CN115164908A - Unmanned aerial vehicle navigation method and device based on plant canopy landmarks - Google Patents
Unmanned aerial vehicle navigation method and device based on plant canopy landmarks Download PDFInfo
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Abstract
The invention provides an unmanned aerial vehicle navigation method and device based on plant canopy landmarks, wherein after a target image corresponding to a target vegetation area shot by an unmanned aerial vehicle is obtained, the plant canopy landmarks corresponding to target plants in the target vegetation area are obtained based on the target image; and then extracting the target navigation route of the unmanned aerial vehicle based on the plant canopy landmarks. Because the canopy of different grade type plants has its specific grey scale gradient distribution rule, consequently, can carry out image segmentation based on the grey scale gradient distribution rule that this plant canopy corresponds, discern the plant canopy landmark that target plant corresponds, because the specific position of every target plant can be clearly and accurately represented to the plant canopy landmark, consequently, the target navigation route of unmanned aerial vehicle is drawed out based on this plant canopy landmark, can satisfy navigation demand to accurate navigation route among the slow speed high accuracy navigation processes such as weeding, fixed point fertilization.
Description
Technical Field
The invention belongs to the technical field of unmanned aerial vehicle navigation, and particularly relates to an unmanned aerial vehicle navigation method and device based on plant canopy landmarks.
Background
The visual navigation is a navigation technology which utilizes a camera to sense environmental information as the basis of airplane flight. In recent years, with the wide application of unmanned aerial vehicles in the aspects of crop growth data acquisition, pesticide spraying, pest and disease detection and the like, the unmanned aerial vehicle navigation technology based on vision is more and more concerned. Plant-based visual navigation is one of the main navigation modes in agriculture, in the process, vegetation crop rows are approximately regarded as a straight line, however, the dotted line of vegetation crop rows in the real environment is discrete, and a more accurate navigation route is often needed in the slow-speed high-precision navigation processes of weeding, fixed-point fertilization and the like. Therefore, how to navigate with a more accurate navigation route in the process of plant-based visual navigation is a problem to be solved.
Disclosure of Invention
The invention provides an unmanned aerial vehicle navigation method and device based on plant canopy landmarks, and aims to solve the problem of navigating by using a more specific and accurate navigation route in a plant-based visual navigation process.
In order to solve or improve the technical problem to a certain extent, according to an aspect of the present invention, there is provided a method for navigating an unmanned aerial vehicle based on a plant canopy landmark, including:
acquiring a target image corresponding to a target vegetation area shot by an unmanned aerial vehicle;
obtaining plant canopy landmarks corresponding to target plants in the target vegetation area based on the target image;
and extracting a target navigation route of the unmanned aerial vehicle based on the plant canopy landmark.
In some embodiments, the obtaining, based on the target image, a vegetation canopy landmark corresponding to a target plant in the target vegetation area includes:
and carrying out image segmentation on the target image to obtain a plant canopy landmark corresponding to the target plant.
In some embodiments, the image segmentation on the target image to obtain the plant canopy landmark corresponding to the target plant includes:
and identifying a plant canopy landmark corresponding to the target plant from the target vegetation area based on the canopy gray gradient distribution of the target plant.
In some embodiments, the identifying a plant canopy landmark corresponding to the target plant from the target vegetation area based on the canopy gray scale gradient distribution of the target plant comprises:
classifying objects contained in the target image based on a gray gradient distribution rule corresponding to the canopy of the target plant in the radial direction to obtain a classification result;
and in response to the classification result indicating that the object is the target plant, marking the object as a plant canopy landmark corresponding to the target plant.
In some embodiments, the classifying the object included in the target image to obtain a classification result includes: classifying the object contained in the target image through a pre-trained inclusion-V3 classifier, and obtaining a classification result which is output by the inclusion-V3 classifier and used for representing whether the object is the target plant.
