CN110210568A - The recognition methods of aircraft trailing vortex and system based on convolutional neural networks - Google Patents
The recognition methods of aircraft trailing vortex and system based on convolutional neural networks Download PDFInfo
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- CN110210568A CN110210568A CN201910492682.5A CN201910492682A CN110210568A CN 110210568 A CN110210568 A CN 110210568A CN 201910492682 A CN201910492682 A CN 201910492682A CN 110210568 A CN110210568 A CN 110210568A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- 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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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/243—Classification techniques relating to the number of classes
Abstract
The present invention relates to a kind of recognition methods of aircraft trailing vortex and system based on convolutional neural networks, the method comprising the steps of: receiving aircraft trailing vortex detection image to be identified, identify that output obtains recognizing the probability value and the unidentified probability value to trailing vortex of trailing vortex to the aircraft trailing vortex detection image using preparatory trained convolutional neural networks model；If the probability value for recognizing trailing vortex is greater than the unidentified probability value to trailing vortex, identify in the aircraft trailing vortex detection image to be identified there are trailing vortex, it is on the contrary then identify and trailing vortex is not present in the aircraft trailing vortex detection image to be identified.Recognition accuracy of the present invention is high, trailing vortex under existing meteorological condition can be detected in real time whether there is, reasonably evade trailing vortex instruction for air traffic controller's sending and provides necessary auxiliary information, and existing wake vortex separation can be reduced, the capacity in airspace and airport is improved, and then improves Effectiveness of Regulation.
Description
Technical field
The present invention relates to technical field of aerospace, in particular to a kind of aircraft trailing vortex identification side based on convolutional neural networks
Method and system.
Background technique
With the development of China's aircraft industry big scale of construction, high speed, airspace and ground safeguard inadequate resource, each aviation hub are gulped down
The amount of spitting is saturated, and brings unprecedented challenge to China Civil Aviation safe operation.It is preceding in the takeoff and landing stage of aircraft
The trailing vortex that machine generates can bring potential threat to the flight safety of its rear aircraft, and in the starting heats of aircraft, liftoff climb
Be entire in-flight most dangerous three phases into nearly landing period, correctly identify Aircraft Training Vortices and reasonably avoiding trailing vortex at
In order to ensure the essential condition of aircraft industry flight safety.The country is in terms of the identification for aircraft trailing vortex at present, according to FAA
In the aircraft grade classification new method based on wake flow that 2012 formulate, aircraft is divided into heavy, medium-sized, light-duty three classes,
Trailing vortex is identified and evaded according to time interval in radar control, but the interval is overly conservative, seriously restricts
The fast development of aircraft industry, and under different meteorological conditions, the Evolution of trailing vortex is not fully consistent, therefore the party
Method wastes a large amount of airspace capacity, and inefficiency.
Summary of the invention
It is an object of the invention to improve the above-mentioned deficiency in the presence of the prior art, provide a kind of based on convolutional Neural net
The accuracy of trailing vortex identification can be improved in the aircraft trailing vortex recognition methods of network and system, increases airspace capacity.
In order to achieve the above-mentioned object of the invention, the embodiment of the invention provides following technical schemes:
On the one hand, a kind of aircraft trailing vortex recognition methods based on convolutional neural networks is provided in the embodiment of the present invention,
Comprising steps of
Aircraft trailing vortex detection image to be identified is received, using preparatory trained convolutional neural networks model to described
Aircraft trailing vortex detection image is identified that output obtains recognizing the probability value and the unidentified probability value to trailing vortex of trailing vortex；
Compare output as a result, identifying institute if the probability value for recognizing trailing vortex is greater than the unidentified probability value to trailing vortex
It states in aircraft trailing vortex detection image to be identified there are trailing vortex, it is on the contrary then identify the aircraft trailing vortex detection to be identified
Trailing vortex is not present in image.
On the other hand, the aircraft trailing vortex identification side the embodiment of the invention provides another kind based on convolutional neural networks
Method, comprising the following steps:
Acquisition is used for the aircraft trailing vortex detection image of model training；
The aircraft trailing vortex detection image of acquisition is classified, is divided into and recognizes trailing vortex and unidentified to two class of trailing vortex,
And respective labels are carried out to sorted aircraft trailing vortex detection image, as training set；
Learning parameter in random initializtion convolutional neural networks model, and training obtains convolutional neural networks model: it will
Part sample image in training set inputs in initialized convolutional neural networks model, and output obtains "current" model parameter
Under, the probability value and the unidentified probability value to trailing vortex of trailing vortex are recognized in sample image, and loss meter is carried out to output result
It calculates, the learning parameter in convolutional neural networks model is updated according to costing bio disturbance result；Circulation executes this step, until convolution mind
Through network convergence.
