CN112327930A - Routing inspection path determining method and device - Google Patents
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Abstract
The invention discloses a method and a device for determining an inspection path, wherein when a signal which is sent by a sensor and used for indicating the inspection path is not received, an image of a target inspection area is firstly obtained, then objects in the image are classified to determine target objects in the image, and finally the optimal inspection path of an unmanned aerial vehicle is determined according to the target objects in the image, so that the unmanned aerial vehicle can obtain related information of the target objects, and an automatic inspection task is executed according to the optimal inspection path.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a method and a device for determining a routing inspection path.
Background
The unmanned aerial vehicle flight system generally determines the position and the attitude of the unmanned aerial vehicle flight system through a combined inertial navigation system, and realizes flight operation in a target area. Because the accuracy and reliability of input information are difficult to ensure by a single sensor signal, and the external environment information is not reflected enough, the robot cannot make a correct decision, and the adoption of a plurality of sensors has many advantages, for example, the plurality of sensors can provide redundant information and complementary information of the same environment; the multi-information can be parallelly and rapidly analyzed for the field environment, for example, in the field of unmanned aerial vehicle inspection power equipment, each tower can be provided with a sensor, and the unmanned aerial vehicle can automatically inspect the complete condition of the power transmission line according to the signal sent by the sensor.
For example, in chinese patent publication No. CN111028377A and publication No. 2020.4.17, which is a system for routing inspection of a power transmission line and a method for transmitting routing inspection data, the power transmission line is detected by a plurality of additional sensors, so that rapid routing inspection of an unmanned aerial vehicle is realized.
Disclosure of Invention
In order to solve the technical problems that when one or more sensors are in fault, an unmanned aerial vehicle cannot decide a routing inspection route and routing inspection tasks are delayed, the invention provides a routing inspection path determining method and a device.
The technical scheme of the invention is as follows:
a routing inspection path determining method is applied to an unmanned aerial vehicle and comprises the following steps:
when a signal which is sent by a sensor and used for indicating an inspection path is not received, acquiring an image of a target inspection area;
classifying objects in the image to determine target objects in the image;
and determining the optimal routing inspection path of the unmanned aerial vehicle according to the target object in the image.
Optionally, the process of classifying the object in the image to determine the target object in the image is as follows:
and acquiring point cloud data corresponding to the image, and determining a target object according to the point cloud data.
Optionally, the process of determining the target object according to the point cloud data is as follows:
establishing a plurality of object models in the image based on the point cloud data;
splitting the object model into a plurality of intermediate models for each object model;
a target object is determined based on the plurality of intermediate models.
Optionally, the process of splitting the object model into a plurality of intermediate models is as follows:
traversing a plurality of object models, and splitting the object model A into a plurality of intermediate models based on each corner point if the object model A has a plurality of corner points;
optionally, the process of determining the target object based on the plurality of intermediate models is as follows:
and inputting the plurality of intermediate models into a preset classification model so as to determine the target object corresponding to each intermediate model.
Optionally, the intermediate model comprises a power line model;
the process of determining the target object based on the power line model is as follows:
taking each angular point in the power transmission line as a demarcation point, and separating the power transmission line into a plurality of sections;
separating each section of transmission line into a plurality of layers in the vertical direction;
separating each layer of power transmission lines into a plurality of power transmission lines in the parallel direction;
and carrying out curve fitting on each transmission line to obtain a target object.
Optionally, the intermediate model comprises a tower;
the process of splitting the object model into a plurality of intermediate models is as follows:
and reducing the cloud dimension of the point cloud data through vertical histogram projection and uniform grid dispersion to obtain a two-dimensional gray image, extracting a point with the maximum histogram response, and setting the point as a tower.
Optionally, the process of determining the optimal routing inspection path of the unmanned aerial vehicle according to the target object in the image is as follows:
and determining the optimal routing inspection path of the unmanned aerial vehicle according to the position relation among the target objects.
Optionally, the target object comprises: towers and transmission lines;
the process of determining the optimal routing inspection path of the unmanned aerial vehicle according to the target object in the image is as follows:
calculate the contained angle of each shaft tower horizontal direction and power transmission line to set up the contained angle into unmanned aerial vehicle steering angle, when in order to predetermine the region on unmanned aerial vehicle reaches the shaft tower, based on unmanned aerial vehicle steering angle adjustment direction of flight.
