CN110414375B - Low-altitude target identification method and device, storage medium and electronic equipment - Google Patents
Low-altitude target identification method and device, storage medium and electronic equipment Download PDFInfo
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- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/40—Scenes; Scene-specific elements in video content
- G06V20/41—Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items
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- G06V2201/07—Target detection
Abstract
The invention provides a low-altitude target identification method, a low-altitude target identification device, a storage medium and electronic equipment, wherein the method comprises the following steps: detecting a suspicious target in a preset area; when a suspicious target is detected, detecting and tracking the suspicious target to acquire video information of the suspicious target in a detection and tracking process, wherein the video information comprises visible light video information and infrared video information; extracting behavior characteristics of the suspicious target according to the video information; and identifying and classifying the characteristic parameters corresponding to the behavior characteristics by adopting a preset deep learning classification model, and identifying the suspicious target according to an identification and classification result. The invention provides an efficient and reliable low-altitude target identification method, which can effectively ensure the identification efficiency and accuracy of a low-altitude target.
Description
Technical Field
The present invention relates to the field of low-altitude target identification technologies, and in particular, to a low-altitude target identification method and apparatus, a storage medium, and an electronic device.
Background
In recent years, the development of small and miniature unmanned aerial vehicles for civil use is rapid, the number of the unmanned aerial vehicles is increased geometrically, and the management of the unmanned aerial vehicles is delayed seriously. The phenomena of black flight and messy flight of the unmanned aerial vehicle are increasingly serious, and the unmanned aerial vehicle poses new threats to the flight safety of civil aviation and military aviation.
At present, the detection, identification and monitoring means for the small micro unmanned aerial vehicle target are single, only suspicious detection of low-altitude slow small targets can be achieved, and whether the suspicious target is an unmanned aerial vehicle or other low-altitude targets is difficult to quickly and accurately identify.
Disclosure of Invention
The invention provides a low-altitude target identification method and device, a storage medium and electronic equipment, and solves the problem that whether a suspicious target is an unmanned aerial vehicle or other low-altitude targets is difficult to quickly and accurately identify by existing unmanned aerial vehicle detection.
In one aspect of the present invention, a method for identifying a low-altitude target is provided, the method including:
detecting a suspicious target in a preset area;
when a suspicious target is detected, detecting and tracking the suspicious target to acquire video information of the suspicious target in a detection and tracking process, wherein the video information comprises visible light video information and infrared video information;
extracting behavior characteristics of the suspicious target according to the video information;
and identifying and classifying the characteristic parameters corresponding to the behavior characteristics by adopting a preset deep learning classification model, and identifying whether the suspicious target is the unmanned aerial vehicle or not according to an identification and classification result.
Optionally, the extracting the behavior feature of the suspicious object according to the video information includes:
analyzing the importance of each image frame in the video information on the behavior recognition of the current video information based on a preset neural network model, and matching the weight of each image frame in the preset behavior recognition model according to the importance of each image frame on the behavior recognition of the current video information;
and extracting the behavior characteristics of the suspicious target based on the behavior recognition model after weight adjustment.
Optionally, after the identifying whether the suspicious target is an unmanned aerial vehicle according to the identification and classification result, the method further includes:
if the identification result of the suspicious target is a ground vehicle, predicting the running track of the suspicious target according to the video information;
acquiring city construction information of the preset area, and analyzing path configuration of each road in the preset area according to the city construction information;
and judging whether roads with path configuration consistent with the running track of the suspicious target exist in all the roads in the preset area, and if so, confirming that the suspicious target is a ground vehicle.
Optionally, the method further comprises:
and according to different recognition results of the suspicious target, adopting different colors to identify the suspicious target.
In another aspect of the present invention, there is provided an apparatus for identifying a low altitude target, the apparatus comprising:
the detection module is used for detecting suspicious targets in a preset area;
the system comprises an acquisition module, a tracking module and a processing module, wherein the acquisition module is used for detecting and tracking a suspicious target when the suspicious target is detected so as to acquire video information of the suspicious target in a detection and tracking process, and the video information comprises visible light video information and infrared video information;
the extraction module is used for extracting the behavior characteristics of the suspicious target according to the video information;
and the recognition module is used for recognizing and classifying the characteristic parameters corresponding to the behavior characteristics by adopting a preset deep learning classification model, and recognizing whether the suspicious target is an unmanned aerial vehicle or not according to a recognition and classification result.
