CN117789063A - Unmanned aerial vehicle-mounted AI image processing equipment and method - Google Patents
Unmanned aerial vehicle-mounted AI image processing equipment and method Download PDFInfo
- Publication number
- CN117789063A CN117789063A CN202311761928.7A CN202311761928A CN117789063A CN 117789063 A CN117789063 A CN 117789063A CN 202311761928 A CN202311761928 A CN 202311761928A CN 117789063 A CN117789063 A CN 117789063A
- Authority
- CN
- China
- Prior art keywords
- image
- aerial vehicle
- unmanned aerial
- network model
- data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000012545 processing Methods 0.000 title claims abstract description 49
- 238000000034 method Methods 0.000 title claims abstract description 17
- 238000013527 convolutional neural network Methods 0.000 claims abstract description 20
- 238000004891 communication Methods 0.000 claims abstract description 16
- 238000004458 analytical method Methods 0.000 claims abstract description 15
- 238000000605 extraction Methods 0.000 claims abstract description 7
- 230000000007 visual effect Effects 0.000 claims description 23
- 238000013528 artificial neural network Methods 0.000 claims description 12
- 238000007781 pre-processing Methods 0.000 claims description 12
- 238000001931 thermography Methods 0.000 claims description 6
- 239000000284 extract Substances 0.000 claims description 4
- 238000007689 inspection Methods 0.000 claims description 4
- 238000013507 mapping Methods 0.000 claims description 4
- 238000003672 processing method Methods 0.000 claims description 3
- 238000012876 topography Methods 0.000 claims description 3
- 241000607479 Yersinia pestis Species 0.000 claims description 2
- 238000001514 detection method Methods 0.000 description 5
- 201000010099 disease Diseases 0.000 description 5
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 5
- 241000238631 Hexapoda Species 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
- 230000002093 peripheral effect Effects 0.000 description 2
- 208000035473 Communicable disease Diseases 0.000 description 1
- 241001465754 Metazoa Species 0.000 description 1
- 230000008485 antagonism Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 208000015181 infectious disease Diseases 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000003062 neural network model Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
Abstract
The invention discloses an unmanned aerial vehicle-mounted AI image processing device and method, wherein the device comprises a data acquisition module, a controller, a wireless communication module and a first server, wherein the data acquisition module, the controller and the wireless communication module are fixed on an unmanned aerial vehicle, the data acquisition module is in signal connection with the first server positioned at the background, the first server is in signal connection with the controller and the wireless communication module in sequence, the data acquisition module is configured to acquire image data of a specific area and other basic data, the first server is configured to construct an countermeasure network model and a convolutional neural network model, the acquired image is subjected to AI image generation through the countermeasure network model, the characteristic extraction and analysis are performed on the generated AI image through the convolutional neural network model, and the controller generates a control instruction based on an analysis result.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle remote sensing, in particular to unmanned aerial vehicle-mounted AI image processing equipment and method.
Background
The unmanned plane is a unmanned plane operated by using radio remote control equipment and a self-provided program control device, or is operated by a vehicle-mounted computer completely or intermittently and autonomously, so that the unmanned plane is widely applied to the fields of aerial photography, agriculture, plant protection, miniature self-timer shooting, express delivery transportation, disaster relief, wild animal observation, infectious disease monitoring, mapping, news reporting, electric power inspection, disaster relief, film and television shooting, romantic manufacturing and the like, and the unmanned plane is adopted for image acquisition and provides convenience for the operation of people;
at present, the image acquisition of the unmanned aerial vehicle is mainly operated by adopting a camera carried on the unmanned aerial vehicle, and the camera simultaneously conveys the acquired picture to a ground base station, so that the ground base station personnel can view the shot picture of the unmanned aerial vehicle at a high place.
The invention patent of patent application number CN201711177677.2 discloses an unmanned aerial vehicle-mounted image processing device, which comprises an unmanned aerial vehicle, wherein a camera, an image sensor, a controller and a wireless data transmission radio station are mounted on the unmanned aerial vehicle; the invention adopts the camera to collect the image and amplifies the value of the collected picture signal, thereby achieving the effect of improving the processing efficiency and the recognition efficiency of the image processing frame and being capable of transmitting high-precision images to the ground control station.