The invention also provides a routing inspection path determining device, which comprises:
the acquisition module is used for acquiring an image of a target inspection area when a signal which is sent by the sensor and used for indicating an inspection path is not received;
the classification module is used for classifying the object in the image to determine a target object in the image;
and the determining module is used for determining the optimal routing inspection path of the unmanned aerial vehicle according to the target object in the image.
Compared with the prior art, the invention has the following advantages:
according to the invention, when a signal which is sent by the sensor and used for indicating the routing inspection path is not received, the image of the target routing inspection area is firstly obtained, then the objects in the image are classified to determine the target objects in the image, and finally the optimal routing inspection path of the unmanned aerial vehicle is determined according to the target objects in the image, so that the unmanned aerial vehicle can obtain the relevant information of the target objects, the automatic routing inspection task is executed according to the optimal routing inspection path, when one or more sensors have faults, the unmanned aerial vehicle can automatically decide the routing inspection route, the routing inspection task is not delayed, and the routing inspection efficiency is effectively improved.
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Fig. 1 is a flowchart of a patrol route determination method of the present invention;
fig. 2 is a schematic structural diagram of the inspection path determining apparatus of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Example 1
A routing inspection path determining method is applied to an unmanned aerial vehicle, and as shown in figure 1, comprises the following steps:
step S1, when a signal which is sent by the sensor and used for indicating the routing inspection path is not received, an image of the target routing inspection area is obtained;
in practical application, when unmanned aerial vehicle can't receive the sensor signal, owing to lose direction indication, self can't realize turning to, continue tasks such as equipment of patrolling and examining, so this embodiment when not receiving the signal that is used for instructing the route of patrolling and examining that the sensor sent, acquires the target and patrols and examines regional image, and wherein, the target is patrolled and examined regional image and can be the image that unmanned aerial vehicle shot at present.
Step S2, classifying the object in the image to determine the target object in the image;
in this step, the image may include a plurality of target objects that need to be detected or acquire information, such as power equipment, water conservancy equipment, and the like, where a specific target object is related to the task executed by the unmanned aerial vehicle, and this embodiment is not specifically limited to this;
in addition, the image may also include objects unrelated to the unmanned aerial vehicle task, such as background objects, so that it is necessary to classify the objects in the image and then retain the target objects related to the unmanned aerial vehicle task.
Specifically, in step S2, the process of classifying the object in the image to determine the target object in the image is as follows:
and acquiring point cloud data corresponding to the image, and determining a target object according to the point cloud data.
In this embodiment, the point cloud data may be point cloud data of an inspection area acquired by an unmanned aerial vehicle, where the point cloud data (point cloud data) refers to scanning data recorded in the form of points, each point includes three-dimensional coordinates, and some points may include color information (RGB) or reflection Intensity information (Intensity), and may be determined specifically according to actual conditions; in the embodiment, the point cloud data is used for synthesizing the model of each object in the image, so that the required target object can be screened out.
Specifically, the process of determining the target object from the point cloud data is as follows:
establishing a plurality of object models in the image based on the point cloud data;
splitting the object model into a plurality of intermediate models for each object model;
a target object is determined based on the plurality of intermediate models.
In practical application, when a laser radar carried by an unmanned aerial vehicle is used for line patrol of an electric power system, a large number of three-dimensional discrete point clouds about a transmission line, a tower, a surface plant and a building are obtained, and after point cloud classification of different objects is realized, point cloud materialization is carried out, so that reconstruction and roaming of a three-dimensional scene can be realized.
However, objects with similar forms may be classified into point clouds of the same category based on the point cloud classification, which causes modeling errors of point cloud data, for example, for autonomous inspection of a multi-rotor unmanned aerial vehicle, conditions of a power transmission line and a tower are most concerned; because the two are connected, the point clouds are generally classified into the same category (transmission line and tower) during point cloud classification, and the tower and the transmission line need to be separated.
Further, the process of splitting the object model into a plurality of intermediate models is as follows:
traversing a plurality of object models, and splitting the object model A into a plurality of intermediate models based on each corner point if the object model A has a plurality of corner points;
in this embodiment, the corner points can be understood as the corners of the object model, and in practical applications, for example, the tower and the power transmission line, although both are linear on a two-dimensional image plane, they are easily classified into the same type, and are merged into the same model, at this time, a plurality of vertical lines, i.e., the tower, are added to the originally linear power transmission line model, so that the object model a is split into a plurality of intermediate models based on each corner point, and the model can be reconstructed to eliminate the modeling deviation caused by the above reasons.
