Branch and leaf separation method and system for vegetation point cloud around power transmission line

Abstract

The invention provides a branch and leaf separation method and system for vegetation point cloud around a power transmission line, and relates to the technical field of data processing. According to the implementation scheme, based on the curvature of each point cloud in surrounding vegetation point clouds of a target power transmission line, the surrounding vegetation point clouds are divided into a first branch point cloud set and a first leaf point cloud set; performing connected domain segmentation on the first branch point cloud set to obtain a plurality of connected components; dividing the first branch point cloud set into a second branch point cloud set and a branch and leaf mixed point cloud set based on the linear feature significance degree of the geometric distribution feature of each connected component; taking each branch point in the second branch point cloud set as a seed point, and dividing the branch and leaf mixed point cloud set into a regional growing point cloud set and a non-regional growing point cloud set through regional growing; determining a target branch point cloud set based on a union set of the second branch point cloud set and the regional growing point cloud set; and determining a target leaf point cloud set based on a union set of the first leaf point cloud set and the non-regional growing point cloud set.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method and system for separating branches and leaves from a vegetation point cloud around a power transmission line. Background Art

[0002] With the continuous development of modern power systems, the safety and reliability of transmission lines have become a key technical research direction. With its high-precision three-dimensional data acquisition capabilities, LiDAR technology has gradually become a core tool in the field of transmission channel monitoring. By acquiring point cloud data around transmission lines, researchers are able to perform detailed modeling of vegetation and infrastructure in complex geographical environments. This detailed modeling is crucial to ensuring the safe operation of transmission lines because it can help identify potential risk factors such as excessive vegetation growth or infrastructure damage.

[0003] However, in the processing of vegetation point cloud data, separating the branches and leaves of vegetation (such as trees) faces great challenges. The complexity and unstructured characteristics of the point cloud data around the transmission line make it difficult for traditional analysis methods to accurately identify and extract the branch and leaf structure. The spatial distribution of the data is complex and the noise interference is significant. In addition, the diversity and dynamic changes of tree morphology further increase the difficulty of accurate separation.

[0004] In addition, the noise and occlusion effects in the point cloud data also significantly increase the difficulty of separating tree branches and leaves. The complex three-dimensional structure of trees, the entanglement of branches and leaves, and the morphological differences between different tree species have brought severe challenges to existing separation algorithms. Traditional methods are prone to misclassification when dealing with small branches and leaves, which affects the accuracy of subsequent analysis of the ecosystem near the transmission line. Moreover, this not only leads to deviations in data analysis results, but may also have an adverse impact on subsequent decision support systems. Summary of the invention

[0005] The present invention provides a method and system for separating branches and leaves from a point cloud of vegetation around a transmission line, which can solve at least one of the above technical problems.

[0006] According to one aspect of the present invention, a method for separating branches and leaves from a vegetation point cloud around a transmission line is provided, comprising: Based on the curvature of each point cloud in the surrounding vegetation point cloud of the target transmission line, the surrounding vegetation point cloud is divided into a first branch point cloud set and a first leaf point cloud set; Performing connected domain segmentation on the first branch point cloud set to obtain multiple connected components; Based on the linear feature significance of the geometric distribution features of each of the connected components, the first branch point cloud set is divided into a second branch point cloud set and a branch-leaf mixed point cloud set; Taking each branch point in the second branch point cloud set as a seed point, dividing the branch-leaf mixed point cloud set into a regional growth point cloud set and a non-regional growth point cloud set by regional growing; Determining a target branch point cloud set of the target transmission line based on a union of the second branch point cloud set and the regional growth point cloud set; Based on the union of the first blade point cloud set and the non-regional growth point cloud set, a target blade point cloud set of the target transmission line is determined.

[0007] According to another aspect of the present invention, a device for separating branches and leaves from a point cloud of vegetation around a power transmission line is provided, the method comprising: A first point cloud classification module, used for dividing the surrounding vegetation point cloud of the target transmission line into a first branch point cloud set and a first leaf point cloud set based on the curvature of each point cloud in the surrounding vegetation point cloud of the target transmission line; A connected domain segmentation module, used for performing connected domain segmentation on the first branch point cloud set to obtain a plurality of connected components; A second point cloud classification module is used to divide the first branch point cloud set into a second branch point cloud set and a branch-leaf mixed point cloud set based on the linear feature significance of the geometric distribution features of each of the connected components; A third point cloud classification module is used to use each branch point in the second branch point cloud set as a seed point, and divide the branch and leaf mixed point cloud set into a regional growth point cloud set and a non-regional growth point cloud set by regional growing; A first union module, configured to determine a target branch point cloud set of the target transmission line based on a union of the second branch point cloud set and the regional growth point cloud set; The second union module is used to determine the target blade point cloud set of the target transmission line based on the union of the first blade point cloud set and the non-regional growth point cloud set.

