CN112381041A - Tree identification method and device for power transmission line and terminal equipment - Google Patents
Tree identification method and device for power transmission line and terminal equipment Download PDFInfo
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Abstract
The invention discloses a tree identification method, a tree identification device and terminal equipment for a power transmission line, wherein the method comprises the following steps: acquiring airborne point cloud data, and performing data filtering on the airborne point cloud data by adopting a preset algorithm to distinguish ground point cloud and non-ground point cloud; constructing a ground surface DEM by using ground point clouds, and separating vegetation point clouds by using non-ground point clouds; extracting point cloud of the breast diameter of the vegetation, fitting a circle based on the extracted point cloud data of the vegetation, and taking the obtained circle center position as the position of a tree seed point, wherein the diameter is the breast diameter of the tree, and the point corresponding to the circle center is the tree seed point; and (4) fitting the trunk growth direction by using the breast height of the tree as the basis to obtain the height of the single tree. The identification method applied by the invention can accurately and efficiently obtain the vegetation point cloud and the height of the single tree, effectively predict the growth height of the tree, and enable power transmission personnel to timely predict the hidden danger of the tree and clean the tree.
Description
Technical Field
The invention relates to the technical field of power transmission line detection, in particular to a tree identification method and device for a power transmission line and terminal equipment.
Background
The reliability of the transmission line is influenced by a plurality of internal and external factors, wherein the contradiction between the line and the tree poses a serious threat to the safe operation of the transmission line, and the cleaning of the hidden danger of the tree becomes an important work for ensuring the safe operation of the transmission facility by an operation and maintenance unit.
Trees in the power transmission line channel can grow every year, so that influence of tree obstacles in the power transmission line corridor is easily caused, and meanwhile, researchers find that the tree obstacles and manpower and material resources consumed by cleaning the tree obstacles become the workload of operation and maintenance units.
In the existing technical means, the main lidar technology is applied to the power transmission line survey design, and generally the height of trees can be extracted, even the types of trees can be identified, for example, chinese patent with publication number CN108680927A and publication number 2018.10.19: a method for measuring and calculating the distance between an iron tower and trees of a power transmission line detects and compares the distance between trees by a laser radar technology, but the method is not suitable for trees growing rapidly, has large detection errors, does not predict the growth height of the trees, and influences the obstacle clearing efficiency of the power transmission line.
Disclosure of Invention
In view of this, the invention provides a tree identification method and device for a power transmission line, and a terminal device. The identification method can accurately and efficiently acquire the vegetation point cloud and the height of a single tree, effectively predict the growth height of the tree, and enable power transmission personnel to timely predict tree hidden dangers and clean the tree.
The specific technical scheme is as follows:
a tree identification method for a power transmission line comprises the following operation steps:
acquiring airborne point cloud data, and performing data filtering on the airborne point cloud data by adopting a preset algorithm to distinguish ground point cloud and non-ground point cloud;
constructing a ground DEM (digital elevation model) by using ground point clouds, and separating vegetation point clouds by using non-ground point clouds;
calculating the space distance between the vegetation point cloud and the ground surface DEM in the vertical direction, and screening out the breast diameter point cloud from the obtained space distance;
extracting point cloud of the breast diameter of the vegetation, fitting a circle based on the extracted point cloud data of the vegetation, and taking the obtained circle center position as the position of a tree seed point, wherein the diameter is the breast diameter of the tree, and the point corresponding to the circle center is the tree seed point;
and (4) fitting the trunk growth direction by using the breast height of the tree as the basis to obtain the height of the single tree.
As a preferred possible embodiment; the preset algorithm is a cloth simulation algorithm (CSF).
As a preferred possible embodiment; the method comprises the following steps of constructing a ground surface DEM by using ground point clouds and separating vegetation point clouds by using non-ground point clouds, and specifically comprises the following operation steps:
constructing a ground surface DEM (digital elevation model) by utilizing a Delauary algorithm according to ground point data, and after the DEM is constructed, performing point cloud resampling on the DEM to obtain a ground DEM point cloud set;
and carrying out data classification on the non-ground point cloud data by using a watershed algorithm to obtain vegetation point cloud.