In some embodiments, said extracting a target navigation route for said drone based on said plant canopy landmark comprises:
and extracting a piecewise linearized local navigation route from the plant canopy landmark according to the canopy gray gradient distribution rule of the plant canopy landmark.
According to another aspect of the present invention, there is provided a drone navigation device based on plant canopy landmarks, comprising:
the target image acquisition unit is used for acquiring a target image corresponding to a target vegetation area shot by the unmanned aerial vehicle;
a plant canopy landmark obtaining unit, configured to obtain, based on the target image, a plant canopy landmark corresponding to a target plant in the target vegetation area;
and the target navigation route extracting unit is used for extracting the target navigation route of the unmanned aerial vehicle based on the plant canopy landmark.
In some embodiments, the obtaining, based on the target image, a vegetation canopy landmark corresponding to a target plant in the target vegetation area includes:
and carrying out image segmentation on the target image to obtain a plant canopy landmark corresponding to the target plant.
In some embodiments, the image segmentation on the target image to obtain the plant canopy landmark corresponding to the target plant includes:
and identifying a plant canopy landmark corresponding to the target plant from the target vegetation area based on the canopy gray gradient distribution of the target plant.
In some embodiments, the identifying a plant canopy landmark corresponding to the target plant from the target vegetation area based on the canopy gray scale gradient distribution of the target plant comprises:
classifying objects contained in the target image based on a gray gradient distribution rule corresponding to the canopy of the target plant in the radial direction to obtain a classification result;
in response to the classification result indicating that the object is the target plant, marking the object as a plant canopy landmark corresponding to the target plant.
In some embodiments, the classifying the object included in the target image to obtain a classification result includes: classifying the object contained in the target image through a pre-trained inclusion-V3 classifier, and obtaining a classification result which is output by the inclusion-V3 classifier and used for representing whether the object is the target plant.
In some embodiments, said extracting a target navigation route for said drone based on said plant canopy landmark comprises:
and extracting the piecewise linearized local navigation line from the plant canopy landmark according to the canopy gray gradient distribution rule of the plant canopy landmark.
According to another aspect of the present invention, there is provided an electronic device comprising a processor and a memory; wherein the memory is configured to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement the method of any of the above embodiments.
According to another aspect of the present invention, there is provided a computer readable storage medium having one or more computer instructions stored thereon, wherein the instructions are executed by a processor to implement the method according to any one of the above embodiments.
Compared with the prior art, the invention has the following advantages:
the unmanned aerial vehicle navigation method based on the plant canopy landmarks, provided by the invention, comprises the steps of obtaining a target image corresponding to a target vegetation area shot by an unmanned aerial vehicle, and obtaining the plant canopy landmarks corresponding to target plants in the target vegetation area based on the target image; and then extracting the target navigation route of the unmanned aerial vehicle based on the plant canopy landmarks. Because the canopy of different grade type plants has its specific grey scale gradient distribution rule, consequently, can carry out image segmentation based on the grey scale gradient distribution rule that this plant canopy corresponds, discern the plant canopy landmark that target plant corresponds, and extract unmanned aerial vehicle's target navigation circuit based on this plant canopy landmark, because above-mentioned plant canopy landmark can clearly and accurately represent the specific position of every target plant, consequently, the target navigation circuit of unmanned aerial vehicle is extracted based on this plant canopy landmark, can satisfy navigation demand to accurate navigation route among the slow-speed high accuracy navigation processes such as weeding, fixed point fertilization.
The foregoing description is only an overview of the technical solutions of the present invention, and in order to make the technical means of the present invention more clearly understood, the present invention may be implemented in accordance with the content of the description, and in order to make the above and other objects, features, and advantages of the present invention more clearly understood, the following preferred embodiments are described in detail with reference to the accompanying drawings.
Drawings
Fig. 1 is a flowchart of a method for navigating an unmanned aerial vehicle based on plant canopy landmarks according to an embodiment of the present application;
fig. 2 is a block diagram of a navigation device of a drone based on plant canopy landmarks according to an embodiment of the present application;
fig. 3 is a schematic logical structure diagram of an electronic device according to an embodiment of the present application.
