CN112884026A - Image recognition assisted power transmission line laser LiDAR point cloud classification method - Google Patents
Image recognition assisted power transmission line laser LiDAR point cloud classification method Download PDFInfo
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2415—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/10—Terrestrial scenes
- G06V20/13—Satellite images
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Abstract
The invention discloses an image recognition-assisted power transmission line laser LiDAR point cloud classification method, which comprises the following steps of: step S1: performing color point cloud dimension reduction processing to generate a channel orthoimage; step S2: establishing a ground object sample library of the power transmission line, and establishing an image recognition model; step S3: performing rough extraction on roads and buildings based on image recognition; step S4: and carrying out fine classification on roads and buildings based on morphological characteristics. According to the invention, on the basis of the traditional classification technology, an image recognition technology is introduced, the point cloud classification is assisted by using texture information contained in the color point cloud, and the point cloud classification is predefined before the traditional classification, so that the classification effect on buildings and roads is improved.
Description
Technical Field
The invention relates to the technical field of airborne laser LiDAR of a power transmission line, in particular to a power transmission line laser LiDAR point cloud classification method assisted by image recognition.
Background
With the increasing scale of the application of airborne laser LiDAR technology in the power industry, the demand for the automation degree of laser LiDAR data processing is increasing. The point cloud classification is a precondition and a foundation for processing power application data of a laser LiDAR technology, most of the existing laser LiDAR classification algorithms only adopt a morphological filtering algorithm at present, and have a good classification effect on vegetation, power lines, towers and other classes with obvious spatial characteristics, but have a poor classification effect on ground structures (buildings and roads) with the same morphological characteristics as the ground. Meanwhile, buildings and roads are ground object types which are very concerned by the power industry, visual judgment and manual adjustment are needed in actual power application, and great workload is brought to data processing personnel.
Disclosure of Invention
In view of the above, one of the objectives of the present invention is to provide an image recognition assisted power transmission line laser LiDAR point cloud classification method. The image recognition technology is utilized to assist in point cloud classification, buildings and roads are pre-recognized from the aspect of images, and then the boundaries of the buildings and the roads are extracted in a refined mode through morphological characteristics, so that the classification effect of the point cloud classification on the buildings, the roads and the towers is improved, and the point cloud data processing automation process is further promoted.
One of the purposes of the invention is realized by the following technical scheme:
the image recognition-assisted power transmission line laser LiDAR point cloud classification method comprises the following steps:
step S1: performing color point cloud dimension reduction processing to generate a channel orthoimage;
step S2: establishing a ground object sample library of the power transmission line, and establishing an image recognition model;
step S3: performing rough extraction on roads and buildings based on image recognition;
step S4: and carrying out fine classification on roads and buildings based on morphological characteristics.
Specifically, the step S1 specifically includes:
using point cloud plane coordinates in the upper left corner (X)min,Ymax) As a starting point, dividing a plane two-dimensional grid along an X axis and a Y axis by setting the grid size dL, and setting the coordinate value of an arbitrary point P plane in a point cloud as (X)P,YP) Then, the grid position is (u)P,vP)。
And dividing each point in the color point cloud into corresponding grids according to the formula. Constructing an RGB channel image having a pixel size of [ (X)max-Xmin)/dL+1]×[(Ymax-Ymin)/dL+1]And the pixel RGB is the RGB mean value of the three-dimensional points in the corresponding grid, and a channel orthophotograph is generated.
Particularly, in step S2, image data of the power transmission line channel is collected, building and road labels are made, a power transmission line ground feature training sample library is established, multidimensional depth features of the images are extracted from the sample library through a u-net network, feature expression capability of various ground feature images is improved, and a high-precision image recognition model for the power transmission line channel image is constructed based on a high-computation-power GPU as a hardware basis in combination with a tensor operation calculation mode.
Specifically, in step S3, the image recognition model is used to automatically recognize buildings, roads, and towers in the channel orthophoto image generated in step one, and obtain frame mark ranges of each category. And assigning the three-dimensional point category attribute in the grid corresponding to the pixels in the category frame mark range to the type code identified by the image according to the one-to-one correspondence between the pixels and the point cloud grid.
Specifically, in the step S4, based on the crude extraction result in the step S3, different morphological feature thresholds are set for different classification results, and the morphological features of the point set after the crude classification are distinguished and denoised to obtain a fine classification result.