As a preferred possible embodiment; based on the position of the seed point of the tree, fitting the growth direction of the trunk by using the breast height of the tree to obtain the height of the single tree, and specifically comprises the following operation steps:
fitting the trunk growth direction by taking the tree seed point position as a circle center and taking the tree breast diameter as a radius from the circle center, and determining a position point on each trunk, which is farthest away from the circle center;
obtaining the height of the farthest position point on each trunk;
and taking the position point with the highest height as the height of the single tree.
As a preferred possible embodiment; on the basis of the position of the seed point of the tree, fitting the growth direction of the trunk by using the breast height of the tree, and calculating the distance between the tree and the power transmission line after obtaining the height of the single tree;
identifying the power transmission line through airborne point cloud data, and calculating the height of the power transmission line;
and calculating the distance between the tree and the power transmission line by using the height of the power transmission line and the height of the single-tree.
As a preferred possible embodiment; after the distance between the tree and the power transmission line is calculated by utilizing the height of the power transmission line and the height of the single-tree, the method further comprises the following operation steps:
judging whether the distance between the current tree and the power transmission line is smaller than the safe distance of the safe operation and maintenance regulation of the current power transmission line, if so, judging that the current tree is a hidden danger point; otherwise, judging the current tree as a safety tree high point.
As a preferred possible embodiment; after judging whether the distance between the current tree and the power transmission line is smaller than the safe distance of the safe operation and maintenance regulation of the current power transmission line, performing high-definition image analysis processing on the tree at the current hidden danger point, and identifying the tree type of the current hidden danger point.
As a preferred possible embodiment; the method comprises the following steps of carrying out high-definition image analysis processing on trees at the current hidden danger point, and identifying the tree type of the current hidden danger point, wherein the method specifically comprises the following operation steps:
acquiring high-definition visible light images of the power transmission line and the trees; training standard image data corresponding to different tree types by using a machine learning model;
and meanwhile, performing model training by taking the hidden trouble points as the input of the machine learning model, thereby identifying the tree type corresponding to the current hidden trouble points.
Correspondingly, the invention also provides a tree identification device for the power transmission line, which comprises a point cloud classification module, an analysis module, a screening module, a fitting circle establishing module and a fitting calculation module;
the point cloud classification module is used for acquiring airborne point cloud data, and performing data filtering on the airborne point cloud data by adopting a preset algorithm to distinguish ground point cloud and non-ground point cloud;
the analysis module is used for constructing a ground DEM (digital elevation model) by utilizing the ground point cloud and separating vegetation point cloud by utilizing the non-ground point cloud;
the screening module is used for calculating the spatial distance between the vegetation point cloud and the ground DEM in the vertical direction and screening the breast diameter point cloud from the obtained spatial distance;
the fitting circle establishing module is used for extracting the point cloud of the breast diameter of the vegetation, fitting a circle based on the point cloud data of the extracted vegetation, and taking the obtained circle center position as the position of a tree seed point, wherein the diameter is the breast diameter of the tree, and the point corresponding to the circle center is the tree seed point;
and the fitting calculation module is used for fitting the trunk growth direction by using the tree breast height on the basis of the position of the tree seed point to obtain the height of the single tree.
The present invention also provides a terminal device, including: a processor and a memory, the memory storing a computer program, the processor being configured to execute the computer program to implement the tree identification method described above.
The invention has the following beneficial effects:
the tree identification method provided by the invention is different from the traditional point cloud data identification method, and by the identification method provided by the invention, a ground surface DEM can be constructed by using ground point clouds, and vegetation point clouds are separated by using non-ground point clouds; calculating the space distance between the vegetation point cloud and the ground surface DEM in the vertical direction, and screening out the breast diameter point cloud from the obtained space distance; extracting point cloud of the breast diameter of the vegetation, fitting a circle based on the extracted point cloud data of the vegetation, and taking the obtained circle center position as the position of a tree seed point, wherein the diameter is the breast diameter of the tree, and the point corresponding to the circle center is the tree seed point; and (4) fitting the trunk growth direction by using the breast height of the tree as the basis to obtain the height of the single tree. Aiming at the severity and complexity of the tree obstacle problem, particularly the tree obstacle problem in mountainous areas, the laser radar equipment is utilized to carry out identification and analysis on trees in a power transmission line channel and prediction and analysis research on hidden dangers, so that the prediction capability of the hidden dangers of the tree obstacle in a power transmission line operation and maintenance unit is improved, and the safe and stable operation of the power transmission line is guaranteed.