Optionally, the extraction module includes:
the configuration unit is used for analyzing the importance of each image frame in the video information on the behavior recognition of the current video information based on a preset neural network model, and matching the weight of each image frame in the preset behavior recognition model according to the importance of each image frame on the behavior recognition of the current video information;
and the extracting unit is used for extracting the behavior characteristics of the suspicious target based on the behavior recognition model after the weight adjustment.
Optionally, the apparatus further comprises:
the prediction module is used for predicting the running track of the suspicious target according to the video information after the suspicious target is identified as the unmanned aerial vehicle according to the identification and classification result and if the suspicious target is identified as the ground vehicle;
the analysis module is used for acquiring city construction information of the preset area and analyzing the path configuration of each road in the preset area according to the city construction information;
and the checking module is used for judging whether a road with the path configuration consistent with the running track of the suspicious target exists in each road of the preset area, and if so, confirming that the suspicious target is a ground vehicle.
Optionally, the apparatus further comprises:
and the identification module is used for identifying the suspicious target by adopting different colors according to different identification results of the suspicious target.
Furthermore, the invention also provides a computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method as described above.
Furthermore, the present invention also provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method as described above when executing the program.
According to the low-altitude target identification method, the low-altitude target identification device, the storage medium and the electronic equipment, after the suspicious target is detected, the behavior characteristics of the suspicious target are extracted according to the video information obtained in the process of detecting and tracking the suspicious target, the characteristic parameters of the behavior characteristics of the suspicious target are identified and classified by adopting the deep learning algorithm model, whether the suspicious target is identified by the unmanned aerial vehicle or not is achieved according to the identification and classification result, and the identification efficiency and accuracy of the low-altitude target can be effectively guaranteed.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
fig. 1 is a schematic flow chart of a method for identifying a low-altitude target according to an embodiment of the present invention;
fig. 2 is a schematic flow chart illustrating a method for identifying a low-altitude target according to another embodiment of the present invention;
fig. 3 is a schematic structural diagram of a low-altitude target recognition apparatus according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a low-altitude target recognition apparatus according to another embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be 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 disclosure to those skilled in the art.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
Fig. 1 schematically shows a flow chart of a low-altitude target identification method according to an embodiment of the present invention. Referring to fig. 1, the method for identifying a low-altitude target provided by the embodiment of the present invention specifically includes steps S11 to S14, as follows:
and S11, detecting suspicious objects in the preset area.
S12, when a suspicious target is detected, detecting and tracking the suspicious target to acquire video information of the suspicious target in the detecting and tracking process, wherein the video information comprises visible light video information and infrared video information.
In this embodiment, a hardware system to which the method for identifying a low-altitude target is applied includes a low-altitude monitoring radar and a photoelectric identification subsystem, wherein the low-altitude monitoring radar can accurately detect and track a suspicious target in a preset area; and then the photoelectric recognition subsystem performs imaging analysis on the tracked suspicious target so as to realize the identification of the low-altitude target based on the subsequent imaging of the photoelectric recognition subsystem.
And S13, extracting the behavior characteristics of the suspicious target according to the video information.
S14, recognizing and classifying the characteristic parameters corresponding to the behavior characteristics by adopting a preset deep learning classification model, and recognizing whether the suspicious target is an unmanned aerial vehicle or not according to the recognition and classification result.
In this embodiment, a data set is collected in advance, the data set is divided into a training set and a test set, behavior features of suspicious targets in the training data set are extracted, deep learning classification models are trained according to the training data set, and the trained deep learning classification models are tested by using the test set, so that certain recognition accuracy is met.
According to the low-altitude target identification method provided by the embodiment of the invention, after the suspicious target is detected, the behavior characteristic of the suspicious target is extracted according to the video information obtained in the process of detecting and tracking the suspicious target, the characteristic parameters of the behavior characteristic of the suspicious target are identified and classified by adopting the deep learning algorithm model, whether the suspicious target is an unmanned aerial vehicle or not is identified according to the identification and classification result, and the identification efficiency and accuracy of the low-altitude target can be effectively ensured.