On the other hand, a kind of aircraft trailing vortex identifying system based on convolutional neural networks is additionally provided in the present embodiment,
Include:
It is integrated with the electronic equipment of preparatory trained convolutional neural networks model, for the aviation to be identified to input
Device trailing vortex detection image is identified that output obtains recognizing the probability value and the unidentified probability value to trailing vortex of trailing vortex.
It further include image capture device in above system, for acquiring the aircraft trailing vortex detection image to be identified,
Or acquire sample image for training the convolutional neural networks model.It is laser doppler thunder that described image, which acquires equipment,
It reaches.
In another aspect, the embodiment of the present invention provides a kind of computer-readable storage including computer-readable instruction simultaneously
Medium, the computer-readable instruction make processor execute the operation in method described in the embodiment of the present invention when executed.
In another aspect, the embodiment of the present invention provides a kind of electronic equipment simultaneously, comprising: memory stores program instruction；
Processor is connected with the memory, executes the program instruction in memory, realizes in method described in the embodiment of the present invention
The step of.
Compared with prior art, beneficial effects of the present invention: method and system provided by the invention is based on convolutional Neural net
Network realizes the identification of aircraft trailing vortex, and accuracy is high, and can detect trailing vortex under existing meteorological condition in real time whether there is, and be
Air traffic controller's sending reasonably evades trailing vortex instruction and provides necessary auxiliary information, and can reduce existing tail
The capacity in airspace and airport is improved, and then improves Effectiveness of Regulation in whirlpool interval.
Detailed description of the invention
In order to illustrate the technical solution of the embodiments of the present invention more clearly, below will be to needed in the embodiment attached
Figure is briefly described, it should be understood that the following drawings illustrates only certain embodiments of the present invention, therefore is not construed as pair
The restriction of range for those of ordinary skill in the art without creative efforts, can also be according to this
A little attached drawings obtain other relevant attached drawings.
Fig. 1 is the structure for the aircraft trailing vortex identifying system based on convolutional neural networks that present pre-ferred embodiments provide
Schematic diagram.
Fig. 2 is the aircraft trailing vortex recognition methods method stream based on convolutional neural networks that present pre-ferred embodiments provide
Cheng Tu.
Fig. 3 is the structural schematic block diagram of a kind of electronic equipment provided in embodiment.
Fig. 4 a-d is respectively the recognition result schematic diagram to 4 aircraft trailing vortex detection images.
Description of symbols in figure: 100- aircraft；200- image capture device；300- electronic equipment.
Specific embodiment
Below in conjunction with attached drawing in the embodiment of the present invention, technical solution in the embodiment of the present invention carries out clear, complete
Ground description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.Usually exist
The component of the embodiment of the present invention described and illustrated in attached drawing can be arranged and be designed with a variety of different configurations herein.Cause
This, is not intended to limit claimed invention to the detailed description of the embodiment of the present invention provided in the accompanying drawings below
Range, but it is merely representative of selected embodiment of the invention.Based on the embodiment of the present invention, those skilled in the art are not doing
Every other embodiment obtained under the premise of creative work out, shall fall within the protection scope of the present invention.
As shown in Figure 1, the aircraft trailing vortex identifying system provided in this embodiment based on convolutional neural networks, includes figure
As acquisition equipment and electronic equipment.
Wherein, image capture device is for acquiring aircraft trailing vortex detection image to be identified, or acquisition for training
The sample image of convolutional neural networks model.Image capture device preferably uses Doppler lidar, such as Wind 3D how general
Laser radar is strangled, and when airport into close aircraft trailing vortex to detecting, it is right using range-height display mode (RHI)
It detects airspace and carries out pitching scanning.
Wherein, it is integrated with preparatory trained convolutional neural networks model in the electronic equipment, and utilizes convolution mind
It is identified through to be identified aircraft trailing vortex detection image of the network model to input, output obtains recognizing the probability of trailing vortex
Value and the unidentified probability value to trailing vortex identify if the probability value for recognizing trailing vortex is greater than the unidentified probability value to trailing vortex
It is on the contrary then identify the aircraft trailing vortex to be identified out in the aircraft trailing vortex detection image to be identified there are trailing vortex
Trailing vortex is not present in detection image.