Detailed Description
To further illustrate the technical means and effects of the present invention adopted to achieve the predetermined objects, the following detailed description will be given to the specific embodiments and effects of the user identity authentication method according to the present invention with reference to the accompanying drawings.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. This application is capable of implementation in many different ways than those herein set forth and of similar import by those skilled in the art without departing from the spirit and scope of this application, and thus this application is not limited to the specific implementations disclosed below.
The visual navigation is a navigation technology which utilizes a camera to sense environmental information as the basis of airplane flight. With the wide application of unmanned aerial vehicles in the aspects of crop growth data acquisition, pesticide spraying, pest detection and the like, the unmanned aerial vehicle navigation technology based on vision is more and more concerned. Visual navigation based on plant canopy landmarks is one of main navigation modes in agriculture, in the process, vegetation crop rows are approximately regarded as a straight line, however, broken lines of vegetation crop behaviors in real environment are discrete, and in slow-speed high-precision navigation processes such as weeding and fixed-point fertilization, more specific and precise navigation routes are often needed.
In order to perform unmanned aerial vehicle navigation by using a more specific and accurate navigation route in the visual navigation process based on the plant canopy landmarks, the application provides an unmanned aerial vehicle navigation method based on the plant canopy landmarks, an unmanned aerial vehicle navigation device based on the plant canopy landmarks, electronic equipment and a computer readable storage medium corresponding to the unmanned aerial vehicle navigation method based on the plant canopy landmarks. The following provides embodiments for detailed description of the above battery charging method, apparatus, electronic device and computer readable storage medium.
An embodiment of the application provides an unmanned aerial vehicle navigation method based on plant canopy landmarks, an application main body of the method can be a computing device application for unmanned aerial vehicle navigation, and the computing device application can run in an unmanned aerial vehicle navigation system and serve as a management module of an unmanned aerial vehicle navigation strategy. Fig. 1 is a flowchart of a method for navigating an unmanned aerial vehicle based on a plant canopy landmark according to a first embodiment of the present application, and the method for navigating an unmanned aerial vehicle based on a plant canopy landmark according to the present embodiment is described in detail below with reference to fig. 1. The following description refers to embodiments for the purpose of illustrating the principles of the methods, and is not intended to be limiting in actual use.
As shown in fig. 1, the unmanned aerial vehicle navigation method based on plant canopy landmarks provided in this embodiment includes the following steps:
s101, acquiring a target image corresponding to a target vegetation area shot by the unmanned aerial vehicle.
This step is used for obtaining the target image that the target vegetation area that unmanned aerial vehicle intaked corresponds, for example, unmanned aerial vehicle need realize the navigation tracking of unmanned aerial vehicle to the target plant in the target vegetation area through visual navigation technique in application processes such as crop growth data acquisition, pesticide spray, plant diseases and insect pests detection, and this process at first need intake the target vegetation area, obtains the target image that the target vegetation area corresponds. For example, in a slow-speed high-precision navigation process of weeding, fixed-point fertilization and the like on corns in a corn field by an unmanned aerial vehicle, an environment image corresponding to the corn field needs to be automatically acquired.
And S102, acquiring plant canopy landmarks corresponding to target plants in the target vegetation area based on the target image.
After the target image corresponding to the target vegetation area shot by the unmanned aerial vehicle is obtained in the above step, the step is used for performing image processing on the shot target image to obtain the plant canopy landmarks corresponding to the target plants in the target vegetation area, and specifically, performing image segmentation on the target image to obtain the plant canopy landmarks corresponding to the target plants. The image segmentation is to divide a target image into a plurality of specific and mutually disjoint regions with unique properties according to characteristics such as gray scale, color, spatial texture, geometric shape and the like, so that the characteristics show consistency or similarity in the same region, which is a key step from image processing to image analysis and is the crucial preprocessing of image recognition and computer vision. The existing image segmentation methods mainly include the following categories: a threshold-based segmentation method, a region-based segmentation method, an edge-based segmentation method, a particular theory-based segmentation method, and the like. The process of image segmentation is also a labeling process, i.e. pixels belonging to the same region are assigned the same number. When the image is divided, the image dividing process is easy to generate dividing errors due to uneven illumination, noise influence, unclear parts, shadows and the like in the image.