However, in specific application, the flying height of the unmanned aerial vehicle is related to the size of the acquired picture, if the unmanned aerial vehicle flies higher, the transmitted picture is small, the reflected flying is low, the transmitted picture is large, the controlled flying height is difficult to directly influence the ground transmitted image, and secondly, the acquired picture is only acquired by adopting a camera, so that the acquired picture is difficult to analyze and process, and the ground base station staff is difficult to make decisions according to the acquired picture.
Disclosure of Invention
Aiming at the prior art, the invention aims to provide unmanned aerial vehicle-mounted AI image processing equipment and method, which mainly solve the technical problems in the background art.
In order to achieve the above object, the technical solution of the embodiment of the present invention is as follows: the invention discloses unmanned aerial vehicle-mounted AI image processing equipment, which comprises a data acquisition module, a controller, a wireless communication module and a first server, wherein the data acquisition module, the controller and the wireless communication module are fixed on an unmanned aerial vehicle, the data acquisition module is in signal connection with the first server positioned at the background, the first server is in signal connection with the controller and the wireless communication module in sequence, the data acquisition module is configured to acquire image data of a specific area and other basic data, the first server is configured to construct an countermeasure network model and a convolutional neural network model, the acquired image is subjected to AI image generation through the countermeasure network model, the generated AI image is subjected to feature extraction and analysis through the convolutional neural network model, and the controller generates a control instruction based on an analysis result.
Optionally, the data acquisition module comprises a picture acquisition sub-module and a climate sensing sub-module, wherein the picture acquisition sub-module comprises a camera, a thermal imaging sensor and a multispectral sensor, and the climate sensing sub-module comprises a wind speed sensor and an ultrasonic sensor.
Optionally, an image processing unit is further provided in the first server, and the image processing module is configured to perform preprocessing on the acquired image, where the preprocessing process includes scaling the acquired image to a predetermined ratio and performing image quality enhancement processing.
Optionally, the image processing module divides the received image into a plurality of areas with the sizes corresponding to the scaled image, extracts visual features in the divided areas respectively, and overlaps the extracted visual features in the areas to realize the image quality enhancement processing.
Optionally, the extracted visual features include one or more of brightness, color, darkness, saturation, and edge shape of the image.
Optionally, the countermeasure network model is composed of a generator neural network and a discriminator neural network, the generator neural network is used for generating the AI image according to the preprocessed image, and the discriminator neural network is used for comparing the AI image with the preprocessed image.
Optionally, when the characteristic extraction is performed on the generated AI image by the convolutional neural network model, the extracted characteristic includes one or more of a geological data characteristic, an agricultural layout data characteristic, a disease and insect hazard data characteristic, an electric power inspection data characteristic, a topography mapping data characteristic and a natural disaster data characteristic.
The second aspect of the invention discloses an unmanned aerial vehicle-mounted AI image processing method, which is applied to the unmanned aerial vehicle-mounted AI image processing equipment according to any one of the previous claims, and comprises the following steps:
acquiring field image data and preprocessing the field image data;
constructing an countermeasure network model, wherein the countermeasure network model generates an AI image based on the preprocessed field image;
and constructing a convolutional neural network model, analyzing the AI image, and transmitting an analysis result to a ground base station by the controller through the wireless communication module.
Optionally, preprocessing the field image data specifically includes: scaling the field image to a predetermined ratio, dividing the field image into a plurality of areas with the sizes corresponding to the scaled image, extracting visual features in the divided areas respectively, overlapping the extracted visual features reflected in the areas, and performing image quality enhancement processing after multiple scaling and overlapping of the visual features, thereby realizing image preprocessing.
The invention has the beneficial effects that: scaling the image acquired by the data acquisition device to a predetermined ratio to adjust the size of the image transmitted to the ground base station according to the distance between the flying height of the unmanned aerial vehicle and the ground, dividing the received image into a plurality of areas corresponding to the scaled image size, reflecting the extracted visual features in the divided areas respectively, overlapping the extracted visual features in the areas, and repeatedly performing scaling, extraction and image quality enhancement processing for a predetermined number of times on the image subjected to the image quality enhancement processing to enhance the quality of the image; the network model module is used for generating AI images of pictures acquired by the unmanned aerial vehicle, and training and analyzing the AI images by combining the convolutional neural network model to assist ground base station personnel to make decisions according to picture conditions detected by the unmanned aerial vehicle.