The preparatory trained convolutional neural networks model, can be and be placed in electronic equipment by way of merging,
It can be electronic equipment directly training to obtain.If electronic equipment directly training obtains, then training process is as follows:
Learning parameter in random initializtion convolutional neural networks model, and training obtains convolutional neural networks model: it will
Part sample image in training set inputs in initialized convolutional neural networks model, and output obtains "current" model parameter
Under, the probability value and the unidentified probability value to trailing vortex of trailing vortex are recognized in sample image, and loss meter is carried out to output result
It calculates, the learning parameter in convolutional neural networks model is updated according to costing bio disturbance result；Circulation executes aforesaid operations, until convolution
Neural network convergence.
Referring to Fig. 2, a kind of aircraft trailing vortex identification side based on convolutional neural networks is provided in the present embodiment simultaneously
Method, this method are based on above system and realize.Specifically, the described method comprises the following steps:
Step 1, application image acquire equipment and acquire a large amount of aircraft trailing vortex detection images, to be used for model training.
Step 2 classifies the aircraft trailing vortex detection image of acquisition, is divided into and recognizes trailing vortex and unidentified to tail
Two class of whirlpool, and respective labels are carried out to sorted aircraft trailing vortex detection image, as training set.
Step 3 obtains convolutional neural networks model using training set training.
Specifically, the training process of convolutional neural networks model is as follows:
Step 1, the learning parameter in random initializtion convolutional neural networks model；
Step 2, the part sample image in training set is inputted in initialized convolutional neural networks model, is worked as
The probability value and the unidentified probability value to trailing vortex of trailing vortex are recognized under preceding model parameter, in sample image；The sample image
For the aircraft trailing vortex detection image through manual tag；
Step 3, costing bio disturbance is carried out to the output result in step 2, and seeks the average damage of the part sample image
It loses；
Step 4, it solves and minimizes average loss, and update the learning parameter in convolutional neural networks model；
Step 5, circulation executes step 2~step 4, until convolutional neural networks are restrained.
In the present embodiment, the training process of convolutional neural networks model is not improved, using conventional training method, because
This does not do excessive elaboration herein for training process.After the completion of model training, i.e., aircraft trailing vortex is visited using the model
Altimetric image is identified.
Step 4 acquires aircraft trailing vortex detection image to be identified using image capture device in real time.
Step 5, using trained convolutional neural networks model to the aircraft trailing vortex detection image to be identified into
Row identification, output obtain recognizing the probability value and the unidentified probability value to trailing vortex of trailing vortex, according to output result it is determining described in
It whether there is trailing vortex in aircraft trailing vortex detection image to be identified.If the probability value for recognizing trailing vortex is greater than unidentified to trailing vortex
Probability value, then identify in the aircraft trailing vortex detection image to be identified there are trailing vortex, it is on the contrary then identify it is described to
Trailing vortex is not present in the aircraft trailing vortex detection image of identification
Fig. 4 a-d is please referred to, respectively using trained convolutional neural networks model to 4 aircraft trailing vortex detection figures
The recognition result schematic diagram of picture, Fig. 4 a-b " predict class 0 " are to recognize aircraft trailing vortex, wherein two images point
The probability for not being classified as recognize trailing vortex is 91.869% and 87.869%；Fig. 4 c-d " predict class 10 " is not know
It is clipped to aircraft trailing vortex, it is 92.476% He that wherein two images are classified as the unidentified probability to trailing vortex respectively
93.669%.Trailing vortex under existing meteorological condition can be detected in real time using the above method whether there is, and be air traffic pipe
Personnel's sending processed reasonably evades trailing vortex instruction and provides necessary auxiliary information, and can reduce existing wake vortex separation, mentions
The capacity in high airspace and airport, and improve Effectiveness of Regulation.
As shown in figure 3, the present embodiment provides a kind of electronic equipment simultaneously, which may include 51 He of processor
Memory 52, wherein memory 52 is coupled to processor 51.It is worth noting that, the figure is exemplary, it can also be used
The structure is supplemented or substituted to the structure of his type.
As shown in figure 3, the electronic equipment can also include: input unit 53, display unit 54 and power supply 55.It is worth noting
, which is also not necessary to include all components shown in Fig. 3.In addition, electronic equipment can also include
The component being not shown in Fig. 3 can refer to the prior art.
Processor 51 is sometimes referred to as controller or operational controls, may include microprocessor or other processor devices and/
Or logic device, the processor 51 receive the operation of all parts of input and controlling electronic devices.
Wherein, memory 52 for example can be buffer, flash memory, hard disk driver, removable medium, volatile memory, it is non-easily
The property lost one of memory or other appropriate devices or a variety of, can store configuration information, the processor 51 of above-mentioned processor 51
The information such as instruction, the image data of execution.Processor 51 can execute memory 52 storage program, with realize information storage or
Processing etc..It in one embodiment, further include buffer storage in memory 52, i.e. buffer, to store average information.