Specifically, in step S4, the definition of each feature is as follows:
building characteristics: the building is divided into an inclined top or flat top plane, and the boundary point and the ground have elevation sudden change of more than 2 m. Building a triangulation network by using the pre-classified building point cloud extracted in the step S3 and a greedy triangulation algorithm, and building a topological relation; and then calculating the normal vector of each three-dimensional point in the point cloud, extracting a normal vector change point and performing height difference comparison on a neighborhood point within 0.5m of the normal vector change point by utilizing the characteristic of the fixation of the building oblique top or flat top normal vector, and when the maximum height difference value is more than 2m, taking the point as a building boundary point. And constructing a convex hull for the extracted boundary points, extracting the outer boundary to obtain the whole outer frame of the building, filtering the points outside the frame, and keeping the points in the frame as the point cloud of the building.
Road characteristics: the road is characterized by a smooth curved surface, and the normal vector is continuous and less than 45 degrees. Constructing a triangulation network by using the pre-classified road point cloud extracted in the step S3 and a greedy triangulation algorithm, and constructing a topological relation; calculating a normal vector of each three-dimensional point in the point cloud, filtering noise points with an included angle of the normal vector and the XOY plane larger than 45 degrees, simultaneously extracting neighborhood points within 1m of each three-dimensional point for normal vector comparison, and determining the three-dimensional points with the included angle of the normal vector larger than 15 degrees as road boundaries.
It is another object of the present invention to provide a computer apparatus, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the computer program to implement the method as described above.
It is a further object of the invention to provide a computer-readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, carries out the method as set forth above.
The invention has the beneficial effects that: the invention introduces an image recognition technology on the basis of the traditional classification technology, utilizes texture information contained in the color point cloud to assist point cloud classification, and predefines the point cloud classification before the traditional classification. The method improves the classification effect on buildings, roads and towers.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the present invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
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In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail with reference to the accompanying drawings, in which:
FIG. 1 is a schematic flow chart of the method of the present invention.
Detailed Description
Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. It should be understood that the preferred embodiments are illustrative of the invention only and are not limiting upon the scope of the invention.
As shown in the figure, the method for classifying the laser LiDAR point cloud of the power transmission line assisted by image recognition in the embodiment comprises the following steps:
step S1: performing color point cloud dimension reduction processing to generate a channel orthoimage;
in this embodiment, the method specifically includes:
using point cloud plane coordinates in the upper left corner (X)min,Ymax) As a starting point, dividing a plane two-dimensional grid along an X axis and a Y axis by setting the grid size dL, and setting the coordinate value of an arbitrary point P plane in a point cloud as (X)P,YP) Then, the grid position is (u)P,vP)。
And dividing each point in the color point cloud into corresponding grids according to the formula. Constructing an RGB channel image having a pixel size of [ (X)max-Xmin)/dL+1]×[(Ymax-Ymin)/dL+1]And the pixel RGB value is the RGB mean value of the three-dimensional points in the corresponding grid, and a channel orthophotograph is generated.
Step S2: the method comprises the steps of collecting image data of a power transmission line channel, manufacturing building and road labels, establishing a power transmission line ground feature training sample library, extracting image multi-dimensional depth features from the sample library through a u-net network, improving feature expression capability of various ground feature images, and establishing a high-precision image recognition model for the power transmission line channel image by taking a high-computation-power GPU as a hardware basis and combining a tensor operation calculation mode.
Step S3: performing rough extraction on roads and buildings based on image recognition; in this embodiment, the image recognition model is used to automatically recognize buildings, roads, and towers in the channel ortho-image generated in step one, and frame mark ranges of various categories are obtained. And assigning the three-dimensional point category attribute in the grid corresponding to the pixels in the category frame mark range to the type code identified by the image according to the one-to-one correspondence between the pixels and the point cloud grid.
Step S4: and carrying out fine classification on roads and buildings based on morphological characteristics. In this embodiment, based on the coarse extraction result in step S3, different morphological feature thresholds are set for different classification results, and the morphological features of the point set after the coarse classification are distinguished and denoised to obtain a fine classification result.
In the present embodiment, the definition of each feature is as follows:
building characteristics: the building is divided into an inclined top or flat top plane, and the boundary point and the ground have elevation sudden change of more than 2 m. Building a triangulation network by using the pre-classified building point cloud extracted in the step S3 and a greedy triangulation algorithm, and building a topological relation; and then calculating the normal vector of each three-dimensional point in the point cloud, extracting a normal vector change point and performing height difference comparison on a neighborhood point within 0.5m of the normal vector change point by utilizing the characteristic of the fixation of the building oblique top or flat top normal vector, and when the maximum height difference value is more than 2m, taking the point as a building boundary point. And constructing a convex hull for the extracted boundary points, extracting the outer boundary to obtain the whole outer frame of the building, filtering the points outside the frame, and keeping the points in the frame as the point cloud of the building.