[0008] By adopting the technical solution of the present invention, the surrounding vegetation point cloud of the target transmission line is divided into a first branch point cloud set and a first leaf point cloud set based on the curvature of each point cloud in the surrounding vegetation point cloud. In this way, the surrounding vegetation point cloud of the transmission line is classified into branches and leaves point by point. Then, the first branch point cloud set is segmented into a connected domain to obtain multiple connected components; based on the linear feature significance of the geometric distribution characteristics of each connected component, the first branch point cloud set is divided into a second branch point cloud set and a branch and leaf mixed point cloud set. In this way, the branch point cloud is further classified into branches and leaves section by section. Moreover, each branch point in the second branch point cloud set is used as a seed point, and the branch and leaf mixed point cloud set is divided into a regional growth point cloud set and a non-regional growth point cloud set through regional growth. In this way, the branch and leaf mixed points are classified point by point again. Finally, the target branch point cloud set of the target transmission line is determined based on the union of the second branch point cloud set and the regional growth point cloud set; the target leaf point cloud set of the target transmission line is determined based on the union of the first leaf point cloud set and the non-regional growth point cloud set. Therefore, the comprehensive use of the point-by-point and segment-by-segment classification strategies can accurately separate branches and leaves from complex vegetation structures and reduce the occurrence of misclassification.

[0009] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The accompanying drawings are used to better understand the present invention and do not constitute a limitation of the present invention. Figure 1 It is a flow chart of a method for separating branches and leaves of vegetation point clouds around a power transmission line according to an embodiment of the present invention; Figure 2 is a flow chart of a method for separating branches and leaves of vegetation point clouds around a power transmission line according to another embodiment of the present invention; Figure 3A is a schematic diagram of an original point cloud according to an embodiment of the present invention; Figure 3B is a schematic diagram of a first branch point cloud according to an embodiment of the present invention; Figure 3C is a schematic diagram of a first blade point cloud set according to an embodiment of the present invention; Figure 4 It is a structural block diagram of a branch and leaf separation device for vegetation point cloud around a transmission line according to an embodiment of the present invention; Figure 5 The block diagram is a block diagram of an electronic device for implementing the method according to the embodiment of the present invention. DETAILED DESCRIPTION

[0011] The following is a description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be appreciated by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope of the present invention. Similarly, for clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0012] Figure 1 It is a flow chart of a method for separating branches and leaves of a vegetation point cloud around a transmission line according to an embodiment of the present invention.

[0013] like Figure 1 As shown, the branch and leaf separation method for the vegetation point cloud around the transmission line may include: S110, dividing the surrounding vegetation point cloud of the target transmission line into a first branch point cloud set and a first leaf point cloud set based on the curvature of each point cloud in the surrounding vegetation point cloud of the target transmission line; S120, performing connected domain segmentation on the first branch point cloud to obtain multiple connected components; S130, dividing the first branch point cloud set into a second branch point cloud set and a branch-leaf mixed point cloud set based on the linear feature significance of the geometric distribution features of each connected component; S140, using each branch point in the second branch point cloud set as a seed point, dividing the branch-leaf mixed point cloud set into a regional growth point cloud set and a non-regional growth point cloud set by regional growing; S150, determining a target branch point cloud set of the target transmission line based on the union of the second branch point cloud set and the regional growth point cloud set; S160: Determine a target blade point cloud set of the target transmission line based on the union of the first blade point cloud set and the non-regional growth point cloud set.

[0014] Exemplarily, the surrounding vegetation point cloud of the target transmission line may be a point cloud within a specified neighborhood.

[0015] For example, the surface normal change rate can be used to calculate the curvature NCR of each point cloud. It describes the change of the point cloud along the surface normal and can reflect the degree to which the point cloud deviates from the tangent plane. Using the curvature of each point cloud, each point cloud can be classified as a branch point cloud or a leaf point cloud. If it is classified as a branch point cloud, the point cloud is added to the first branch point cloud set. If it is classified as a leaf point cloud, the point cloud is added to the first leaf point cloud set.

[0016] Exemplarily, a connected domain refers to a group of points connected by a neighborhood. For a three-dimensional point cloud, if two points are close enough to each other in space, for example, within a certain threshold range, the two points are determined to be adjacent, and then a connected region can be formed by connecting these adjacent points, and the connected region is used as a connected component. In this way, the first branch point cloud set can be divided into multiple connected components. Each connected component includes multiple adjacent point clouds.