Drawings
FIG. 1 is a schematic flow chart of a tree identification method of the present invention;
fig. 2 is a schematic structural diagram of the tree identification device of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
Hereinafter, the terms "including", "having", and their derivatives, which may be used in various embodiments of the present invention, are only intended to indicate specific features, numbers, steps, operations, elements, components, or combinations of the foregoing, and should not be construed as first excluding the existence of, or adding to, one or more other features, numbers, steps, operations, elements, components, or combinations of the foregoing.
Furthermore, the terms "first," "second," "third," and the like are used solely to distinguish one from another and are not to be construed as indicating or implying relative importance.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which various embodiments of the present invention belong. The terms (such as those defined in commonly used dictionaries) should be interpreted as having a meaning that is consistent with their contextual meaning in the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein in various embodiments of the present invention.
Example 1
The embodiment provides a tree identification method for a power transmission line, which can be applied to an application platform such as an unmanned aerial vehicle. The unmanned aerial vehicle data acquisition system used in the embodiment of the invention can acquire high-precision airborne laser scanning (Li DAR) point cloud data, high-resolution aviation digital images, thermal infrared images, ultraviolet images and the like in the power line corridor. The data acquisition system mainly comprises a laser, a visible light detector, an infrared thermal imager, an ultraviolet camera, a receiver, a Global Positioning System (GPS) and an Inertial Measurement Unit (IMU) which form a positioning and attitude determination system (POS), a management system, a data storage system and the like.
Referring to fig. 1, a tree identification method for a power transmission line is performed, and includes the following operation steps:
step S100: acquiring airborne point cloud data, and performing data filtering on the airborne point cloud data by adopting a preset algorithm to distinguish ground point cloud and non-ground point cloud;
step S200: constructing a ground surface DEM by using ground point clouds, and separating vegetation point clouds by using non-ground point clouds;
step S300: calculating the space distance between the vegetation point cloud and the ground surface DEM in the vertical direction, and screening out the breast diameter point cloud from the obtained space distance;
step S400: extracting point cloud of the breast diameter of the vegetation, fitting a circle based on the extracted point cloud data of the vegetation, and taking the obtained circle center position as the position of a tree seed point, wherein the diameter is the breast diameter of the tree, and the point corresponding to the circle center is the tree seed point;
step S500: and (4) fitting the trunk growth direction by using the breast height of the tree as the basis to obtain the height of the single tree.
According to the tree identification method provided by the embodiment of the invention, aiming at the severity and complexity of the problem of the tree obstacle, particularly the problem of the tree obstacle in mountainous areas, the laser radar equipment is utilized to carry out identification and analysis on trees in a channel of the power transmission line and research on prediction and analysis of hidden dangers, so that the prediction capability of the hidden dangers of the tree obstacle in a power transmission line operation and maintenance unit is improved, and the safe and stable operation of the power transmission line is ensured.
In a particular aspect of the embodiments of the present invention; the preset algorithm is a cloth simulation algorithm (CSF).
In step S200, a ground surface DEM is constructed by using ground point clouds and a vegetation point cloud is separated by using non-ground point clouds, which specifically includes the following steps:
step S210: constructing a ground surface DEM by utilizing a Delauary algorithm according to ground point data; after the DEM is constructed, point cloud resampling is carried out on the DEM, so that a ground DEM point cloud set is obtained;
step S220: and carrying out data classification on the non-ground point cloud data by using a watershed algorithm to obtain vegetation point cloud.
In step S300, a spatial distance between the vegetation point cloud and the ground surface DEM in the vertical direction is calculated, and a diameter at breast height point cloud is screened from the obtained spatial distance (this is a technique known to those skilled in the art, and details of this embodiment of the present application are not described one by one): the diameter at breast height generally refers to the diameter of trees at 1.3 meters above the ground, and in order to ensure that the point cloud has sufficient calculation density, the point cloud screened by the distance threshold is used as the point cloud at the diameter at breast height. The screened point clouds are point clouds at the breast diameters of all trees, the breast diameter point clouds of each tree are not distinguished, and further processing is needed on the basis, so that the tree breast diameter point clouds of each tree are extracted.
In this embodiment, a circle is fitted based on the extracted point cloud data, the obtained circle center position is used as a tree seed point position, the diameter is a tree breast diameter, and a point corresponding to the circle center is a tree seed point.