Accordingly, the process of determining the target object based on the plurality of intermediate models is as follows:
and inputting the plurality of intermediate models into a preset classification model so as to determine the target object corresponding to each intermediate model.
In this embodiment, the training samples of the classification models may be trained by using a large number of image files or point cloud files marked with target objects, and the main point is that the target objects corresponding to the respective intermediate models can be determined in detail.
Specifically, the intermediate model includes a power line model, and the process of determining the target object based on the power line model is as follows:
taking each angular point in the power transmission line as a demarcation point, and separating the power transmission line into a plurality of sections;
separating each section of transmission line into a plurality of layers in the vertical direction;
separating each layer of power transmission lines into a plurality of power transmission lines in the parallel direction;
and carrying out curve fitting on each transmission line to obtain a target object.
In practical application, there is discontinuous defect in the modeling of point cloud data, for example, many line segments may be included in the power transmission line model, so it is necessary to convert the intermediate model into a more accurate target object model, this embodiment provides a way of fitting of multiple line segments, because the tower is set up only by changing the trend of the power transmission line under most circumstances, so each corner point in the power transmission line can be used as a demarcation point first, the power transmission line is separated into multiple segments, then each segment of power transmission line is separated into multiple layers in the vertical direction, because the number of electric wires of the power transmission line is certain, so it is necessary to use the hierarchy as a unit, and the non-electric wire units side by side are excluded, that is: the method comprises the steps of separating each layer of power transmission line into a plurality of power transmission lines in the parallel direction, and finally performing curve fitting on each power transmission line, wherein the curve fitting refers to fitting all line segments which are divided in a power transmission line model to obtain a uniquely determined target object (single straight line), so that the unmanned aerial vehicle can determine the routing inspection direction.
In addition, the step of splitting the object model into a plurality of intermediate models can be realized in a mode that the intermediate models comprise towers, the cloud dimension of the point cloud data is reduced through vertical histogram projection and uniform grid dispersion to obtain two-dimensional gray images, and points with the largest histogram response can be directly extracted and set as the towers due to the fact that the density of the point data is increased.
And step S3, determining the optimal routing inspection path of the unmanned aerial vehicle according to the target object in the image, so that the unmanned aerial vehicle can acquire the information of the target object.
According to the embodiment, when a signal which is sent by a sensor and used for indicating the routing inspection path is not received, the image of the target routing inspection area is firstly obtained, then the objects in the image are classified to determine the target objects in the image, and finally the optimal routing inspection path of the unmanned aerial vehicle is determined according to the target objects in the image, so that the unmanned aerial vehicle can obtain the relevant information of the target objects, and the automatic routing inspection task is executed according to the optimal routing inspection path.
Further, in this embodiment, the process of determining the optimal routing inspection path of the unmanned aerial vehicle according to the target object in the image is as follows:
and determining the optimal routing inspection path of the unmanned aerial vehicle according to the position relation among the target objects.
In this embodiment, consider that unmanned aerial vehicle actually patrols and examines the in-process, there may be the incidence relation between each target object, for example shaft tower and power transmission line, the overhead power transmission line that has put up of shaft tower, so when detecting the shaft tower, need confirm the power transmission line direction again, and then change unmanned aerial vehicle flight direction, until flying next shaft tower, realize that unmanned aerial vehicle is automatic to patrol and examine the process.
Specifically, the target object includes a tower and a power line;
the process of determining the optimal routing inspection path of the unmanned aerial vehicle according to the target object in the image is as follows: calculate the contained angle of each shaft tower horizontal direction and power transmission line to set up the contained angle into unmanned aerial vehicle steering angle, when in order to predetermine the region on unmanned aerial vehicle reaches the shaft tower, based on unmanned aerial vehicle steering angle adjustment direction of flight.
In this embodiment, a routing inspection path determining apparatus is further provided, as shown in fig. 2, including:
the acquisition module is used for acquiring an image of a target inspection area when a signal which is sent by the sensor and used for indicating an inspection path is not received;
the classification module is used for classifying the object in the image to determine a target object in the image;
and the determining module is used for determining the optimal routing inspection path of the unmanned aerial vehicle according to the target object in the image so that the unmanned aerial vehicle can acquire the information of the target object.
In this embodiment, there is also provided an electronic device including: the system comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete mutual communication through the communication bus;
a memory for storing a computer program;
and the processor is used for realizing the routing inspection path determining method when executing the program stored in the memory.