Road characteristics: the road is characterized by a smooth curved surface, and the normal vector is continuous and less than 45 degrees. Constructing a triangulation network by using the pre-classified road point cloud extracted in the step S3 and a greedy triangulation algorithm, and constructing a topological relation; calculating a normal vector of each three-dimensional point in the point cloud, filtering noise points with an included angle of the normal vector and the XOY plane larger than 45 degrees, simultaneously extracting neighborhood points within 1m of each three-dimensional point for normal vector comparison, and determining the three-dimensional points with the included angle of the normal vector larger than 15 degrees as road boundaries.
It should be noted that any process or method descriptions in flow charts of the present invention or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and that the scope of the preferred embodiments of the present invention includes additional implementations in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, and the program may be stored in a computer readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
Finally, the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered by the claims of the present invention.
Claims (9)
1. A power transmission line laser LiDAR point cloud classification method assisted by image recognition is characterized by comprising the following steps: the method comprises the following steps:
step S1: performing color point cloud dimension reduction processing to generate a channel orthoimage;
step S2: establishing a ground object sample library of the power transmission line, and establishing an image recognition model;
step S3: performing rough extraction on roads and buildings based on image recognition;
step S4: and carrying out fine classification on roads and buildings based on morphological characteristics.
2. The image recognition assisted power transmission line laser LiDAR point cloud classification method of claim 1, wherein: the step S1 specifically includes:
using point cloud plane coordinates in the upper left corner (X)min,Ymax) As a starting point, dividing a plane two-dimensional grid along an X axis and a Y axis by setting the grid size dL, and setting the coordinate value of an arbitrary point P plane in a point cloud as (X)P,YP) Then, the grid position is (u)P,vP)。
According to the aboveAnd (4) dividing each point in the color point cloud into corresponding grids. Constructing an RGB channel image having a pixel size of [ (X)max-Xmin)/dL+1]×[(Ymax-Ymin)/dL+1]And the pixel RGB is the RGB mean value of the three-dimensional points in the corresponding grid, and a channel orthophotograph is generated.
3. The image recognition assisted power transmission line laser LiDAR point cloud classification method of claim 1, wherein: and step S2, collecting image data of the power transmission line channel, making building and road labels, establishing a power transmission line ground feature training sample library, extracting image multi-dimensional depth features from the sample library through a u-net network, improving feature expression capability of various ground feature images, and constructing a high-precision image recognition model aiming at the power transmission line channel image by combining a tensor operation calculation mode based on a high-computation-power GPU (graphics processing unit) as a hardware basis.
4. The image recognition assisted power transmission line laser LiDAR point cloud classification method of claim 1, wherein: and step S3, automatically identifying buildings, roads, and towers in the channel orthophoto image generated in step one by using the image identification model, and acquiring frame mark ranges of each category. And assigning the three-dimensional point category attribute in the grid corresponding to the pixels in the category frame mark range to the type code identified by the image according to the one-to-one correspondence between the pixels and the point cloud grid.
5. The image recognition assisted power transmission line laser LiDAR point cloud classification method of claim 1, wherein: and S4, setting different morphological feature thresholds for different classification results based on the crude extraction result of the step S3, and distinguishing and denoising morphological features of the point set after the crude classification to obtain a fine classification result.
6. The image recognition-assisted power transmission line laser LiDAR point cloud classification method according to any one of claims 1 to 5, characterized in that: in step S4, the architectural features are defined as follows:
building characteristics: the building is divided into an inclined top or flat top plane, the boundary point and the ground have elevation mutation of more than 2m, the pre-classified building point cloud extracted in the step S3 is utilized, a triangular net is constructed by a greedy triangular algorithm, and a topological relation is constructed; and then calculating a normal vector of each three-dimensional point in the point cloud, extracting a normal vector change point and performing height difference comparison on a neighborhood point within 0.5m of the normal vector change point by utilizing the characteristic of fixing the building oblique top or flat top normal vector, when the maximum height difference value is more than 2m, taking the point as a building boundary point, constructing a convex hull on the extracted boundary point, extracting an outer boundary, obtaining an integral outer frame of the building, filtering the points outside the frame, and keeping the points in the frame as the building point cloud.