In this embodiment of the present invention, the extracting behavior characteristics of the suspicious object according to the video information includes:
analyzing the importance of each image frame in the video information on the behavior recognition of the current video information based on a preset neural network model, and matching the weight of each image frame in the preset behavior recognition model according to the importance of each image frame on the behavior recognition of the current video information;
and extracting the behavior characteristics of the suspicious target based on the behavior recognition model after weight adjustment.
In the embodiment of the invention, before the behavior characteristics of the suspicious target are extracted according to the video information, the video information can be processed through a video image stabilization algorithm to compensate the motion of the photoelectric identification subsystem, the motion candidate target area is detected from the motion-compensated video image through a low-rank matrix recovery algorithm, and fine noise points in the motion candidate target area are removed, so that the behavior characteristic extraction accuracy of the suspicious target is improved, the behavior characteristic extraction accuracy is further improved, and the identification efficiency and accuracy of the low-altitude target are ensured.
Further, in this embodiment, after the suspicious target is identified as the unmanned aerial vehicle according to the identification and classification result, when the identification result is other objects, a corresponding verification method may be further adopted to further determine the suspicious target. In a specific embodiment, if the identification result of the suspicious target is a ground vehicle, predicting the running track of the suspicious target according to the video information; acquiring city construction information of the preset area, and analyzing path configuration of each road in the preset area according to the city construction information; and judging whether roads with path configuration consistent with the running track of the suspicious target exist in all the roads in the preset area, and if so, confirming that the suspicious target is a ground vehicle.
In another specific embodiment, if the identification result of the suspicious target is the unmanned aerial vehicle, the sound characteristic of the suspicious target can be obtained according to the video information; and according to whether the sound characteristic of the suspicious target is consistent with the sound characteristic of the corresponding unmanned aerial vehicle in a preset sound library or not, if so, determining that the suspicious target is the unmanned aerial vehicle.
In this embodiment, further identification verification is carried out through other characteristics to the unmanned aerial vehicle recognition result that obtains according to the recognition classification result of the degree of depth learning classification model that predetermines, effectively guarantees the recognition accuracy degree of low latitude target.
In an embodiment of the present invention, as shown in fig. 2, after identifying whether the suspicious target is an unmanned aerial vehicle according to the identification and classification result in step S14, the method further includes:
and S15, according to different recognition results of the suspicious target, adopting different colors to identify the suspicious target.
In the embodiment, the suspicious target can be identified by different colors on the terminal display according to different identification results of the suspicious target, so that an operator can dynamically adjust a human-computer interface through the control command center, and after the suspicious target is further confirmed, the interference destroying system is started to interfere normal flight of the unmanned aerial vehicle or directly destroy the unmanned aerial vehicle, so that the low-altitude suspicious flying target can be effectively detected and protected.
For simplicity of explanation, the method embodiments are described as a series of acts or combinations, but those skilled in the art will appreciate that the embodiments are not limited by the order of acts described, as some steps may occur in other orders or concurrently with other steps in accordance with the embodiments of the invention. Further, those skilled in the art will appreciate that the embodiments described in the specification are presently preferred and that no particular act is required to implement the invention.
Fig. 3 schematically shows a structural diagram of a low-altitude target recognition device according to an embodiment of the present invention. Referring to fig. 3, the identification apparatus for a low-altitude target according to the embodiment of the present invention specifically includes a detection module 201, an acquisition module 202, an extraction module 203, and an identification module 204, where:
the detection module 201 is configured to detect a suspicious target in a preset area;
the acquiring module 202 is configured to, when a suspicious target is detected, perform detection tracking on the suspicious target to acquire video information of the suspicious target in a detection tracking process, where the video information includes visible light video information and infrared video information;
the extraction module 203 is configured to extract behavior characteristics of the suspicious target according to the video information;
and the identification module 204 is configured to identify and classify the characteristic parameters corresponding to the behavior characteristics by using a preset deep learning classification model, and identify whether the suspicious target is an unmanned aerial vehicle according to an identification and classification result.