According to the electronic equipment provided by the embodiment, when the signal which is sent by the sensor and used for indicating the routing inspection path is not received, the processor firstly obtains the image of the target routing inspection area, then classifies the objects in the image to determine the target objects in the image, and finally determines the optimal routing inspection path of the unmanned aerial vehicle according to the target objects in the image, so that the unmanned aerial vehicle can obtain the relevant information of the target objects, and the automatic routing inspection task is executed according to the optimal routing inspection path.
The communication bus mentioned in the above electronic device may be a Serial Peripheral Interface (SPI) bus or an integrated circuit (ICC) bus. The communication bus may be divided into an address bus, a data bus, a control bus, etc.
The communication interface is used for communication between the electronic equipment and other equipment.
The memory may include a Random Access Memory (RAM), or may also include a non-volatile memory (non-volatile memory), such as at least one disk memory. Optionally, the memory may also be at least one memory device located remotely from the processor.
The Processor may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the integrated circuit may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic device, or discrete hardware components.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
It is noted that, in this document, relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The foregoing are merely exemplary embodiments of the present invention, which enable those skilled in the art to understand or practice the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. The utility model provides a patrol and examine route determination method, is applied to unmanned aerial vehicle, its characterized in that includes:
when a signal which is sent by a sensor and used for indicating an inspection path is not received, acquiring an image of a target inspection area;
classifying objects in the image to determine target objects in the image;
and determining the optimal routing inspection path of the unmanned aerial vehicle according to the target object in the image so that the unmanned aerial vehicle can acquire the information of the target object.
2. The inspection path determining method according to claim 1, wherein the process of classifying the objects in the image to determine the target object in the image is as follows:
and acquiring point cloud data corresponding to the image, and determining a target object according to the point cloud data.
3. The inspection path determining method according to claim 2, wherein the process of determining the target object from the point cloud data is as follows:
establishing a plurality of object models in the image based on the point cloud data;
splitting the object model into a plurality of intermediate models for each object model;
a target object is determined based on the plurality of intermediate models.
4. The inspection path determining method according to claim 3, wherein splitting the object model into a plurality of intermediate models is as follows:
and traversing the object models, and splitting the object model A into a plurality of intermediate models based on each corner point if the object model A has a plurality of corner points.
5. The inspection path determining method according to claim 4, wherein the process of determining the target object based on the plurality of intermediate models is as follows:
and inputting the plurality of intermediate models into a preset classification model so as to determine the target object corresponding to each intermediate model.
6. The inspection path determination method of claim 5, wherein the intermediate model includes a power line model;
the process of determining the target object based on the power line model is as follows:
taking each angular point in the power transmission line as a demarcation point, and separating the power transmission line into a plurality of sections;
separating each section of transmission line into a plurality of layers in the vertical direction;
separating each layer of power transmission lines into a plurality of power transmission lines in the parallel direction;
and carrying out curve fitting on each transmission line to obtain a target object.
7. The inspection path determining method according to claim 6, wherein the intermediate model includes a tower;
the process of splitting the object model into a plurality of intermediate models is as follows:
and reducing the cloud dimension of the point cloud data through vertical histogram projection and uniform grid dispersion to obtain a two-dimensional gray image, extracting a point with the maximum histogram response, and setting the point as a tower.
8. The inspection path determining method according to claim 7, wherein the process of determining the optimal inspection path of the unmanned aerial vehicle according to the target object in the image is as follows:
and determining the optimal routing inspection path of the unmanned aerial vehicle according to the position relation among the target objects.
9. The inspection path determining method according to claim 8, wherein the target object includes: towers and transmission lines;
the process of determining the optimal routing inspection path of the unmanned aerial vehicle according to the target object in the image is as follows:
calculate the contained angle of each shaft tower horizontal direction and power transmission line to set up the contained angle into unmanned aerial vehicle steering angle, when in order to predetermine the region on unmanned aerial vehicle reaches the shaft tower, based on unmanned aerial vehicle steering angle adjustment direction of flight.
10. A routing inspection path determining device, comprising:
the acquisition module is used for acquiring an image of a target inspection area when a signal which is sent by the sensor and used for indicating an inspection path is not received;
the classification module is used for classifying the object in the image to determine a target object in the image;
and the determining module is used for determining the optimal routing inspection path of the unmanned aerial vehicle according to the target object in the image so that the unmanned aerial vehicle can acquire the information of the target object.
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| CN115981366A (en) * | 2022-12-30 | 2023-04-18 | 广东电网有限责任公司肇庆供电局 | Unmanned aerial vehicle line-tracing flight control method based on real-time identification of power line point cloud target |
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