Input unit 53 for example can be document reading apparatus, for providing aircraft trailing vortex detection figure to processor 51
Picture.Display unit 54 passes through sorted aircraft trailing vortex detection image label figure for showing, which for example can be with
For LCD display, but the present invention is not limited thereto.Power supply 55 is used to provide electric power for electronic equipment.
The embodiment of the present invention also provides a kind of computer-readable instruction, wherein when executing described instruction in the electronic device
When, described program makes electronic equipment execute the operating procedure that the above-mentioned method realized based on electronic equipment end is included.
The embodiment of the present invention also provides a kind of storage medium for being stored with computer-readable instruction, wherein the computer can
Reading instruction makes electronic equipment execute the operating procedure that the above-mentioned method realized based on electronic equipment end is included.
It should be understood that in various embodiments of the present invention, magnitude of the sequence numbers of the above procedures are not meant to execute suitable
Sequence it is successive, the execution of each process sequence should be determined by its function and internal logic, the implementation without coping with the embodiment of the present invention
Process constitutes any restriction.
Those of ordinary skill in the art may be aware that list described in conjunction with the examples disclosed in the embodiments of the present disclosure
Member and algorithm steps, can be realized with electronic hardware, computer software, or a combination of the two, in order to clearly demonstrate hardware
With the interchangeability of software, each exemplary composition and step are generally described according to function in the above description.This
A little functions are implemented in hardware or software actually, the specific application and design constraint depending on technical solution.Specially
Industry technical staff can use different methods to achieve the described function each specific application, but this realization is not
It is considered as beyond the scope of this invention.
It is apparent to those skilled in the art that for convenience of description and succinctly, foregoing description is
The specific work process of system, device and unit, can refer to corresponding processes in the foregoing method embodiment, details are not described herein.
In several embodiments provided herein, it should be understood that disclosed systems, devices and methods, it can be with
It realizes by another way.For example, the apparatus embodiments described above are merely exemplary, for example, the unit
It divides, only a kind of logical function partition, there may be another division manner in actual implementation, such as multiple units or components
It can be combined or can be integrated into another system, or some features can be ignored or not executed.In addition, shown or beg for
Opinion mutual coupling, direct-coupling or communication connection can be through some interfaces, the INDIRECT COUPLING of device or unit
Or communication connection, it is also possible to electricity, mechanical or other form connections.
The unit as illustrated by the separation member may or may not be physically separated, aobvious as unit
The component shown may or may not be physical unit, it can and it is in one place, or may be distributed over multiple
In network unit.Some or all of unit therein can be selected to realize the embodiment of the present invention according to the actual needs
Purpose.
It, can also be in addition, the functional units in various embodiments of the present invention may be integrated into one processing unit
It is that each unit physically exists alone, is also possible to two or more units and is integrated in one unit.It is above-mentioned integrated
Unit both can take the form of hardware realization, can also realize in the form of software functional units.
If the integrated unit is realized in the form of SFU software functional unit and sells or use as independent product
When, it can store in a computer readable storage medium.Based on this understanding, technical solution of the present invention is substantially
The all or part of the part that contributes to existing technology or the technical solution can be in the form of software products in other words
It embodies, which is stored in a storage medium, including some instructions are used so that a computer
Equipment (can be personal computer, server or the network equipment etc.) executes the complete of each embodiment the method for the present invention
Portion or part steps.And storage medium above-mentioned includes: USB flash disk, mobile hard disk, read-only memory (ROM, Read-Only
Memory), random access memory (RAM, Random Access Memory), magnetic or disk etc. are various can store journey
The medium of sequence code.
The above description is merely a specific embodiment, but scope of protection of the present invention is not limited thereto, any
Those familiar with the art in the technical scope disclosed by the present invention, can easily think of the change or the replacement, and should all contain
Lid is within protection scope of the present invention.Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (9)
1. a kind of aircraft trailing vortex recognition methods based on convolutional neural networks, which is characterized in that comprising steps of
Aircraft trailing vortex detection image to be identified is received, using preparatory trained convolutional neural networks model to the aviation
Device trailing vortex detection image is identified that output obtains recognizing the probability value and the unidentified probability value to trailing vortex of trailing vortex；
Compare output as a result, if recognize trailing vortex probability value be greater than the unidentified probability value to trailing vortex, identify described in
It is on the contrary then identify the aircraft trailing vortex detection image to be identified there are trailing vortex in the aircraft trailing vortex detection image of identification
In be not present trailing vortex.