In step S500, based on the position of the seed point of the tree, fitting the trunk growth direction by using the diameter at breast height of the tree to obtain the height of the single tree, specifically comprising the following operation steps:
step S510: fitting the trunk growth direction by taking the position of the seed point of the tree as the center of a circle and taking the diameter at breast height of the tree as the radius from the center of a circle, and determining the position point on each trunk, which is farthest away from the center of a circle;
step S520: obtaining the height of the farthest position point on each trunk;
step S530: and taking the position point with the highest height as the height of the single tree.
It should be noted that, when single tree segmentation is realized, two-dimensional hough transformation is performed on the tree point cloud data, a tree trunk is detected, a circle is fitted to calculate the diameter of the tree trunk (namely, the diameter of breast height of the tree) and the trunk diameter is fitted to the growth direction of the tree trunk, so that the height of the single tree is obtained. The single tree is identified based on the height of the single tree, and morphological parameters such as the position of the tree, the crown, the height under the branch, the volume of the standing tree and the like can be further acquired, so that forest resource management can be performed according to the morphological parameters.
After the step S500, calculating the distance between the tree and the power transmission line;
step S610: identifying the power transmission line through airborne point cloud data, and calculating the height of the power transmission line;
step S620: and calculating the distance between the tree and the power transmission line by using the height of the power transmission line and the height of the single-tree.
After step S620, the following steps are also included:
step S700: judging whether the distance between the current tree and the power transmission line is smaller than the safe distance of the safe operation and maintenance regulation of the current power transmission line, if so, judging that the current tree is a hidden danger point; otherwise, judging the current tree as a safety tree high point.
After step S700, performing high-definition image analysis processing on the tree at the current hidden danger point, and identifying the tree type at the current hidden danger point.
The method comprises the following steps of carrying out high-definition image analysis processing on trees at the current hidden danger point, and identifying the tree type of the current hidden danger point, wherein the method specifically comprises the following operation steps:
step S800: acquiring high-definition visible light images of the power transmission line and the trees; training standard image data corresponding to different tree types by using a machine learning model;
step S900: and meanwhile, performing model training by taking the hidden trouble points as the input of the machine learning model, thereby identifying the tree type corresponding to the current hidden trouble points.
Example 2
Referring to fig. 2, the invention further provides a tree identification device for the power transmission line, which comprises a point cloud classification module, an analysis module, a screening module, a fitting circle establishing module and a fitting calculation module;
the point cloud classification module is used for acquiring airborne point cloud data, and performing data filtering on the airborne point cloud data by adopting a preset algorithm to distinguish ground point cloud and non-ground point cloud;
the analysis module is used for constructing a ground DEM (digital elevation model) by utilizing the ground point cloud and separating vegetation point cloud by utilizing the non-ground point cloud;
the screening module is used for calculating the spatial distance between the vegetation point cloud and the ground DEM in the vertical direction and screening the breast diameter point cloud from the obtained spatial distance;
the fitting circle establishing module is used for extracting the point cloud of the breast diameter of the vegetation, fitting a circle based on the point cloud data of the extracted vegetation, and taking the obtained circle center position as the position of a tree seed point, wherein the diameter is the breast diameter of the tree, and the point corresponding to the circle center is the tree seed point;
and the fitting calculation module is used for fitting the trunk growth direction by using the tree breast height on the basis of the position of the tree seed point to obtain the height of the single tree.
It is understood that the above-described tree recognition apparatus for a power transmission line corresponds to the recognition method of embodiment 1. Any of the options in embodiment 1 are also applicable to this embodiment, and will not be described in detail here.
An embodiment of the present invention further provides a terminal device, including: a processor and a memory, the memory storing a computer program, the processor being configured to execute the computer program to implement the tree identification method described above.
The memory may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal, and the like. Further, the memory may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative and, for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention.
Claims (10)
1. A tree identification method for a power transmission line is characterized by comprising the following steps:
acquiring airborne point cloud data, and performing data filtering on the airborne point cloud data by adopting a preset algorithm to distinguish ground point cloud and non-ground point cloud;
constructing a ground surface DEM by using ground point clouds, and separating vegetation point clouds by using non-ground point clouds;
calculating the space distance between the vegetation point cloud and the ground surface DEM in the vertical direction, and screening out the breast diameter point cloud from the obtained space distance;
extracting point cloud of the breast diameter of the vegetation, fitting a circle based on the extracted point cloud data of the vegetation, and taking the obtained circle center position as the position of a tree seed point, wherein the diameter is the breast diameter of the tree, and the point corresponding to the circle center is the tree seed point;
and (4) fitting the trunk growth direction by using the breast height of the tree as the basis to obtain the height of the single tree.