In this embodiment, the above-mentioned manner of performing image segmentation on the target image to obtain the plant canopy landmark corresponding to the target plant may specifically be: identifying a plant canopy landmark corresponding to a target plant from a target vegetation area based on canopy gray gradient distribution of the target plant, specifically, classifying objects contained in a target image based on a gray gradient distribution rule corresponding to a canopy of the target plant in a radial direction to obtain a classification result; and in response to the classification result indicating that the object is the target plant, marking the object as a plant canopy landmark corresponding to the target plant. For example, based on a gray gradient distribution rule corresponding to the canopy of the target plant in the radial direction, classifying the object contained in the target image through a pre-trained inclusion-V3 classifier to obtain a classification result output by the inclusion-V3 classifier and used for characterizing whether the classified object is the target plant, and the inclusion-V3 classifier may output a classification score of the object contained in the target image based on the input target image to characterize the intensity of each possible classification (e.g., which plant) of the object contained in the input target image; and if the classification result shows that the classified object contained in the target image is the target plant, marking the object as a plant canopy landmark corresponding to the target plant.
And S103, extracting the target navigation route of the unmanned aerial vehicle based on the plant canopy landmarks.
After the target image is subjected to image segmentation in the above steps, and the plant canopy landmarks corresponding to the target plants are obtained, the step is used for extracting the target navigation line of the unmanned aerial vehicle based on the plant canopy landmarks, specifically, according to the canopy gray gradient distribution rule of the plant canopy landmarks, the piecewise linearized local navigation line is extracted from the plant canopy landmarks, and the local path tracking of the unmanned aerial vehicle in the target vegetation area is realized.
According to the unmanned aerial vehicle navigation method based on the plant canopy landmarks, after a target image corresponding to a target vegetation area shot by an unmanned aerial vehicle is obtained, plant canopy landmarks corresponding to target plants in the target vegetation area are obtained based on the target image; and then extracting the target navigation route of the unmanned aerial vehicle based on the plant canopy landmarks. Because the canopy of different grade type plants has its specific grey scale gradient distribution rule, consequently, can carry out image segmentation based on the grey scale gradient distribution rule that this plant canopy corresponds, discern the plant canopy landmark that target plant corresponds, and extract unmanned aerial vehicle's target navigation circuit based on this plant canopy landmark, because above-mentioned plant canopy landmark can clearly and accurately represent the specific position of every target plant, consequently, the target navigation circuit of unmanned aerial vehicle is extracted based on this plant canopy landmark, can satisfy navigation demand to accurate navigation route among the slow-speed high accuracy navigation processes such as weeding, fixed point fertilization.
The first embodiment provides an unmanned aerial vehicle navigation method based on plant canopy landmarks, corresponding to the unmanned aerial vehicle navigation method, another embodiment of the application also provides an unmanned aerial vehicle navigation device based on plant canopy landmarks, and the device is arranged in a vehicle machine operating system and can be used as a battery charging control module. Since the device embodiments are substantially similar to the method embodiments and therefore are described relatively simply, reference may be made to the corresponding description of the method embodiments provided above for details of relevant technical features, and the following description of the device embodiments is merely illustrative.
Please refer to fig. 2 for understanding the embodiment, fig. 2 is a block diagram of the unmanned aerial vehicle navigation apparatus based on a plant canopy landmark provided in the embodiment, and as shown in fig. 2, the unmanned aerial vehicle navigation apparatus based on a plant canopy landmark provided in the embodiment includes:
a target image obtaining unit 201, configured to obtain a target image corresponding to a target vegetation area captured by an unmanned aerial vehicle;
a vegetation canopy landmark obtaining unit 202, configured to obtain a vegetation canopy landmark corresponding to a target plant in the target vegetation area based on the target image;
and a target navigation route extracting unit 203, configured to extract a target navigation route of the unmanned aerial vehicle based on the plant canopy landmark.