In an optional embodiment of the present invention, the extracting module 203 specifically includes a configuration unit and an extracting unit, where:
the configuration unit is used for analyzing the importance of each image frame in the video information on the behavior recognition of the current video information based on a preset neural network model, and matching the weight of each image frame in the preset behavior recognition model according to the importance of each image frame on the behavior recognition of the current video information;
and the extracting unit is used for extracting the behavior characteristics of the suspicious target based on the behavior recognition model after the weight adjustment.
In an alternative embodiment of the invention, the apparatus further comprises a prediction module, a parsing module and a verification module, not shown in the drawings, wherein:
the prediction module is used for predicting the running track of the suspicious target according to the video information after the suspicious target is identified as the unmanned aerial vehicle according to the identification and classification result and if the suspicious target is identified as the ground vehicle;
the analysis module is used for acquiring city construction information of the preset area and analyzing the path configuration of each road in the preset area according to the city construction information;
the checking module is used for judging whether a road with a path configuration consistent with the running track of the suspicious target exists in each road of the preset area, and if so, the suspicious target is determined to be a ground vehicle.
In an optional embodiment of the present invention, as shown in fig. 4, the apparatus further includes an identification module 205, configured to identify the suspicious target by using different colors according to different recognition results of the suspicious target.
For the device embodiment, since it is basically similar to the method embodiment, the description is simple, and for the relevant points, refer to the partial description of the method embodiment.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
According to the low-altitude target identification method and device provided by the embodiment of the invention, after the suspicious target is detected, the behavior characteristic of the suspicious target is extracted according to the video information obtained in the process of detecting and tracking the suspicious target, the characteristic parameters of the behavior characteristic of the suspicious target are identified and classified by adopting the deep learning algorithm model, whether the suspicious target is an unmanned aerial vehicle or not is identified according to the identification and classification result, and the identification efficiency and accuracy of the low-altitude target can be effectively ensured.
Furthermore, an embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps of the method as described above.
In this embodiment, the module/unit integrated with the low altitude target recognition device may be stored in a computer readable storage medium if it is implemented in the form of a software functional unit and sold or used as a stand-alone product. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, etc. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
The electronic device provided by the embodiment of the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the steps in the above embodiments of the low altitude target identification method are implemented, for example, S11 to S14 shown in fig. 1. Alternatively, the processor, when executing the computer program, implements the functions of the modules/units in the above-mentioned embodiments of the low-altitude target recognition apparatus, such as the detection module 201, the acquisition module 202, the extraction module 203, and the recognition module 204 shown in fig. 3.
Illustratively, the computer program may be partitioned into one or more modules/units that are stored in the memory and executed by the processor to implement the invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used for describing the execution process of the computer program in the low altitude target identification device. For example, the computer program may be partitioned into a detection module 201, an acquisition module 202, an extraction module 203, and a recognition module 204.
The electronic device may include, but is not limited to, a processor, a memory. Those skilled in the art will appreciate that the electronic device in this embodiment may include more or fewer components, or combine certain components, or different components, for example, the electronic device may also include an input-output device, a network access device, a bus, etc.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like that is the control center for the electronic device and that connects the various parts of the overall electronic device using various interfaces and wires.
The memory may be used to store the computer programs and/or modules, and the processor may implement various functions of the electronic device by running or executing the computer programs and/or modules stored in the memory and calling data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
Those skilled in the art will appreciate that while some embodiments herein include some features included in other embodiments, rather than others, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (6)
1. A method for identifying a low-altitude target, the method comprising:
detecting a suspicious target in a preset area;
when a suspicious target is detected, detecting and tracking the suspicious target to acquire video information of the suspicious target in a detection and tracking process, wherein the video information comprises visible light video information and infrared video information;
extracting behavior characteristics of the suspicious target according to the video information;
identifying and classifying the characteristic parameters corresponding to the behavior characteristics by adopting a preset deep learning classification model, identifying whether the suspicious target is an unmanned aerial vehicle or not according to an identification and classification result, and if the suspicious target is the unmanned aerial vehicle, acquiring the sound characteristics of the suspicious target according to the video information; according to whether the sound characteristics of the suspicious target are consistent with the sound characteristics of the corresponding unmanned aerial vehicle in a preset sound library or not, if so, determining that the suspicious target is the unmanned aerial vehicle;
before extracting the behavior characteristics of the suspicious target according to the video information, the method comprises the following steps: processing video information through a video image stabilization algorithm, compensating the motion of a photoelectric identification subsystem, detecting a motion candidate target area from a motion-compensated video image through a low-rank matrix recovery algorithm, and removing fine noise points in the motion candidate target area;
the extracting the behavior feature of the suspicious target according to the video information includes: analyzing the importance of each image frame in the video information on the behavior recognition of the current video information based on a preset neural network model, and matching the weight of each image frame in the preset behavior recognition model according to the importance of each image frame on the behavior recognition of the current video information; extracting behavior characteristics of the suspicious target based on the behavior recognition model after weight adjustment;
after identifying whether the suspicious target is an unmanned aerial vehicle according to the identification and classification result, the method further comprises: if the identification result of the suspicious target is a ground vehicle, predicting the running track of the suspicious target according to the video information; acquiring city construction information of the preset area, and analyzing path configuration of each road in the preset area according to the city construction information; and judging whether roads with path configuration consistent with the running track of the suspicious target exist in all the roads in the preset area, and if so, confirming that the suspicious target is a ground vehicle.