Drawings
FIG. 1 is a block diagram of the overall architecture of the present invention;
FIG. 2 is a block diagram of the overall detailed construction of the present invention;
FIG. 3 is a system diagram of a data acquisition device of the present invention;
FIG. 4 is a flow chart of the disclosed method;
fig. 5 is a flowchart of the present invention when processing an image.
1 data acquisition module, 11 picture acquisition submodule, 12 climate sensing submodule, 11a camera, 11b thermal imaging sensor, 11c multispectral sensor, 12a wind speed sensor, 12b ultrasonic sensor, 2 controller, 3 wireless communication module, 4 first server, 401 countermeasure network model, 402 convolution neural network model, 403 image processing module.
Detailed Description
The technical scheme of the invention is further elaborated below by referring to the drawings in the specification and the specific embodiments. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. In the following description, reference is made to the expression "some embodiments" which describe a subset of all possible embodiments, but it should be understood that "some embodiments" may be the same subset or a different subset of all possible embodiments and may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a more thorough understanding of the present invention. It will be apparent, however, to one skilled in the art that the invention may be practiced without one or more of these details. In other instances, well-known features have not been described in detail in order to avoid obscuring the invention.
It should be understood that the present invention may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. And the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term "and/or" includes any and all combinations of the associated listed items.
It will be further understood that when an element is referred to as being "fixed to" another element, it can be directly on the other element or intervening elements may also be present. When an element is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements may also be present. The terms "vertical," "horizontal," "inner," "outer," "left," "right," and the like are used herein for illustrative purposes only and are not meant to be the only embodiment.
In order to provide a thorough understanding of the present invention, detailed structures will be presented in the following description in order to illustrate the technical solutions presented by the present invention. Alternative embodiments of the invention are described in detail below, however, the invention may have other implementations in addition to these detailed descriptions.
Referring to fig. 1 to 3 and 5 in combination, a first aspect of the present invention discloses an unmanned aerial vehicle-mounted AI image processing apparatus, which includes a data acquisition module 1, a controller 2, a wireless communication module 2, and a first server 4, wherein the data acquisition module 1, the controller 2, and the wireless communication module 2 are fixed on an unmanned aerial vehicle, the data acquisition module 1 is connected to the first server 4 located in the background, the first server 4 is connected to the controller 2 and the wireless communication module 2 in sequence, the data acquisition module 1 is configured to acquire image data of a specific area and other basic data, the first server 4 is configured to construct an countermeasure network model 401 and a convolutional neural network model 402, AI images are generated by the countermeasure network model 401, characteristics of the AI images are extracted and analyzed by the convolutional neural network model 402, and the controller 2 generates a control command based on the analysis result.
In the present application, the data acquisition module 1 can acquire images, generate AI images by using the countermeasure network model 401 to process the images, extract and analyze image features of the AI images by using the convolutional neural network model 402, and finally transmit the acquired images and the analyzed data to the ground base station, and at this time, the ground base station staff views the processed images from the unmanned aerial vehicle and performs planning processing in combination with the analyzed data.
Because there is certain influence between the height that unmanned aerial vehicle flies and the picture that ground was shot, when the distance of height and ground that like unmanned aerial vehicle flies is great, the picture of gathering is then less, and is the same, and like the distance of height and ground that unmanned aerial vehicle flies is less, the picture of gathering is great, in order to improve the quality and the size of transmitting to ground basic station image are even, this application is equipped with image processing unit in first server 4, image processing module 403 is used for carrying out the preliminary treatment with the image of gathering, the preliminary treatment process includes zooming the image of gathering to predetermined ratio to carry out image quality enhancement processing.
Specifically, the image processing module 403 scales the image acquired by the data acquisition device to a predetermined ratio, denoted as n=1, extracts visual features from the scaled image, and performs image quality enhancement processing reflecting the extracted visual features in the received image through a series of operations; when image quality enhancement processing reflecting the extracted visual features in the received image is performed through a series of operations, the received image is divided into a plurality of areas having a size corresponding to the scaled image, the extracted visual features are respectively reflected in the divided areas, and the extracted visual features reflected in the plurality of areas are overlapped, scaling and extraction are repeatedly performed a predetermined number of times on the image subjected to the image quality enhancement processing, and finally the image quality enhancement processing is realized.