In some embodiments, the obtaining, based on the target image, a vegetation canopy landmark corresponding to a target plant in the target vegetation area includes:
and carrying out image segmentation on the target image to obtain a plant canopy landmark corresponding to the target plant.
In some embodiments, the image segmentation on the target image to obtain the plant canopy landmark corresponding to the target plant includes:
and identifying a plant canopy landmark corresponding to the target plant from the target vegetation area based on the canopy gray gradient distribution of the target plant.
In some embodiments, the identifying a vegetation canopy landmark corresponding to the target plant from the target vegetation area based on the canopy gray gradient distribution of the target plant includes:
classifying objects contained in the target image based on a gray gradient distribution rule corresponding to the canopy of the target plant in the radial direction to obtain a classification result;
and in response to the classification result indicating that the object is the target plant, marking the object as a plant canopy landmark corresponding to the target plant.
In some embodiments, the classifying the object included in the target image to obtain a classification result includes: classifying the object contained in the target image through a pre-trained inclusion-V3 classifier, and obtaining a classification result which is output by the inclusion-V3 classifier and used for representing whether the object is the target plant. In some embodiments, said extracting a target navigation route for said drone based on said plant canopy landmark comprises:
and extracting the piecewise linearized local navigation line from the plant canopy landmark according to the canopy gray gradient distribution rule of the plant canopy landmark.
The unmanned aerial vehicle navigation device based on the plant canopy landmarks, provided by the embodiment, acquires the target image corresponding to the target vegetation area shot by the unmanned aerial vehicle, and then acquires the plant canopy landmarks corresponding to the target plants in the target vegetation area based on the target image; and then extracting the target navigation route of the unmanned aerial vehicle based on the plant canopy landmarks. As the canopy of the different types of plants has the specific gray gradient distribution rule, the image segmentation can be carried out based on the gray gradient distribution rule corresponding to the plant canopy, the plant canopy landmark corresponding to the target plant is identified, and the target navigation route of the unmanned aerial vehicle is extracted based on the plant canopy landmark.
In the above embodiments, an unmanned aerial vehicle navigation method based on a plant canopy landmark and an unmanned aerial vehicle navigation apparatus based on a plant canopy landmark are provided, and in addition, another embodiment of the present application also provides an electronic device, which is relatively simple to describe because the embodiment of the electronic device is substantially similar to the embodiment of the method, and please refer to the corresponding description of the embodiment of the method in the detail section of the related technical features, and the following description of the embodiment of the electronic device is only illustrative. The embodiment of the electronic equipment is as follows:
please refer to fig. 3 for understanding the present embodiment, fig. 3 is a schematic diagram of an electronic device provided in the present embodiment.
As shown in fig. 3, the electronic device provided in this embodiment includes: a processor 301 and a memory 302;
the memory 302 is used for storing computer instructions for data processing, which when read and executed by the processor 301, perform the following operations:
acquiring a target image corresponding to a target vegetation area shot by an unmanned aerial vehicle;
obtaining plant canopy landmarks corresponding to target plants in the target vegetation area based on the target image;
and extracting a target navigation route of the unmanned aerial vehicle based on the plant canopy landmark.
In some embodiments, the obtaining, based on the target image, a vegetation canopy landmark corresponding to a target plant in the target vegetation area includes:
and carrying out image segmentation on the target image to obtain a plant canopy landmark corresponding to the target plant.
In some embodiments, the image segmentation of the target image to obtain the plant canopy landmark corresponding to the target plant includes:
and identifying a plant canopy landmark corresponding to the target plant from the target vegetation area based on the canopy gray gradient distribution of the target plant.