2. The method of claim 1, further comprising:
and according to different recognition results of the suspicious target, adopting different colors to identify the suspicious target.
3. An apparatus for identifying low altitude objects, the apparatus comprising:
the detection module is used for detecting suspicious targets in a preset area;
the system comprises an acquisition module, a tracking module and a processing module, wherein the acquisition module is used for detecting and tracking a suspicious target when the suspicious target is detected so as to acquire video information of the suspicious target in a detection and tracking process, and the video information comprises visible light video information and infrared video information;
the extraction module is used for extracting the behavior characteristics of the suspicious target according to the video information, processing the video information through a video image stabilization algorithm before extracting the behavior characteristics of the suspicious target according to the video information, compensating the motion of the photoelectric identification subsystem, detecting a motion candidate target area from a motion-compensated video image through a low-rank matrix recovery algorithm, and removing fine noise points in the motion candidate target area;
the recognition module is used for recognizing and classifying the characteristic parameters corresponding to the behavior characteristics by adopting a preset deep learning classification model and recognizing whether the suspicious target is an unmanned aerial vehicle or not according to the recognition and classification result; if the identification result of the suspicious target is the unmanned aerial vehicle, acquiring the sound characteristic of the suspicious target according to the video information; according to whether the sound characteristics of the suspicious target are consistent with the sound characteristics of the corresponding unmanned aerial vehicle in a preset sound library or not, if so, determining that the suspicious target is the unmanned aerial vehicle;
the extraction module comprises:
the configuration unit is used for analyzing the importance of each image frame in the video information on the behavior recognition of the current video information based on a preset neural network model, and matching the weight of each image frame in the preset behavior recognition model according to the importance of each image frame on the behavior recognition of the current video information;
the extraction unit is used for extracting the behavior characteristics of the suspicious target based on the behavior recognition model after weight adjustment;
the device further comprises:
the prediction module is used for predicting the running track of the suspicious target according to the video information after the suspicious target is identified as the unmanned aerial vehicle according to the identification and classification result and if the suspicious target is identified as the ground vehicle;
the analysis module is used for acquiring city construction information of the preset area and analyzing the path configuration of each road in the preset area according to the city construction information;
and the checking module is used for judging whether a road with the path configuration consistent with the running track of the suspicious target exists in each road of the preset area, and if so, confirming that the suspicious target is a ground vehicle.
4. The apparatus of claim 3, further comprising:
and the identification module is used for identifying the suspicious target by adopting different colors according to different identification results of the suspicious target.
5. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1-2.
6. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method according to any of claims 1-2 are implemented when the processor executes the program.
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| CN112288655B (en) * | 2020-11-09 | 2022-11-01 | 南京理工大学 | Sea surface image stabilization method based on MSER region matching and low-rank matrix decomposition |
| CN113569644B (en) * | 2021-06-28 | 2024-03-15 | 西安理工大学 | Airport bird target detection method based on machine vision |
| CN115457420B (en) * | 2022-11-10 | 2023-03-17 | 松立控股集团股份有限公司 | Vehicle weight detection method based on low contrast ratio at night when unmanned aerial vehicle shoots |
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