[0017] Exemplarily, the geometric distribution characteristics of connected components may include linear characteristics, planar characteristics and scattering characteristics. For different connected components, their geometric distribution characteristics are different. For example, for the connected components of the branch category, the significance of its linear characteristics is higher, while the significance of the planar characteristics and scattering characteristics is lower. For the connected components of the leaf category or the mixed category of branches and leaves, the significance of its linear characteristics is lower, while the significance of the planar characteristics and scattering characteristics is higher.

[0018] Therefore, the significance of the linear features in the geometric distribution features of the connected components can be used to determine whether the connected component is a branch category. If it is a branch category, the connected component is added to the second branch point cloud set. If it is not a branch category, the connected component is added to the branch-leaf mixed point cloud set. The point cloud in the branch-leaf mixed point cloud set is then separated into branches and leaves.

[0019] Exemplarily, each branch point in the second branch point cloud set is used as a seed point, and each point cloud in the branch-leaf mixed point cloud set is identified by regional growth, so that the regional growth point identified as the seed point is added to the regional growth point cloud set, and the regional growth point is deleted from the branch-leaf mixed point cloud set. After each seed point has completed the identification of each point cloud in the branch-leaf mixed point cloud set, the remaining point clouds in the branch-leaf mixed point cloud set are added to the non-regional growth point cloud set.

[0020] Exemplarily, by calculating the normal vector angle between the seed point and any point cloud in the branch-leaf mixed point cloud set, it is determined whether the point cloud is a regional growth point of the seed point.

[0021] Exemplarily, by calculating the distance between the seed point and any point cloud in the branch-leaf mixed point cloud set, it can be determined whether the point cloud is a regional growth point of the seed point.

[0022] Exemplarily, the union of the second branch point cloud set and the regional growth point cloud set is used as the target branch point cloud set of the target transmission line.

[0023] Exemplarily, the union of the first blade point cloud set and the non-regional growth point cloud set is used as the target blade point cloud set of the target transmission line.

[0024] For example, before step S140, the first leaf point cloud set may be merged into the branch-leaf mixed point cloud set, and the first leaf point cloud set may be changed into an empty set, and then step S140 may be performed. Furthermore, in step S160, the non-regional growth point cloud set is actually used as the target leaf point cloud set.

[0025] Exemplarily, based on the target branch point cloud set and the target leaf point cloud set, a target tree can be accurately constructed, and thus, by analyzing the target tree, it can be evaluated whether the target tree will cause power transmission hazards to the target transmission line.

[0026] According to the above-mentioned implementation, based on the curvature of each point cloud in the surrounding vegetation point cloud of the target transmission line, the surrounding vegetation point cloud is divided into a first branch point cloud set and a first leaf point cloud set. In this way, the surrounding vegetation point cloud of the transmission line is classified into branches and leaves point by point. Then, the first branch point cloud set is segmented into a connected domain to obtain a plurality of connected components; based on the linear feature significance of the geometric distribution features of each connected component, the first branch point cloud set is divided into a second branch point cloud set and a branch and leaf mixed point cloud set. In this way, the branch point cloud is further classified into branches and leaves section by section. Moreover, with each branch point in the second branch point cloud set as a seed point, the branch and leaf mixed point cloud set is divided into a regional growth point cloud set and a non-regional growth point cloud set through regional growth. In this way, the branch and leaf mixed points are classified point by point again. Finally, the target branch point cloud set of the target transmission line is determined based on the union of the second branch point cloud set and the regional growth point cloud set; the target leaf point cloud set of the target transmission line is determined based on the union of the first leaf point cloud set and the non-regional growth point cloud set. Therefore, the comprehensive use of the point-by-point and segment-by-segment classification strategies can accurately separate branches and leaves for complex vegetation structures and reduce the occurrence of misclassification. In one embodiment, the above method may also include: determining the covariance matrix of each point cloud based on the position information of the geometric center of the target transmission line in the surrounding vegetation point cloud within a specified neighborhood, and the position information of each point cloud in the surrounding vegetation point cloud; performing singular value decomposition on the covariance matrix of each point cloud to obtain the singular eigenvalues ​​of each point cloud; and determining the curvature of each point cloud based on the singular eigenvalues ​​of each point cloud.

[0027] For example, for a point p i = (x i ,y i ,z i ), its covariance matrix C is defined as follows: ; in, is the geometric center of neighborhood p, and n is the number of point clouds in neighborhood p.

[0028] Exemplarily, singular eigenvalues ​​of the point cloud may be obtained by performing singular value decomposition on the covariance matrix of the point cloud, and the characteristics thereof may include three eigenvalues, including a first eigenvalue of rank 0, a second eigenvalue of rank 1, and a third eigenvalue of rank 2.