In some embodiments, the identifying a vegetation canopy landmark corresponding to the target plant from the target vegetation area based on the canopy gray gradient distribution of the target plant includes:
classifying objects contained in the target image based on a gray gradient distribution rule corresponding to the canopy of the target plant in the radial direction to obtain a classification result;
in response to the classification result indicating that the object is the target plant, marking the object as a plant canopy landmark corresponding to the target plant.
In some embodiments, the classifying the object included in the target image to obtain a classification result includes: classifying the object contained in the target image through a pre-trained inclusion-V3 classifier, and obtaining a classification result which is output by the inclusion-V3 classifier and used for representing whether the object is the target plant. In some embodiments, said extracting a target navigation route for said drone based on said plant canopy landmark comprises:
and extracting the piecewise linearized local navigation line from the plant canopy landmark according to the canopy gray gradient distribution rule of the plant canopy landmark.
By using the electronic device provided by the embodiment, after a target image corresponding to a target vegetation area shot by an unmanned aerial vehicle is obtained, plant canopy landmarks corresponding to target plants in the target vegetation area are obtained based on the target image; and then extracting the target navigation route of the unmanned aerial vehicle based on the plant canopy landmarks. Because the canopy of different grade type plants has its specific grey scale gradient distribution rule, consequently, can carry out image segmentation based on the grey scale gradient distribution rule that this plant canopy corresponds, discern the plant canopy landmark that target plant corresponds, and extract unmanned aerial vehicle's target navigation circuit based on this plant canopy landmark, because above-mentioned plant canopy landmark can clearly and accurately represent the specific position of every target plant, consequently, the target navigation circuit of unmanned aerial vehicle is extracted based on this plant canopy landmark, can satisfy navigation demand to accurate navigation route among the slow-speed high accuracy navigation processes such as weeding, fixed point fertilization.
In the above embodiment, an unmanned aerial vehicle navigation method based on a plant canopy landmark, an unmanned aerial vehicle navigation device based on a plant canopy landmark, and an electronic device are provided. The embodiments of the computer-readable storage medium provided in the present application are described relatively simply, and for relevant portions, reference may be made to the corresponding descriptions of the above method embodiments, and the embodiments described below are merely illustrative.
The present embodiment provides a computer readable storage medium having stored thereon computer instructions, which when executed by a processor, implement the steps of:
acquiring a target image corresponding to a target vegetation area shot by an unmanned aerial vehicle;
obtaining plant canopy landmarks corresponding to target plants in the target vegetation area based on the target image;
and extracting a target navigation route of the unmanned aerial vehicle based on the plant canopy landmark.
In some embodiments, the obtaining, based on the target image, a vegetation canopy landmark corresponding to a target plant in the target vegetation area includes:
and carrying out image segmentation on the target image to obtain a plant canopy landmark corresponding to the target plant.
In some embodiments, the image segmentation of the target image to obtain the plant canopy landmark corresponding to the target plant includes:
and identifying a plant canopy landmark corresponding to the target plant from the target vegetation area based on canopy gray gradient distribution of the target plant.
In some embodiments, the identifying a vegetation canopy landmark corresponding to the target plant from the target vegetation area based on the canopy gray gradient distribution of the target plant includes:
classifying objects contained in the target image based on a gray gradient distribution rule corresponding to the canopy of the target plant in the radial direction to obtain a classification result;
in response to the classification result indicating that the object is the target plant, marking the object as a plant canopy landmark corresponding to the target plant.
In some embodiments, the classifying the object included in the target image to obtain a classification result includes: classifying the object contained in the target image through a pre-trained inclusion-V3 classifier, and obtaining a classification result which is output by the inclusion-V3 classifier and used for representing whether the object is the target plant. In some embodiments, said extracting a target navigation route for said drone based on said plant canopy landmark comprises:
and extracting the piecewise linearized local navigation line from the plant canopy landmark according to the canopy gray gradient distribution rule of the plant canopy landmark.