Further, the extracted visual features include one or more of brightness, color, darkness, saturation, and edge shape of the image, and wherein the predetermined scale is a range value between 1 and 1.5.
Specifically, in some embodiments of the present application, the data acquisition module 1 includes a picture acquisition sub-module 11 and a climate sensor sub-module 12, the picture acquisition sub-module 11 includes a camera 11a, a thermal imaging sensor 11b, a multispectral sensor 11c, and the climate sensor sub-module 12 includes a wind speed sensor 12a and an ultrasonic sensor 12b.
The image data is acquired in multiple directions through the high-definition camera 11a, the thermal imaging sensor 11b and the multispectral sensor 11c, the acquisition range of the image is improved, the wind speed of the unmanned aerial vehicle is detected in real time through the wind speed sensor 12a, the peripheral rainfall condition is detected in real time through the ultrasonic sensor 12b, once the wind speed is high and the rainfall condition is found, the unmanned aerial vehicle is stopped, and the influence on the unmanned aerial vehicle in the severe condition is reduced.
Further, the specific position of the picture collection is displayed in the GIS satellite map, so that ground base station personnel can conveniently know the position of the picture collection and make decisions in time.
Specifically, the countermeasure network model 401 is composed of a generator neural network and a discriminator neural network, the generator neural network is used for generating an AI image according to the preprocessed image, the discriminator neural network is used for comparing the AI image with the preprocessed image, and if the comparison difference is too large, the generator neural network regenerates a new AI image until the comparison result meets the requirement.
Specifically, when the characteristic extraction is performed on the generated AI image by the convolutional neural network model 402, the extracted characteristic includes one or more of a geological data characteristic, an agricultural layout data characteristic, a disease and pest hazard data characteristic, an electric power inspection data characteristic, a topography mapping data characteristic, and a natural disaster data characteristic.
The convolutional neural network model 402 can analyze the acquired images to assist ground base station personnel to make decisions for the conditions of the images detected by the unmanned aerial vehicle, specifically, when the unmanned aerial vehicle detects in different fields, such as terrain detection, farmland detection, electric power detection and natural disaster detection, the convolutional neural network model 402 is adopted to perform intelligent analysis on the acquired images, for example, when the farmland detection is performed, the images shot by the unmanned aerial vehicle show that a large number of farmlands are damaged by diseases and insects, and then the ground base station personnel are warned to perform disease and insect treatment on the farmlands after analysis.
Referring to fig. 4, a second aspect of the present invention discloses a method for processing an AI image on board a unmanned aerial vehicle, the method being applied to an AI image processing apparatus on board a unmanned aerial vehicle according to any one of the preceding claims, the method comprising the steps of:
s1, acquiring field image data, and preprocessing the field image data;
s2, constructing an countermeasure network model 401, wherein the countermeasure network model 401 generates an AI image based on the preprocessed live image;
and S3, constructing a convolutional neural network model 402, analyzing the AI image, and transmitting an analysis result to a ground base station by the controller 2 through the wireless communication module 2.
Further, preprocessing the field image data specifically includes: scaling the field image to a predetermined ratio, dividing the field image into a plurality of areas with the sizes corresponding to the scaled image, extracting visual features in the divided areas respectively, overlapping the extracted visual features reflected in the areas, and performing image quality enhancement processing after multiple scaling and overlapping of the visual features, thereby realizing image preprocessing.
