and downsampling operations are performed on the original point cloud to generate a depth point cloud, including: Input the original point cloud into the multi-layer perceptron to perform feature extraction and generate the first feature point cloud; Performing a downsampling operation on the first feature point cloud to generate a first downsampled point cloud; Input the first downsampled point cloud into a multilayer perceptron for feature extraction to generate a second feature point cloud; The second feature point cloud is downsampled to generate a second downsampled point cloud; and the second downsampled point cloud is used as the depth point cloud.

3. The optimization method for semantic segmentation of overhead line point clouds according to claim 2, characterized in that: The dividing the original point cloud according to the distance to generate a plurality of sub-point clouds includes: For each point in the original point cloud, select the plane pole with the smallest distance to it as the target pole of the point; The original point cloud is divided according to the target poles to generate several sub-point clouds.

4. The optimization method for semantic segmentation of overhead line point clouds according to claim 3, characterized in that: The performing plane fitting on the current point cloud to generate the point cloud plane equation includes: Repeat the point cloud plane fitting operation until the proportion of inliers is less than the preset convergence threshold, and generate the point cloud plane equation; The point cloud plane fitting operation includes: Randomly select several points from the current point cloud as fitting points; According to the fitting points, the parameters of the preset plane equation are solved to generate a specific plane equation; Generate the first point cloud plane according to the specific plane equation; Calculate the distances of all points in the current point cloud except the fitting point to the first point cloud plane, and compare each distance with a preset first distance threshold; Recording points whose distance from the first point cloud plane is less than a preset first distance threshold as inliers; Calculate the ratio of the number of inliers to the total number of points in the current point cloud to obtain the inlier ratio; Compare the proportion of inliers with the preset convergence threshold; When the proportion of inliers is less than a preset convergence threshold, the specific plane equation is used as the point cloud plane equation; When the proportion of inliers exceeds the preset convergence threshold, several new points are randomly selected as fitting points.

5. The optimization method for semantic segmentation of overhead line point clouds according to claim 4, characterized in that: The step of dividing the current point cloud by the point cloud plane equation to generate a planar point cloud and a non-planar point cloud includes: generating a second point cloud plane according to the point cloud plane equation; Calculating the distance between each point of the current point cloud and the second point cloud plane, and comparing each distance with a preset first distance threshold; combining points whose distance from the second point cloud plane is less than a preset first distance threshold as a plane point cloud; The points whose distance from the second point cloud plane is not less than a preset first distance threshold are combined as a non-planar point cloud.

6. An optimization device for semantic segmentation of overhead line point clouds, characterized in that: include: Original point cloud acquisition module, depth point cloud generation module, plane pole generation module, sub-point cloud generation module, sub-point cloud segmentation module, original point cloud segmentation module and point cloud semantic segmentation operation module; The original point cloud acquisition module is used to acquire the original point cloud to be segmented of the overhead line; The depth point cloud generation module is used to perform feature extraction and downsampling operations on the original point cloud to generate a depth point cloud; The plane pole generation module is used to cluster the depth point cloud to generate a plurality of clusters, and set the cluster center of each cluster as a plane pole; The sub-point cloud generation module is used to calculate the distance between each point in the original point cloud and each plane pole when the number of plane poles is not zero, and divide the original point cloud according to the distance to generate a plurality of sub-point clouds; The sub-point cloud segmentation module is used to update each sub-point cloud to the current point cloud in sequence when generating multiple sub-point clouds, perform point cloud semantic segmentation operations, and generate corresponding segmentation results; the segmentation results of each sub-point cloud are used as the segmentation results of the original point cloud; The original point cloud segmentation module is used to update the original point cloud to the current point cloud if the number of plane extreme points is 0, perform the point cloud semantic segmentation operation, and generate the segmentation result of the original point cloud; The point cloud semantic segmentation operation module is used to perform plane fitting on the current point cloud to generate a point cloud plane equation; and divide the current point cloud by the point cloud plane equation to generate a planar point cloud and a non-planar point cloud; The planar point cloud is segmented by the point cloud plane equation to obtain the segmentation result of the planar point cloud; the non-planar point cloud is input into the preset point cloud semantic segmentation neural network for segmentation to obtain the segmentation result of the non-planar point cloud; The segmentation results of the planar point cloud and the segmentation results of the non-planar point cloud are used as the segmentation results of the current point cloud.

7. The device for optimizing semantic segmentation of overhead line point clouds according to claim 6, wherein: The depth point cloud generation module includes: a first feature point cloud generation unit, a first down-sampling point cloud generation unit, a second feature point cloud generation unit, and a second down-sampling point cloud generation unit; The first feature point cloud generating unit is used to input the original point cloud into the multi-layer perceptron to perform feature extraction and generate a first feature point cloud; The first down-sampling point cloud generating unit is configured to perform a down-sampling operation on the first feature point cloud to generate a first down-sampling point cloud; The second feature point cloud generating unit is configured to input the first downsampled point cloud into a multilayer perceptron to perform feature extraction and generate a second feature point cloud; The second down-sampling point cloud generating unit is configured to perform a down-sampling operation on the second feature point cloud to generate a second down-sampling point cloud; and use the second down-sampling point cloud as a depth point cloud.

8. The device for optimizing semantic segmentation of overhead line point clouds according to claim 7, wherein: The sub-point cloud generation module divides the original point cloud according to the distance to generate a plurality of sub-point clouds, including: For each point in the original point cloud, select the plane pole with the smallest distance to it as the target pole of the point; The original point cloud is divided according to the target poles to generate several sub-point clouds.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, it can implement the optimization method for semantic segmentation of overhead line point clouds as described in any one of claims 1 to 5.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it can implement the optimization method for semantic segmentation of overhead line point clouds as described in any one of claims 1 to 5.

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