7. The image recognition-assisted power transmission line laser LiDAR point cloud classification method according to any one of claims 1 to 5, characterized in that: in step S4, the road characteristics are defined as follows:
road characteristics: the morphological characteristics of the road are a smooth curved surface, the normal vector is continuous and is less than 45 degrees, a triangular network is constructed by utilizing the pre-classified road point cloud extracted in the step S3 and a greedy triangular algorithm, and a topological relation is constructed; calculating a normal vector of each three-dimensional point in the point cloud, filtering noise points with an included angle of the normal vector and the XOY plane larger than 45 degrees, simultaneously extracting neighborhood points within 1m of each three-dimensional point for normal vector comparison, and determining the three-dimensional points with the included angle of the normal vector larger than 15 degrees as road boundaries.
8. A computer apparatus comprising a memory, a processor, and a computer program stored on the memory and capable of running on the processor, wherein: the processor, when executing the computer program, implements the method of any of claims 1 to 7.
9. A computer-readable storage medium having stored thereon a computer program, characterized in that: the computer program, when executed by a processor, implements the method of any one of claims 1 to 7.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113569914A (en) * | 2021-06-29 | 2021-10-29 | 山东信通电子股份有限公司 | Power transmission line inspection method and system fusing point cloud data |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160309065A1 (en) * | 2015-04-15 | 2016-10-20 | Lytro, Inc. | Light guided image plane tiled arrays with dense fiber optic bundles for light-field and high resolution image acquisition |
| US9672734B1 (en) * | 2016-04-08 | 2017-06-06 | Sivalogeswaran Ratnasingam | Traffic aware lane determination for human driver and autonomous vehicle driving system |
| CN109977921A (en) * | 2019-04-11 | 2019-07-05 | 广东电网有限责任公司 | A kind of transmission line of electricity perils detecting method |
| CN111507423A (en) * | 2020-04-24 | 2020-08-07 | 国网湖南省电力有限公司 | Engineering quantity calculation method for cleaning transmission line channel |
| CN111915662A (en) * | 2019-05-07 | 2020-11-10 | 北京京东尚科信息技术有限公司 | Three-dimensional laser point cloud data preprocessing method and device |
| CN112013830A (en) * | 2020-08-20 | 2020-12-01 | 中国电建集团贵州电力设计研究院有限公司 | Accurate positioning method for unmanned aerial vehicle inspection image detection defects of power transmission line |
-
2021
- 2021-02-02 CN CN202110142298.XA patent/CN112884026B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160309065A1 (en) * | 2015-04-15 | 2016-10-20 | Lytro, Inc. | Light guided image plane tiled arrays with dense fiber optic bundles for light-field and high resolution image acquisition |
| US9672734B1 (en) * | 2016-04-08 | 2017-06-06 | Sivalogeswaran Ratnasingam | Traffic aware lane determination for human driver and autonomous vehicle driving system |
| CN109977921A (en) * | 2019-04-11 | 2019-07-05 | 广东电网有限责任公司 | A kind of transmission line of electricity perils detecting method |
| CN111915662A (en) * | 2019-05-07 | 2020-11-10 | 北京京东尚科信息技术有限公司 | Three-dimensional laser point cloud data preprocessing method and device |
| CN111507423A (en) * | 2020-04-24 | 2020-08-07 | 国网湖南省电力有限公司 | Engineering quantity calculation method for cleaning transmission line channel |
| CN112013830A (en) * | 2020-08-20 | 2020-12-01 | 中国电建集团贵州电力设计研究院有限公司 | Accurate positioning method for unmanned aerial vehicle inspection image detection defects of power transmission line |
Non-Patent Citations (2)
| Title |
|---|
| K. SIKORSKA-ŁUKASIEWICZ: "Methods of automatic vegetation encroachment detection for high voltage power lines" * |
| 孙苏玉: "林区道路3D信息采集与建模方法研究", 《中国优秀硕士学位论文全文数据库 (农业科技辑)》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113569914A (en) * | 2021-06-29 | 2021-10-29 | 山东信通电子股份有限公司 | Power transmission line inspection method and system fusing point cloud data |
| CN113569914B (en) * | 2021-06-29 | 2024-02-09 | 山东信通电子股份有限公司 | Point cloud data fusion type power transmission line inspection method and system |
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