By executing computer instructions stored on a computer-readable storage medium provided by this embodiment, after a target image corresponding to a target vegetation area captured by an unmanned aerial vehicle is acquired, a vegetation canopy landmark corresponding to a target plant in the target vegetation area is acquired based on the target image; and then extracting the target navigation route of the unmanned aerial vehicle based on the plant canopy landmarks. Because the canopy of different grade type plants has its specific grey scale gradient distribution rule, consequently, can carry out image segmentation based on the grey scale gradient distribution rule that this plant canopy corresponds, discern the plant canopy landmark that target plant corresponds, and extract unmanned aerial vehicle's target navigation circuit based on this plant canopy landmark, because above-mentioned plant canopy landmark can clearly and accurately represent the specific position of every target plant, consequently, the target navigation circuit of unmanned aerial vehicle is extracted based on this plant canopy landmark, can satisfy navigation demand to accurate navigation route among the slow-speed high accuracy navigation processes such as weeding, fixed point fertilization.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
1. Computer-readable media, including both permanent and non-permanent, removable and non-removable media, may implement the information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include non-transitory computer readable media (transient media), such as modulated data signals and carrier waves.
2. As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
Although the present invention has been described with reference to a preferred embodiment, it should be understood that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (9)
1. An unmanned aerial vehicle navigation method based on plant canopy landmarks is characterized by comprising the following steps:
acquiring a target image corresponding to a target vegetation area shot by an unmanned aerial vehicle;
obtaining plant canopy landmarks corresponding to target plants in the target vegetation area based on the target image;
and extracting a target navigation route of the unmanned aerial vehicle based on the plant canopy landmark.
2. The method of claim 1, wherein obtaining vegetation canopy landmarks corresponding to target vegetation in the target vegetation area based on the target image comprises:
and carrying out image segmentation on the target image to obtain a plant canopy landmark corresponding to the target plant.
3. The method of claim 2, wherein the image segmenting the target image to obtain the plant canopy landmark corresponding to the target plant comprises:
and identifying a plant canopy landmark corresponding to the target plant from the target vegetation area based on the canopy gray gradient distribution of the target plant.
4. The method of claim 3, wherein identifying the plant canopy landmark corresponding to the target plant from the target vegetation area based on the canopy gray scale gradient distribution of the target plant comprises:
classifying objects contained in the target image based on a gray gradient distribution rule corresponding to the canopy of the target plant in the radial direction to obtain a classification result;
in response to the classification result indicating that the object is the target plant, marking the object as a plant canopy landmark corresponding to the target plant.
5. The method according to claim 4, wherein the classifying the object included in the target image to obtain a classification result comprises: classifying the object contained in the target image through a pre-trained inclusion-V3 classifier, and obtaining a classification result which is output by the inclusion-V3 classifier and used for representing whether the object is the target plant or not.
6. The method of claim 1, wherein said extracting a target navigation route for said drone based on said plant canopy landmark comprises:
and extracting the piecewise linearized local navigation line from the plant canopy landmark according to the canopy gray gradient distribution rule of the plant canopy landmark.
7. The utility model provides an unmanned aerial vehicle navigation head based on plant canopy ground mark, its characterized in that includes:
the target image acquisition unit is used for acquiring a target image corresponding to a target vegetation area shot by the unmanned aerial vehicle;
a vegetation canopy landmark obtaining unit, configured to obtain a vegetation canopy landmark corresponding to a target plant in the target vegetation area based on the target image;
and the target navigation route extracting unit is used for extracting the target navigation route of the unmanned aerial vehicle based on the plant canopy landmark.
8. An electronic device comprising a processor and a memory; wherein, the first and the second end of the pipe are connected with each other,
the memory is to store one or more computer instructions, wherein the one or more computer instructions are to be executed by the processor to implement the method of any one of claims 1-6.
9. A computer-readable storage medium having stored thereon one or more computer instructions for execution by a processor to perform the method of any one of claims 1-6.
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