2. The tree identification method according to claim 1, wherein the predetermined algorithm is a cloth simulation algorithm.
3. The tree identification method according to claim 1, wherein a ground surface DEM is constructed by using ground point clouds, and vegetation point clouds are separated by using non-ground point clouds, and the method specifically comprises the following operation steps:
constructing a ground surface DEM (digital elevation model) by utilizing a Delauary algorithm according to ground point data, and after the DEM is constructed, performing point cloud resampling on the DEM to obtain a ground DEM point cloud set;
and carrying out data classification on the non-ground point cloud data by using a watershed algorithm to obtain vegetation point cloud.
4. The tree identification method according to claim 1, wherein the tree height is obtained by fitting the trunk growth direction with the tree breast height based on the tree seed point position, and specifically comprising the following operation steps:
fitting the trunk growth direction by taking the tree seed point position as a circle center and taking the tree breast diameter as a radius from the circle center, and determining a position point on each trunk, which is farthest away from the circle center;
obtaining the height of the farthest position point on each trunk;
and taking the position point with the highest height as the height of the single tree.
5. The tree identification method according to claim 4, wherein after the tree height is obtained by fitting the trunk growth direction with the tree breast height based on the tree seed point position, the method further comprises the operation of calculating the distance between the tree and the power transmission line;
identifying the power transmission line through airborne point cloud data, and calculating the height of the power transmission line;
and calculating the distance between the tree and the power transmission line by using the height of the power transmission line and the height of the single-tree.
6. The tree identification method according to claim 5, further comprising the following steps after calculating the distance between the tree and the power transmission line by using the height of the power transmission line and the height of the single-tree:
judging whether the distance between the current tree and the power transmission line is smaller than the safe distance of the safe operation and maintenance regulation of the current power transmission line, if so, judging that the current tree is a hidden danger point; otherwise, judging the current tree as a safety tree high point.
7. The tree identification method according to claim 6, wherein after judging whether the distance between the current tree and the power transmission line is smaller than the safe distance of the safe operation and maintenance regulation of the current power transmission line, the method further comprises performing high-definition image analysis processing on the tree at the current hidden danger point, and identifying the tree type of the current hidden danger point.
8. The tree identification method according to claim 7, wherein the tree at the current hidden danger point is subjected to high-definition image analysis processing, and the tree type at the current hidden danger point is identified, and the method specifically comprises the following operation steps:
acquiring high-definition visible light images of the power transmission line and the trees; training standard image data corresponding to different tree types by using a machine learning model;
and meanwhile, the hidden danger points are used as the input of a machine learning model to carry out model training, so that the tree type corresponding to the current hidden danger points is identified.
9. A tree identification device for a power transmission line is characterized by comprising a point cloud classification module, an analysis module, a screening module, a fitting circle establishing module and a fitting calculation module;
the point cloud classification module is used for acquiring airborne point cloud data, and performing data filtering on the airborne point cloud data by adopting a preset algorithm to distinguish ground point cloud and non-ground point cloud;
the analysis module is used for constructing a ground DEM (digital elevation model) by utilizing the ground point cloud and separating vegetation point cloud by utilizing the non-ground point cloud;
the screening module is used for calculating the spatial distance between the vegetation point cloud and the ground DEM in the vertical direction and screening the breast diameter point cloud from the obtained spatial distance;
the fitting circle establishing module is used for extracting the point cloud of the breast diameter of the vegetation, fitting a circle based on the point cloud data of the extracted vegetation, and taking the obtained circle center position as the position of a tree seed point, wherein the diameter is the breast diameter of the tree, and the point corresponding to the circle center is the tree seed point;
and the fitting calculation module is used for fitting the trunk growth direction by using the breast height of the tree based on the position of the seed point of the tree, so as to obtain the height of the single tree.
10. A terminal device, comprising: a processor and a memory, the memory storing a computer program, the processor being configured to execute the computer program to implement the tree identification method according to any one of claims 1 to 8.
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| WO2023060632A1 (en) * | 2021-10-14 | 2023-04-20 | 重庆数字城市科技有限公司 | Street view ground object multi-dimensional extraction method and system based on point cloud data |
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