2. the method according to claim 1, wherein the convolutional neural networks model passes through following steps training
It obtains:
Step 1, the learning parameter in random initializtion convolutional neural networks model；
Step 2, the part sample image in training set is inputted in initialized convolutional neural networks model, obtains current mould
The probability value and the unidentified probability value to trailing vortex of trailing vortex are recognized under shape parameter, in sample image；The sample image is warp
The aircraft trailing vortex detection image of manual tag；
Step 3, costing bio disturbance is carried out to the output result in step 2, and seeks the average loss of the part sample image；
Step 4, it solves and minimizes average loss, and update the learning parameter in convolutional neural networks model；
Step 5, circulation executes step 2~step 4, until convolutional neural networks are restrained.
3. a kind of aircraft trailing vortex recognition methods based on convolutional neural networks, which comprises the following steps:
Acquisition is used for the aircraft trailing vortex detection image of model training；
The aircraft trailing vortex detection image of acquisition is classified, is divided into and recognizes trailing vortex and unidentified to two class of trailing vortex and right
Sorted aircraft trailing vortex detection image carries out respective labels, as training set；
Learning parameter in random initializtion convolutional neural networks model, and training obtains convolutional neural networks model: it will train
The part sample image of concentration inputs in initialized convolutional neural networks model, and output obtains under "current" model parameter, sample
The probability value and the unidentified probability value to trailing vortex of trailing vortex are recognized in this image, and costing bio disturbance, root are carried out to output result
The learning parameter in convolutional neural networks model is updated according to costing bio disturbance result；Circulation executes this step, until convolutional Neural net
Network convergence.
4. according to the method described in claim 3, it is characterized in that, further comprising the steps of:
Acquire aircraft trailing vortex detection image to be identified；
The aircraft trailing vortex detection image to be identified is identified using trained convolutional neural networks model, is exported
The probability value and the unidentified probability value to trailing vortex for obtaining recognizing trailing vortex determine the aviation to be identified according to output result
It whether there is trailing vortex in device trailing vortex detection image.
5. a kind of aircraft trailing vortex identifying system based on convolutional neural networks characterized by comprising
It is integrated with the electronic equipment of preparatory trained convolutional neural networks model, for the aircraft tail to be identified to input
Whirlpool detection image is identified that output obtains recognizing the probability value and the unidentified probability value to trailing vortex of trailing vortex.
6. system according to claim 5, which is characterized in that it further include image capture device, it is described wait know for acquiring
Other aircraft trailing vortex detection image, or acquire the sample image for training the convolutional neural networks model.
7. system according to claim 6, which is characterized in that it is Doppler lidar that described image, which acquires equipment,.
8. a kind of computer readable storage medium including computer-readable instruction, which is characterized in that the computer-readable finger
Enable the operation for requiring processor perform claim in any the method for 1-2.
9. a kind of electronic equipment, which is characterized in that the equipment includes:
Memory stores program instruction；
Processor is connected with the memory, executes the program instruction in memory, realizes that claim 1-2 is any described
Step in method.
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| CN112182762A (en) * | 2020-09-28 | 2021-01-05 | 中国民用航空飞行学院 | Aircraft wake vortex CFD (computational fluid dynamics) calculation method and system based on self-adaptive grid |
| CN112488159A (en) * | 2020-11-13 | 2021-03-12 | 中国民用航空飞行学院 | Wake vortex identification method based on neural network |
| CN113011557A (en) * | 2021-02-22 | 2021-06-22 | 山东航空股份有限公司 | Method and system for judging unstable approach of airplane based on convolutional neural network |
| CN113011557B (en) * | 2021-02-22 | 2021-09-21 | 山东航空股份有限公司 | Method and system for judging unstable approach of airplane based on convolutional neural network |
| CN113297910A (en) * | 2021-04-25 | 2021-08-24 | 云南电网有限责任公司信息中心 | Distribution network field operation safety belt identification method |
| US20230410470A1 (en) * | 2022-06-16 | 2023-12-21 | The Government of the United States of America, as represented by the Secretary of Homeland Security | Systems and Methods for Detecting a Travelling Object Vortex |
| US11922676B2 (en) * | 2022-06-16 | 2024-03-05 | The Government of the United States of America, as represented by the Secretary of Homeland Security | Systems and methods for detecting a travelling object vortex |
| CN115372988A (en) * | 2022-10-18 | 2022-11-22 | 青岛镭测创芯科技有限公司 | Method, device and medium for identifying and positioning aircraft wake vortexes |
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