When aerial image acquisition is carried out through the unmanned aerial vehicle, the data acquisition module 1 is adopted to acquire images, wherein the image acquisition mainly adopts a high-definition camera 11a, a thermal imaging sensor 11b and a multispectral sensor 11c in the image acquisition module to acquire multidirectional images, and a wind speed sensor 12a and an ultrasonic sensor 12b in the climate sensor sub-module 12 can detect the wind speed and rainfall conditions around the unmanned aerial vehicle in real time when the unmanned aerial vehicle flies, so that ground base station personnel can know the current flight environment of the unmanned aerial vehicle conveniently in real time;
the acquired image is subjected to picture adjustment and picture enhancement processing by an image processing module 403, and on the basis, the acquired image is subjected to AI image generation by an antagonism network module, at the moment, the acquired picture is intelligently analyzed by a convolution neural network module, and the result of image display and the analysis result are transmitted to a ground base station, so that a ground base station worker can make a decision on the acquired picture in time;
in a specific application environment, for example, when an unmanned aerial vehicle surveys a farmland, a data acquisition module 1 acquires pictures of the farmland, the acquired pictures are scaled and adjusted according to the flying height of the unmanned aerial vehicle and in combination with the size and the quality of the shot pictures, and the picture quality is enhanced, meanwhile, the acquired pictures are displayed in a GIS satellite map in real time, an anti-network model 401 generates AI images of the processed pictures, and a convolutional neural network model 402 is combined to extract features of the images, for example, the situation of diseases and insects in shot farmland paddy is detected, and the trained convolutional neural network model 402 performs early warning and analysis on the transmitted pictures to assist ground base station staff to make decisions in time;
moreover, the wind speed sensor 12a and the ultrasonic sensor 12b can detect the wind speed and the peripheral rainfall condition of the unmanned aerial vehicle in the flying process in real time, and if the condition occurs, ground base station staff can timely make the flying specification of stopping the unmanned aerial vehicle, so that the unmanned aerial vehicle is prevented from being influenced by extreme weather.
In a word, the invention can carry out scaling adjustment and image quality enhancement processing on the acquired pictures, simultaneously achieve the generation of AI images, and carry out intelligent analysis on the transmitted image conditions so as to assist staff to make good decisions on the transmitted pictures in time, combine early warning of wind speed and rainfall conditions and reduce the influence of extreme weather flight on the unmanned aerial vehicle.
The foregoing is merely illustrative embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily think about variations or substitutions within the technical scope of the present invention, and the invention should be covered. The scope of the invention is to be determined by the appended claims.
Claims (9)
1. The unmanned aerial vehicle-mounted AI image processing device is characterized by comprising a data acquisition module, a controller, a wireless communication module and a first server, wherein the data acquisition module, the controller and the wireless communication module are fixed on an unmanned aerial vehicle, the data acquisition module is in signal connection with the first server positioned at the background, the first server is in signal connection with the controller and the wireless communication module in sequence, the data acquisition module is configured to acquire image data of a specific area and other basic data, the first server is configured to construct an countermeasure network model and a convolutional neural network model, the acquired image is subjected to AI image generation through the countermeasure network model, the generated AI image is subjected to feature extraction and analysis through the convolutional neural network model, and the controller generates a control instruction based on an analysis result.
2. The unmanned aerial vehicle-mounted AI image processing device of claim 1, wherein the data acquisition module comprises a picture acquisition sub-module and a climate sensing sub-module, the picture acquisition sub-module comprising a camera, a thermal imaging sensor, a multispectral sensor, the climate sensing sub-module comprising a wind speed sensor and an ultrasonic sensor.
3. The unmanned aerial vehicle-mounted AI image processing apparatus of claim 1, wherein the first server further comprises an image processing unit, wherein the image processing module is configured to pre-process the acquired image, wherein the pre-processing process includes scaling the acquired image to a predetermined ratio and performing an image quality enhancement process.
4. The unmanned aerial vehicle-mounted AI image processing apparatus according to claim 3, wherein the image processing module divides the received image into a plurality of areas having a size corresponding to the scaled image, extracts visual features respectively in the divided areas, and overlaps the extracted visual features in the plurality of areas to realize the image quality enhancement processing.
5. The unmanned aerial vehicle on-board AI image processing device of claim 4, wherein the visual features extracted include one or more of brightness, color, darkness, saturation, and edge shape of an image.
6. The unmanned aerial vehicle-mounted AI image processing apparatus of claim 5, wherein the countermeasure network model is comprised of a generator neural network for generating AI images from the preprocessed images and a discriminator neural network for comparing the AI images with the preprocessed images.