[0029] Exemplarily, the ratio between the first eigenvalue of the point cloud and the sum of the three eigenvalues ​​is taken as the curvature of the point cloud.

[0030] For example, the point cloud p is calculated using the following formula: i Curvature: ; in, represents the first eigenvalue of rank 0, represents the second eigenvalue of rank 1, represents the third eigenvalue of rank 2.

[0031] In this example, the covariance matrix of the point cloud is determined based on the position of the point cloud relative to the geometric center of the point cloud in the neighborhood, and then the covariance matrix is ​​subjected to singular value decomposition to obtain the singular eigenvalues ​​of the point cloud. The singular eigenvalues ​​of the point cloud can be used to accurately calculate the curvature of the point cloud.

[0032] In one embodiment, the surrounding vegetation point cloud is divided into a first branch point cloud set and a first leaf point cloud set based on the local curvature NCR of each point cloud in the surrounding vegetation point cloud of the target transmission line, including one of the following: when the local curvature NCR of the point cloud is greater than a preset local curvature threshold T NCR In the case of , the point cloud is added to the first leaf point cloud set; when the local curvature NCR of the point cloud is less than the preset local curvature threshold T NCR , add the point cloud to the first branch point cloud set.

[0033] For example, from the above curvature calculation formula, it can be seen that the curvature of any point cloud is between 0 and 1 / 3, reflecting the degree of change of the point along the surface normal. The higher the value, the more obvious the curvature change, and the lower the value, the less obvious the curvature change.

[0034] It can be understood that the leaf point cloud has a higher curvature. The branch point cloud has a lower curvature. Although the spatial distribution of leaf points is complex, resulting in the curvature NCR of some leaf point clouds being similar to that of the branch point cloud, by setting the curvature threshold T NCR , which can effectively separate leaf points with large local curvature changes from the point cloud.

[0035] For example, Figure 2As shown, the curvature of the original point cloud data is estimated, and the curvature is used to divide it into a first branch point cloud set and a first leaf point cloud set. The connected domain segmentation and branch and leaf separation of the connected components of the above-mentioned steps S120 and S130 are performed on the first branch point cloud set to obtain a second branch point cloud set and a branch and leaf mixed point cloud set. Then, the first leaf point cloud set is merged into the branch and leaf mixed point cloud set to obtain a new branch and leaf mixed point cloud set. Finally, the above-mentioned step S140 is adopted, and each branch point in the second branch point cloud set is used as a seed point, and the new branch and leaf mixed point cloud set is divided into a regional growth point cloud set and a non-regional growth point cloud set by regional growth. Finally, in step S160, the non-regional growth point cloud set is used as the target leaf point cloud set.

[0036] For example, when the curvature threshold is 0.1, the curvature method described above is used to Figure 3A The original point cloud shown in the figure is separated into branches and leaves, and the first branch point cloud set can be obtained as follows Figure 3B As shown, the first blade point cloud is as follows Figure 3C shown.

[0037] Based on the local curvature of each point cloud in the vegetation point cloud around the target transmission line, the surrounding vegetation point cloud is divided into the first branch point cloud set and the first leaf point cloud set. In one embodiment, the above method also includes: determining the linear features, planar features and scattering features of the connected components based on the geometric distribution characteristics of the connected components; processing the linear features, planar features and scattering features based on the nonlinear adjustment coefficient, planarity weight and scattering weight to obtain the linear feature significance value; processing the planar features and scattering features based on the nonlinear adjustment coefficient, planarity weight and scattering weight to obtain the nonlinear feature significance value; determining the linear feature significance degree of the connected components based on the ratio between the linear feature significance degree and the nonlinear feature significance degree.

[0038] For example, the singular eigenvalues ​​of the connected components may be calculated, and the linear features, planar features, and scattering features of the connected components may be calculated using the singular eigenvalues ​​of the connected components.

[0039] Exemplarily, singular value decomposition is performed on the covariance matrix of the connected component to obtain three eigenvalues ​​of the connected component with ranks of 0, 1 and 2, namely, the first eigenvalue, the second eigenvalue and the third eigenvalue.

[0040] Exemplarily, the ratio of the difference between the first eigenvalue and the second eigenvalue of the connected component to the first eigenvalue is taken as the linear feature of the connected component.

[0041] Exemplarily, the ratio of the difference between the second eigenvalue and the third eigenvalue of the connected component to the first eigenvalue is taken as the plane feature of the connected component.

[0042] Exemplarily, the ratio between the third eigenvalue and the first eigenvalue of the connected component is used as the scattering feature of the connected component.

[0043] Exemplarily, the linear feature significance of the connected component is calculated using the following formula: : ; in, represents linear features, represents the nonlinear adjustment coefficient, Represents a plane feature, represents the scattering characteristics, represents the planarity weight, Represents the scattering weight.

[0044] For example, and Represent the planarity and scattering weights respectively, and satisfy the constraints + =1. By adjusting the weights, the importance of planarity and scattering can be more accurately reflected. In order to adapt to the situation where the linear features are not significant, a nonlinear adjustment coefficient is introduced. , dynamically adjusts the contribution of the nonlinear part, making the above formula more flexible and accurate in distinguishing branches from leaves. The LWSR value ranges from 0 to positive infinity, where the larger the value, the more likely the segment is dominated by linear features.

[0045] For example, in order to accurately determine the best parameter combination, a grid search method can be used to traverse multiple parameter combinations to find the optimal parameters that can maximize the classification accuracy based on the LWSR value, thereby effectively distinguishing branches from leaves. Among them, the value range of WP and λ is 0~1, WS is 1-WP, and the LWSR threshold range is 0.5~10. By conducting a systematic search in the preset parameter space, it is finally determined that (WP = 0.2), (WS = 0.8), (λ=0.8) and the LWSR threshold is 2 are the optimal combinations, achieving the highest classification accuracy on the LWSR indicator, and further reducing the omission error of branch point detection in subsequent processing.

[0046] According to the above-mentioned embodiment, the linear feature significance of the connected components can be accurately calculated.

[0047] In one implementation, the first branch point cloud set is divided into a second branch point cloud set and a branch-leaf mixed point cloud set based on the linear feature significance of the geometric distribution features of each connected component, including one of the following: When the linear feature significance of the connected component is greater than a preset linear feature significance threshold, the connected component is added to the second branch point cloud set; When the linear feature significance of the connected component is less than a preset linear feature significance threshold, the connected component is added to the branch-leaf mixed point cloud set.

[0048] In this example, the first branch point cloud is first divided into multiple connected components, and the multiple connected components are identified as branches or leaves respectively. In this way, during calculation, the geometric distribution characteristics of each connected component are calculated instead of each point, which greatly reduces the amount of calculation and also improves the classification efficiency.

[0049] In one embodiment, the above-mentioned method uses each branch point in the second branch point cloud set as a seed point and divides the branch-leaf mixed point cloud set into a regional growth point cloud set and a non-regional growth point cloud set by regional growing, including: Extract seed points from the second branch point cloud set, delete the seed points in the second branch point cloud set, and perform the following first operation on the seed points, return to continue extracting seed points, stop extracting seed points when the second branch point cloud set is an empty set, and use the branch-leaf mixed point cloud set as a non-regional growth point cloud set; The first operation includes: Based on the normal angle between the normal vector of the seed point and the normal vectors of each branch-leaf mixed point cloud in the branch-leaf mixed point cloud set, determining whether each branch-leaf mixed point cloud is a regional growth point of the seed point; For any branch-and-leaf mixed point cloud in the branch-and-leaf mixed point cloud set, if the branch-and-leaf mixed point cloud is a regional growth point of a seed point, the branch-and-leaf mixed point cloud is added to the regional growth point cloud set, and the branch-and-leaf mixed point cloud is deleted from the branch-and-leaf mixed point cloud set.

[0050] Exemplarily, based on the normal angle between the normal vector of the seed point and the normal vectors of each branch and leaf mixed point cloud in the branch and leaf mixed point cloud set, as well as the distance between the seed point and the branch and leaf mixed point cloud, it is determined whether each branch and leaf mixed point cloud is the regional growth point of the seed point.

[0051] Exemplarily, if the distance between the seed point and the branch-leaf mixed point cloud is relatively close, and the normal angle between the normal vector of the seed point and the normal vector of the branch-leaf mixed point cloud is smaller than the smoothing threshold, then the branch-leaf mixed point cloud is the regional growth point of the seed point.

[0052] According to the above example, the branch points in the second branch point cloud set are used as seed points, and the neighborhood points (irregular branch points, i.e., branch-leaf mixed point cloud) are extracted from the branch-leaf mixed point cloud set through region growing. This process defines relevant constraint rules to determine whether the neighborhood points (irregular branch points) can be merged with the current region (seed points). This example determines whether to classify the neighboring points (irregular branch points) into the same region by checking the normal vector angle between the seed point and its neighboring points (irregular branch points). If the normal vector angle is less than the smoothing threshold θ, the neighboring point is a branch point and continues to grow as a seed point. This stage divides neighboring points with smooth surfaces into the same region, and the normal changes are small.

[0053] In one embodiment, the angle between the normal vector of the seed point and the normal vector of each branch-leaf mixed point cloud in the branch-leaf mixed point cloud set is determined to determine whether each branch-leaf mixed point cloud is a regional growth point of the seed point, including: When the normal angle between the normal vector of the seed point and the normal vector of the adjacent branch-leaf mixed point cloud is less than a preset smoothing threshold, or the difference between 180 degrees and the normal angle is less than the smoothing threshold, the branch-leaf mixed point cloud is determined to be a regional growth point of the seed point; When the normal angle between the normal vector of the seed point and the normal vector of the adjacent branch-leaf mixed point cloud is greater than the preset smoothing threshold, or the difference between 180 degrees and the normal angle is greater than the smoothing threshold, the branch-leaf mixed point cloud is determined to be the regional growth point of the seed point.

[0054] According to the above embodiment, adjacent points with smooth surfaces can be divided into the same branch area, and the normal line changes very little.

[0055] Figure 4 It is a structural block diagram of a branch and leaf separation device for vegetation point cloud around a transmission line according to an embodiment of the present invention.

[0056] like Figure 4 As shown, the branch and leaf separation device for the vegetation point cloud around the transmission line may include: A first point cloud classification module 410 is used to divide the surrounding vegetation point cloud of the target transmission line into a first branch point cloud set and a first leaf point cloud set based on the curvature of each point cloud in the surrounding vegetation point cloud of the target transmission line; A connected domain segmentation module 420 is used to perform connected domain segmentation on the first branch point cloud set to obtain a plurality of connected components; A second point cloud classification module 430 is used to divide the first branch point cloud set into a second branch point cloud set and a branch-leaf mixed point cloud set based on the linear feature significance of the geometric distribution features of each of the connected components; The third point cloud classification module 440 is used to use each branch point in the second branch point cloud set as a seed point, and divide the branch-leaf mixed point cloud set into a regional growth point cloud set and a non-regional growth point cloud set by regional growing; A first merging module 450, configured to determine a target branch point cloud set of the target transmission line based on a union of the second branch point cloud set and the regional growth point cloud set; The second union module 460 is used to determine a target blade point cloud set of the target transmission line based on the union of the first blade point cloud set and the non-regional growth point cloud set.

[0057] In one embodiment, the above device further comprises: A covariance matrix determination module, used to determine the covariance matrix of each point cloud based on the position information of the geometric center of the surrounding vegetation point cloud of the target transmission line in the specified neighborhood and the position information of each point cloud in the surrounding vegetation point cloud; A singular value decomposition module, used for performing singular value decomposition on the covariance matrix of each point cloud to obtain the singular eigenvalue of each point cloud; The curvature determination module is used to determine the curvature of each of the point clouds based on the singular eigenvalues ​​of each of the point clouds.

[0058] In one implementation, the first point cloud classification module 410 includes: a first classification unit, configured to add the point cloud to the first blade point cloud set when the local curvature of the point cloud is greater than a preset local curvature threshold; The second classification unit is used to add the point cloud to the first branch point cloud set when the local curvature of the point cloud is less than a preset local curvature threshold.

[0059] In one embodiment, the above device further comprises: A geometric feature determination module, used to determine the linear feature, planar feature and scattering feature of the connected component based on the geometric distribution feature of the connected component; A linear feature determination module, used for processing the linear feature, the planar feature and the scattering feature based on a nonlinear adjustment coefficient, a planarity weight and a scattering weight to obtain a linear feature significance value; A nonlinear feature determination module, used to process the planar feature and the scattering feature based on the nonlinear adjustment coefficient, the planarity weight and the scattering weight to obtain a nonlinear feature significance value; The linear significance degree determination module is used to determine the linear feature significance degree of the connected component based on the ratio between the linear feature significance degree and the nonlinear feature significance degree.

[0060] In one implementation, the second point cloud classification module 430 includes: A third classification unit, configured to add the connected component to the second branch point cloud set when the linear feature significance degree of the connected component is greater than a preset linear feature significance degree threshold; The fourth classification unit is used to add the connected component to the branch-leaf mixed point cloud set when the linear feature significance of the connected component is less than a preset linear feature significance threshold.

[0061] In one implementation, the third point cloud classification module 440 is specifically used to: Extracting seed points from the second branch point cloud set, deleting the seed points in the second branch point cloud set, and performing the following first operation on the seed points, returning to continue extracting the seed points until the second branch point cloud set is an empty set, stopping the extraction of the seed points, and using the branch-leaf mixed point cloud set as the non-regional growth point cloud set; The first operation includes: Based on the normal angle between the normal vector of the seed point and the normal vectors of each branch-leaf mixed point cloud in the branch-leaf mixed point cloud set, determining whether each branch-leaf mixed point cloud is a regional growth point of the seed point; For any branch and leaf mixed point cloud in the branch and leaf mixed point cloud set, if the branch and leaf mixed point cloud is a regional growth point of the seed point, the branch and leaf mixed point cloud is added to the regional growth point cloud set, and the branch and leaf mixed point cloud is deleted from the branch and leaf mixed point cloud set.

[0062] In one embodiment, the determining whether each of the branch-leaf mixed point clouds is a regional growth point of the seed point based on the angle between the normal vector of the seed point and the normal vector of each branch-leaf mixed point cloud in the branch-leaf mixed point cloud set includes: When the normal angle between the normal vector of the seed point and the normal vector of the adjacent branch-leaf mixed point cloud is less than a preset smoothing threshold, or the difference between 180 degrees and the normal angle is less than the smoothing threshold, the branch-leaf mixed point cloud is determined to be the regional growth point of the seed point; When the normal angle between the normal vector of the seed point and the normal vector of the adjacent branch-leaf mixed point cloud is greater than a preset smoothing threshold, or the difference between 180 degrees and the normal angle is greater than the smoothing threshold, the branch-leaf mixed point cloud is determined to be the regional growth point of the seed point.

[0063] For the description of specific functions and examples of each module and submodule of the system in the embodiment of the present invention, reference can be made to the relevant description of the corresponding steps in the above method embodiment, which will not be repeated here.

[0064] In the technical solution of the present invention, the acquisition, storage and application of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0065] According to an embodiment of the present invention, the present invention also provides a system and a readable storage medium.

[0066] Figure 5 A schematic block diagram of an example electronic device 800 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0067] like Figure 5 As shown, the device 800 includes a computing unit 801, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 to a random access memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the device 800 can also be stored. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0068] A number of components in the device 800 are connected to the I / O interface 805, including: an input unit 806, such as a keyboard, a mouse, etc.; an output unit 807, such as various types of displays, speakers, etc.; a storage unit 808, such as a disk, an optical disk, etc.; and a communication unit 809, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 809 allows the device 800 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0069] The computing unit 801 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 801 performs the various methods and processes described above, such as a branch and leaf separation method for a vegetation point cloud around a transmission line. For example, in some embodiments, the branch and leaf separation method for a vegetation point cloud around a transmission line may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 808. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 800 via the ROM 802 and / or the communication unit 809. When the computer program is loaded into the RAM 803 and executed by the computing unit 801, one or more steps of the branch and leaf separation method for a vegetation point cloud around a transmission line described above may be performed. Alternatively, in other embodiments, the computing unit 801 may be configured in any other appropriate manner (for example, by means of firmware) to execute the branch and leaf separation method for the vegetation point cloud around the transmission line.

[0070] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0071] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0072] In the context of the present invention, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0073] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0074] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0075] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0076] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution disclosed in the present invention can be achieved, and this document does not limit this.

[0077] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for separating branches and leaves from vegetation point clouds around power transmission lines, characterized in that: include: Based on the curvature of each point cloud in the surrounding vegetation point cloud of the target transmission line, the surrounding vegetation point cloud is divided into a first branch point cloud set and a first leaf point cloud set; Performing connected domain segmentation on the first branch point cloud set to obtain multiple connected components; Based on the linear feature significance of the geometric distribution features of each of the connected components, the first branch point cloud set is divided into a second branch point cloud set and a branch-leaf mixed point cloud set; Taking each branch point in the second branch point cloud set as a seed point, dividing the branch-leaf mixed point cloud set into a regional growth point cloud set and a non-regional growth point cloud set by regional growing; Determining a target branch point cloud set of the target transmission line based on a union of the second branch point cloud set and the regional growth point cloud set; Based on the union of the first blade point cloud set and the non-regional growth point cloud set, a target blade point cloud set of the target transmission line is determined.

2. The method according to claim 1, characterized in that Also includes: Based on the position information of the geometric center of the target transmission line in the surrounding vegetation point cloud within the specified neighborhood, and the position information of each point cloud in the surrounding vegetation point cloud, respectively determine the covariance matrix of each point cloud; Performing singular value decomposition on the covariance matrix of each of the point clouds to obtain singular eigenvalues ​​of each of the point clouds; Based on the singular eigenvalues ​​of each of the point clouds, the curvature of each of the point clouds is determined.

3. The method according to claim 1, characterized in that The dividing of the surrounding vegetation point cloud into a first branch point cloud set and a first leaf point cloud set based on the local curvature of each point cloud in the surrounding vegetation point cloud of the target transmission line comprises one of the following: When the local curvature of the point cloud is greater than a preset local curvature threshold, adding the point cloud to the first blade point cloud set; When the local curvature of the point cloud is less than a preset local curvature threshold, the point cloud is added to the first branch point cloud set.

4. The method according to claim 1, characterized in that: Also includes: Determining linear features, planar features, and scattering features of the connected components based on geometric distribution features of the connected components; Based on the nonlinear adjustment coefficient, the planarity weight and the scattering weight, the linear feature, the planar feature and the scattering feature are processed to obtain a linear feature significance value; Based on the nonlinear adjustment coefficient, the planarity weight and the scattering weight, the planar feature and the scattering feature are processed to obtain a nonlinear feature significance value; The linear feature significance level of the connected component is determined based on the ratio between the linear feature significance level and the nonlinear feature significance level.

5. The method according to claim 1, characterized in that The dividing of the first branch point cloud set into a second branch point cloud set and a branch-leaf mixed point cloud set based on the linear feature significance of the geometric distribution features of each of the connected components comprises one of the following: When the linear feature significance of the connected component is greater than a preset linear feature significance threshold, adding the connected component to the second branch point cloud set; When the linear feature significance level of the connected component is less than a preset linear feature significance level threshold, the connected component is added to the branch-leaf mixed point cloud set.

6. The method according to claim 1, characterized in that The method of taking each branch point in the second branch point cloud set as a seed point and dividing the branch-leaf mixed point cloud set into a regional growth point cloud set and a non-regional growth point cloud set by regional growing includes: Extracting seed points from the second branch point cloud set, deleting the seed points in the second branch point cloud set, and performing the following first operation on the seed points, returning to continue extracting the seed points until the second branch point cloud set is an empty set, stopping the extraction of the seed points, and using the branch-leaf mixed point cloud set as the non-regional growth point cloud set; The first operation includes: Based on the normal angle between the normal vector of the seed point and the normal vectors of each branch-leaf mixed point cloud in the branch-leaf mixed point cloud set, determining whether each branch-leaf mixed point cloud is a regional growth point of the seed point; For any branch and leaf mixed point cloud in the branch and leaf mixed point cloud set, if the branch and leaf mixed point cloud is a regional growth point of the seed point, the branch and leaf mixed point cloud is added to the regional growth point cloud set, and the branch and leaf mixed point cloud is deleted from the branch and leaf mixed point cloud set.

7. The method according to claim 6, characterized in that The determining whether each of the branch-leaf mixed point clouds is a regional growth point of the seed point based on the angle between the normal vector of the seed point and the normal vector of each branch-leaf mixed point cloud in the branch-leaf mixed point cloud set includes: When the normal angle between the normal vector of the seed point and the normal vector of the adjacent branch-leaf mixed point cloud is less than a preset smoothing threshold, or the difference between 180 degrees and the normal angle is less than the smoothing threshold, the branch-leaf mixed point cloud is determined to be the regional growth point of the seed point; When the normal angle between the normal vector of the seed point and the normal vector of the adjacent branch-leaf mixed point cloud is greater than a preset smoothing threshold, or the difference between 180 degrees and the normal angle is greater than the smoothing threshold, the branch-leaf mixed point cloud is determined to be the regional growth point of the seed point.

8. A branch and leaf separation device for vegetation point cloud around power transmission lines, characterized in that: include: A first point cloud classification module, used for dividing the surrounding vegetation point cloud of the target transmission line into a first branch point cloud set and a first leaf point cloud set based on the curvature of each point cloud in the surrounding vegetation point cloud of the target transmission line; A connected domain segmentation module, used for performing connected domain segmentation on the first branch point cloud set to obtain a plurality of connected components; A second point cloud classification module is used to divide the first branch point cloud set into a second branch point cloud set and a branch-leaf mixed point cloud set based on the linear feature significance of the geometric distribution features of each of the connected components; A third point cloud classification module is used to use each branch point in the second branch point cloud set as a seed point, and divide the branch and leaf mixed point cloud set into a regional growth point cloud set and a non-regional growth point cloud set by regional growing; A first union module, configured to determine a target branch point cloud set of the target transmission line based on a union of the second branch point cloud set and the regional growth point cloud set; The second union module is used to determine the target blade point cloud set of the target transmission line based on the union of the first blade point cloud set and the non-regional growth point cloud set.

9. A branch and leaf separation system for vegetation point cloud around a transmission line, comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-7.

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