7. The unmanned aerial vehicle-mounted AI image processing device of claim 6, wherein when the generated AI image is feature extracted by the convolutional neural network model, the extracted features include one or more of geologic data features, agricultural layout data features, pest hazard data features, power inspection data features, topography mapping data features, natural disaster data features.
8. A unmanned aerial vehicle-mounted AI image processing method, which is applied to the unmanned aerial vehicle-mounted AI image processing apparatus according to any one of claims 1 to 7, and which comprises the steps of:
acquiring field image data and preprocessing the field image data;
constructing an countermeasure network model, wherein the countermeasure network model generates an AI image based on the preprocessed field image;
and constructing a convolutional neural network model, analyzing the AI image, and transmitting an analysis result to a ground base station by the controller through the wireless communication module.
9. The unmanned aerial vehicle-mounted AI image processing method of claim 8, wherein preprocessing the live image data specifically comprises: scaling the field image to a predetermined ratio, dividing the field image into a plurality of areas with the sizes corresponding to the scaled image, extracting visual features in the divided areas respectively, overlapping the extracted visual features reflected in the areas, and performing image quality enhancement processing after multiple scaling and overlapping of the visual features, thereby realizing image preprocessing.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311761928.7A CN117789063A (en) | 2023-12-20 | 2023-12-20 | Unmanned aerial vehicle-mounted AI image processing equipment and method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311761928.7A CN117789063A (en) | 2023-12-20 | 2023-12-20 | Unmanned aerial vehicle-mounted AI image processing equipment and method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117789063A true CN117789063A (en) | 2024-03-29 |
Family
ID=90401143
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311761928.7A Pending CN117789063A (en) | 2023-12-20 | 2023-12-20 | Unmanned aerial vehicle-mounted AI image processing equipment and method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117789063A (en) |
-
2023
- 2023-12-20 CN CN202311761928.7A patent/CN117789063A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Ahirwar et al. | Application of drone in agriculture | |
| KR20180081104A (en) | A method for acquiring and analyzing aerial images | |
| Bargoti et al. | A pipeline for trunk detection in trellis structured apple orchards | |
| CN113705108B (en) | Real-time landslide disaster monitoring and early warning method and system | |
| CN111225854A (en) | Unmanned plane | |
| DE102013019488A1 (en) | PHOTO WITH PROTECTION OF THE PRIVACY | |
| CN106197380A (en) | Aquatic vegetation monitoring method based on unmanned plane and system | |
| CN110718137A (en) | Method and device for constructing density distribution map of target object, terminal and mobile device | |
| CN112101088A (en) | Automatic unmanned aerial vehicle power inspection method, device and system | |
| CN106940734A (en) | A kind of Migrating Insects monitor recognition methods and device in the air | |
| CN105930804A (en) | Power transmission equipment identification platform arranged on unmanned aerial vehicle | |
| Vanegas et al. | Multi and hyperspectral UAV remote sensing: Grapevine phylloxera detection in vineyards | |
| CN112163483A (en) | Target quantity detection system | |
| CN111352404A (en) | Method, system, medium and device for acquiring agricultural condition by cooperation of unmanned aerial vehicle and ground mobile robot | |
| CN110413002A (en) | A kind of unmanned aerial vehicle remote sensing information collecting method and device | |
| AU2021236591A1 (en) | Method and system for utility power lines vegetation proximity monitoring and controlling | |
| CN113454558A (en) | Obstacle detection method and device, unmanned aerial vehicle and storage medium | |
| CN108873931A (en) | A kind of unmanned plane vision avoiding collision combined based on subjectiveness and objectiveness | |
| Hung et al. | Using robotic aircraft and intelligent surveillance systems for orange hawkweed detection | |
| CN117789063A (en) | Unmanned aerial vehicle-mounted AI image processing equipment and method | |
| CN111985472A (en) | Trough hay temperature image processing method based on artificial intelligence and active ball machine | |
| Dorfling et al. | Satellite, Aerial, and Ground Sensor Fusion Experiment for Management of Elephants and Rhinos and Poaching Prevention | |
| CN113099169A (en) | Unmanned aerial vehicle, low-altitude remote sensing enabling platform and construction and data transmission method thereof | |
| CN218258725U (en) | Unmanned aerial vehicle and system for poppy inspection | |
| CN111894046B (en) | Cutting construction monitoring system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |