CN112530205A - Airport parking apron airplane state detection method and device - Google Patents
Airport parking apron airplane state detection method and device Download PDFInfo
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- CN112530205A CN112530205A CN202011318220.0A CN202011318220A CN112530205A CN 112530205 A CN112530205 A CN 112530205A CN 202011318220 A CN202011318220 A CN 202011318220A CN 112530205 A CN112530205 A CN 112530205A
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- G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
- G08G5/0043—Traffic management of multiple aircrafts from the ground
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- G08G—TRAFFIC CONTROL SYSTEMS
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Abstract
The embodiment of the application provides a method and a device for detecting the state of an airplane at an airport apron, wherein the method comprises the following steps: acquiring a real-time video image of the airport network camera according to the camera SDK, and detecting and analyzing an airplane target; drawing a mask image of an analysis area of the airplane parking apron according to the real-time video image; performing deep learning airplane detection on the real-time video image; queue caching is carried out on the detection result of the deep learning airplane, analysis is carried out, and whether the airplane drives away from an apron is judged; the method and the device can improve the accuracy and the robustness of detection of the airplane state of the airport parking apron, can assist airport managers to monitor the state of the airport parking apron, and have important practical application value.
Description
Technical Field
The application relates to the field of computer vision, in particular to a method and a device for detecting the state of an airplane on an airport apron.
Background
With the rapid development of computer science technology, the application of computer vision technology to automatically and intelligently analyze the airplane target in the monitoring scene gradually becomes a research hotspot.
The detection that the airplane exits the parking apron means that the airplane target is continuously detected in the monitoring area of the network camera, and when the airplane exits the parking apron, an alarm is given out, so that airport management personnel can conveniently perform daily management.
The inventor finds that the prior art lacks a technical scheme for accurately detecting the state of the airplane in the airport apron (particularly whether the airplane is driven away or not).
Disclosure of Invention
Aiming at the problems in the prior art, the method and the device for detecting the airplane state of the airport apron can improve the accuracy and the robustness of detection of the airplane state of the airport apron, can assist airport managers to monitor the state of the airport apron, and have important practical application value.
In order to solve at least one of the above problems, the present application provides the following technical solutions:
in a first aspect, the present application provides a method for detecting the state of an aircraft at an airport apron, comprising:
acquiring a real-time video image of the airport network camera according to the camera SDK, and detecting and analyzing an airplane target;
drawing a mask image of an analysis area of the airplane parking apron according to the real-time video image;
performing deep learning airplane detection on the real-time video image;
and performing queue caching on the detection result of the deep learning aircraft, analyzing, and judging whether the aircraft drives away from the parking apron.
Further, after the drawing a mask map of an analysis area of the aircraft apron according to the real-time video image, the method further includes:
and selecting an aircraft analysis region according to the mask map for eliminating complex background interference.
Further, the performing deep learning aircraft detection on the real-time video image comprises:
and (3) carrying out aircraft detection by adopting YoloV3, if the detection result is that the aircraft exists and the confidence coefficient is greater than a threshold value, the aircraft target exists, the returned result is True, and otherwise, the returned result is False.
Further, the queue caching and analyzing the detection result of the deep learning aircraft, and judging whether the aircraft drives off the parking apron includes:
performing frame-by-frame caching on the detection result of the deep learning aircraft, when the length of a cache queue is less than N, performing enqueuing operation on the detection result of the deep learning aircraft from the tail of the cache queue, when the length of the cache queue is equal to N, performing dequeuing operation on the head of the queue, and performing enqueuing operation on the detection result of the deep learning aircraft from the tail of the cache queue in the next frame;
analyzing the cache queue, counting p elements with a True result in the cache queue, q elements with a False result in the cache queue, setting the current apron state variable as E, defaulting to False, setting the current apron state variable E to be equal to Ture if p is equal to N, judging that the airplane exists in the current apron, and judging that the airplane on the current apron moves and giving an alarm by the system if q is equal to N and the current apron state variable E is equal to Ture.
In a second aspect, the present application provides an airport apron aircraft state detection device, comprising:
the real-time video image acquisition module is used for acquiring a real-time video image of the airport network camera according to the camera SDK and detecting and analyzing an airplane target;
the mask image drawing module is used for drawing a mask image of an analysis area of the airplane apron according to the real-time video image;
the deep learning airplane detection module is used for carrying out deep learning airplane detection on the real-time video image;
and the airplane state judgment module is used for performing queue caching on the detection result of the deep learning airplane and analyzing the detection result to judge whether the airplane drives away from the parking apron.
Further, still include:
and the analysis region determining unit is used for selecting the airplane analysis region according to the mask map and eliminating complex background interference.
Further, the deep learning aircraft detection module includes:
and the YoloV3 detection unit is used for detecting the airplane by adopting YoloV3, if the detection result is the airplane and the confidence coefficient is greater than a threshold value, the airplane target exists, the returned result is True, and otherwise, the returned result is False.
Further, the aircraft state determination module includes:
the queue caching unit is used for caching the detection result of the deep learning aircraft frame by frame, when the length of a cache queue is smaller than N, the detection result of the deep learning aircraft is subjected to enqueuing operation from the tail of the cache queue, when the length of the cache queue is equal to N, the head of the queue is subjected to dequeuing operation, and the detection result of the deep learning aircraft flies next frame is subjected to enqueuing operation from the tail of the cache queue;
and the queue analysis unit is used for analyzing the cache queue, counting p elements with a True result in the cache queue, q elements with a False result in the cache queue, setting the current apron state variable as E and default as False, setting the current apron state variable E to be equal to Ture if p is equal to N, judging that the airplane exists in the current apron, and judging that the airplane on the current apron has moved and giving an alarm by the system if q is equal to N and the current apron state variable E is equal to Ture.
In a third aspect, the present application 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 airport apron aircraft state detection method when executing the program.
In a fourth aspect, the present application provides a computer-readable storage medium having stored thereon a computer program which, when being executed by a processor, carries out the steps of the method for detecting a status of an aircraft in an airport apron.
According to the technical scheme, the method and the device for detecting the airplane state of the airport parking apron are used for acquiring the real-time video image of the airport network camera according to the camera SDK and detecting and analyzing the airplane target; drawing a mask image of an analysis area of the airplane parking apron according to the real-time video image; performing deep learning airplane detection on the real-time video image; queue caching is carried out on the detection result of the deep learning airplane, analysis is carried out, and whether the airplane drives away from an apron is judged; the method and the device can improve the accuracy and the robustness of detection of the airplane state of the airport parking apron, can assist airport managers to monitor the state of the airport parking apron, and have important practical application value.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a schematic flow chart of a method for detecting the status of an airport apron aircraft according to an embodiment of the present application;
FIG. 2 is a second schematic flow chart of a method for detecting the status of an airport apron aircraft according to an embodiment of the present application;
FIG. 3 is a block diagram of an airport apron aircraft condition detection device in an embodiment of the present application;
FIG. 4 is a second block diagram of the airport apron airplane condition detection device in the embodiment of the present application;
FIG. 5 is a third block diagram of an airport apron aircraft condition detection apparatus according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of an electronic device in an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In view of the problem that the prior art lacks a technical scheme for accurately detecting the airplane state (particularly whether the airplane is driven away or not) in the airport apron, the application provides an airport apron airplane state detection method and device, wherein a real-time video image of an airport network camera is obtained according to a camera SDK and is used for detecting and analyzing an airplane target; drawing a mask image of an analysis area of the airplane parking apron according to the real-time video image; performing deep learning airplane detection on the real-time video image; queue caching is carried out on the detection result of the deep learning airplane, analysis is carried out, and whether the airplane drives away from an apron is judged; the method and the device can improve the accuracy and the robustness of detection of the airplane state of the airport parking apron, can assist airport managers to monitor the state of the airport parking apron, and have important practical application value.
In order to improve the accuracy and robustness of detecting the airplane state of the airport apron, assist an airport manager in monitoring the state of the airport apron and have important practical application value, the application provides an embodiment of a method for detecting the airplane state of the airport apron, and referring to fig. 1, the method for detecting the airplane state of the airport apron specifically comprises the following contents:
step S101: and acquiring a real-time video image of the airport network camera according to the camera SDK, and using the real-time video image for detecting and analyzing the airplane target.
Optionally, the camera SDK may be used to obtain a real-time video RGB image of the network camera for subsequent airplane target detection and analysis.
Step S102: and drawing a mask image of the airplane apron analysis area according to the real-time video image.
Optionally, the method and the device can utilize the mask map to select the aircraft analysis region completely, and complex background interference is eliminated.
Step S103: and carrying out deep learning airplane detection on the real-time video image.
Optionally, the YoloV3 may be used to perform aircraft detection, if the detection result is an aircraft and the confidence is greater than 0.8(0< confidence <1), an aircraft target exists, the returned result is True, otherwise, the returned result is False.
Step S104: and performing queue caching on the detection result of the deep learning aircraft, analyzing, and judging whether the aircraft drives away from the parking apron.
Optionally, the method and the device can perform airplane target detection on the monitored scene mask image, and if an airplane exists, the returned result is True, and if the airplane does not exist, the returned result is False; and buffering the detection result frame by frame. And when the length of the buffer queue is smaller than N, carrying out enqueuing operation on the detection result from the tail of the buffer queue. When the buffer queue length is equal to N, the head of the buffer queue performs dequeue operation, and the next frame of aircraft detection result performs enqueue operation from the tail of the buffer queue (N is 30 in this embodiment).
Optionally, the obtained buffer queue with the length of N is analyzed, and p elements with the result of True in the buffer queue and q elements with the result of False in the buffer queue are counted. And setting the current apron state variable as E and defaulting to False. And if p is equal to N, setting the current apron state variable E to be equal to Ture, and judging that the airplane exists in the current apron. And if q is equal to N and the current apron state variable E is equal to Ture, judging that the airplane on the current apron is moved, and alarming by the system.
As can be seen from the above description, the airport apron airplane state detection method provided in the embodiment of the present application can obtain a real-time video image of an airport network camera according to the camera SDK, and is used for detecting and analyzing an airplane target; drawing a mask image of an analysis area of the airplane parking apron according to the real-time video image; performing deep learning airplane detection on the real-time video image; queue caching is carried out on the detection result of the deep learning airplane, analysis is carried out, and whether the airplane drives away from an apron is judged; the method and the device can improve the accuracy and the robustness of detection of the airplane state of the airport parking apron, can assist airport managers to monitor the state of the airport parking apron, and have important practical application value.
In an embodiment of the method for detecting the airplane state of the airport apron, after the drawing a mask map of an analysis area of the airport apron according to the real-time video image, the method further includes:
and selecting an aircraft analysis region according to the mask map for eliminating complex background interference.
In an embodiment of the method for detecting the airplane status of the airport apron, the step S103 may further include the following steps:
and (3) carrying out aircraft detection by adopting YoloV3, if the detection result is that the aircraft exists and the confidence coefficient is greater than a threshold value, the aircraft target exists, the returned result is True, and otherwise, the returned result is False.
In an embodiment of the method for detecting the airplane status of the airport apron of the present application, referring to fig. 2, the step S104 may further include the following steps:
step S201: performing frame-by-frame caching on the detection result of the deep learning aircraft, when the length of a cache queue is less than N, performing enqueuing operation on the detection result of the deep learning aircraft from the tail of the cache queue, when the length of the cache queue is equal to N, performing dequeuing operation on the head of the queue, and performing enqueuing operation on the detection result of the deep learning aircraft from the tail of the cache queue in the next frame;
step S202: analyzing the cache queue, counting p elements with a True result in the cache queue, q elements with a False result in the cache queue, setting the current apron state variable as E, defaulting to False, setting the current apron state variable E to be equal to Ture if p is equal to N, judging that the airplane exists in the current apron, and judging that the airplane on the current apron moves and giving an alarm by the system if q is equal to N and the current apron state variable E is equal to Ture.
In order to improve the accuracy and robustness of detecting the airplane state of the airport apron, and assist an airport manager in monitoring the state of the airport apron, which has an important practical application value, the present application provides an embodiment of an airport apron airplane state detection apparatus for implementing all or part of the airport apron airplane state detection method, which is shown in fig. 3, and specifically includes the following contents:
the real-time video image acquisition module 10 is used for acquiring a real-time video image of the airport network camera according to the camera SDK, and is used for detecting and analyzing an airplane target;
the mask map drawing module 20 is used for drawing a mask map of the analysis area of the airplane apron according to the real-time video image;
a deep learning airplane detection module 30, configured to perform deep learning airplane detection on the real-time video image;
and the aircraft state judgment module 40 is used for performing queue caching on the detection result of the deep learning aircraft, analyzing the detection result and judging whether the aircraft drives away from the parking apron.
As can be seen from the above description, the airport apron airplane state detection device provided in the embodiment of the present application can acquire a real-time video image of an airport network camera according to the camera SDK, and is used for detecting and analyzing an airplane target; drawing a mask image of an analysis area of the airplane parking apron according to the real-time video image; performing deep learning airplane detection on the real-time video image; queue caching is carried out on the detection result of the deep learning airplane, analysis is carried out, and whether the airplane drives away from an apron is judged; the method and the device can improve the accuracy and the robustness of detection of the airplane state of the airport parking apron, can assist airport managers to monitor the state of the airport parking apron, and have important practical application value.
In an embodiment of the airport apron airplane status detection apparatus of the present application, the following contents are further included:
and the analysis region determining unit is used for selecting the airplane analysis region according to the mask map and eliminating complex background interference.
In an embodiment of the airport apron airplane status detection apparatus of the present application, referring to fig. 4, the deep learning airplane detection module 30 includes:
the yoloV3 detection unit 31 is configured to perform aircraft detection by using yoloV3, if the detection result is an aircraft and the confidence is greater than a threshold, an aircraft target exists, the returned result is True, otherwise, the returned result is False.
In an embodiment of the airport apron airplane status detecting apparatus of the present application, referring to fig. 5, the airplane status determining module 40 includes:
the queue caching unit 41 is configured to perform frame-by-frame caching on a detection result of the deep learning aircraft, perform enqueuing operation on the detection result of the deep learning aircraft from the tail of the cache queue when the length of the cache queue is less than N, perform dequeuing operation on the head of the queue when the length of the cache queue is equal to N, and perform enqueuing operation on the detection result of the deep learning aircraft from the tail of the cache queue in the next frame;
and the queue analysis unit 42 is configured to analyze the cache queue, count p elements with a True result in the cache queue, count q elements with a False result in the cache queue, set the current apron state variable to be E, default to be False, set the current apron state variable to be equal to Ture if p is equal to N, determine that the airplane exists on the current apron, and determine that the airplane on the current apron has moved and the system alarms if q is equal to N and the current apron state variable E is equal to Ture.
In terms of hardware, in order to improve the accuracy and robustness of detecting the airplane state of the airport apron, and assist an airport manager in monitoring the state of the airport apron, which has an important practical application value, the present application provides an embodiment of an electronic device for implementing all or part of the contents in the method for detecting the airplane state of the airport apron, where the electronic device specifically includes the following contents:
a processor (processor), a memory (memory), a communication Interface (Communications Interface), and a bus; the processor, the memory and the communication interface complete mutual communication through the bus; the communication interface is used for realizing information transmission between the airport parking apron airplane state detection device and relevant equipment such as a core service system, a user terminal, a relevant database and the like; the logic controller may be a desktop computer, a tablet computer, a mobile terminal, and the like, but the embodiment is not limited thereto. In this embodiment, the logic controller may be implemented with reference to the embodiments of the airport apron airplane state detection method and the airport apron airplane state detection apparatus in the embodiments, and the contents thereof are incorporated herein, and repeated descriptions thereof are omitted.
It is understood that the user terminal may include a smart phone, a tablet electronic device, a network set-top box, a portable computer, a desktop computer, a Personal Digital Assistant (PDA), an in-vehicle device, a smart wearable device, and the like. Wherein, intelligence wearing equipment can include intelligent glasses, intelligent wrist-watch, intelligent bracelet etc..
In practical applications, part of the airport apron airplane state detection method may be executed on the electronic device side as described above, or all operations may be completed in the client device. The selection may be specifically performed according to the processing capability of the client device, the limitation of the user usage scenario, and the like. This is not a limitation of the present application. The client device may further include a processor if all operations are performed in the client device.
The client device may have a communication module (i.e., a communication unit), and may be communicatively connected to a remote server to implement data transmission with the server. The server may include a server on the task scheduling center side, and in other implementation scenarios, the server may also include a server on an intermediate platform, for example, a server on a third-party server platform that is communicatively linked to the task scheduling center server. The server may include a single computer device, or may include a server cluster formed by a plurality of servers, or a server structure of a distributed apparatus.
Fig. 6 is a schematic block diagram of a system configuration of an electronic device 9600 according to an embodiment of the present application. As shown in fig. 6, the electronic device 9600 can include a central processor 9100 and a memory 9140; the memory 9140 is coupled to the central processor 9100. Notably, this FIG. 6 is exemplary; other types of structures may also be used in addition to or in place of the structure to implement telecommunications or other functions.
In one embodiment, airport apron aircraft condition detection method functionality may be integrated into the central processor 9100. The central processor 9100 may be configured to control as follows:
step S101: and acquiring a real-time video image of the airport network camera according to the camera SDK, and using the real-time video image for detecting and analyzing the airplane target.
Step S102: and drawing a mask image of the airplane apron analysis area according to the real-time video image.
Step S103: and carrying out deep learning airplane detection on the real-time video image.
Step S104: and performing queue caching on the detection result of the deep learning aircraft, analyzing, and judging whether the aircraft drives away from the parking apron.
As can be seen from the above description, the electronic device provided in the embodiment of the present application obtains a real-time video image of an airport network camera according to the camera SDK, and uses the real-time video image for detecting and analyzing an airplane target; drawing a mask image of an analysis area of the airplane parking apron according to the real-time video image; performing deep learning airplane detection on the real-time video image; queue caching is carried out on the detection result of the deep learning airplane, analysis is carried out, and whether the airplane drives away from an apron is judged; the method and the device can improve the accuracy and the robustness of detection of the airplane state of the airport parking apron, can assist airport managers to monitor the state of the airport parking apron, and have important practical application value.
In another embodiment, the airport apron airplane status detection apparatus may be configured separately from the central processor 9100, for example, the airport apron airplane status detection apparatus may be configured as a chip connected to the central processor 9100, and the airport apron airplane status detection method function may be implemented by the control of the central processor.
As shown in fig. 6, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is noted that the electronic device 9600 also does not necessarily include all of the components shown in fig. 6; further, the electronic device 9600 may further include components not shown in fig. 6, which may be referred to in the art.
As shown in fig. 6, a central processor 9100, sometimes referred to as a controller or operational control, can include a microprocessor or other processor device and/or logic device, which central processor 9100 receives input and controls the operation of the various components of the electronic device 9600.
The memory 9140 can be, for example, one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, or other suitable device. The information relating to the failure may be stored, and a program for executing the information may be stored. And the central processing unit 9100 can execute the program stored in the memory 9140 to realize information storage or processing, or the like.
The input unit 9120 provides input to the central processor 9100. The input unit 9120 is, for example, a key or a touch input device. Power supply 9170 is used to provide power to electronic device 9600. The display 9160 is used for displaying display objects such as images and characters. The display may be, for example, an LCD display, but is not limited thereto.
The memory 9140 can be a solid state memory, e.g., Read Only Memory (ROM), Random Access Memory (RAM), a SIM card, or the like. There may also be a memory that holds information even when power is off, can be selectively erased, and is provided with more data, an example of which is sometimes called an EPROM or the like. The memory 9140 could also be some other type of device. Memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application/function storage portion 9142, the application/function storage portion 9142 being used for storing application programs and function programs or for executing a flow of operations of the electronic device 9600 by the central processor 9100.
The memory 9140 can also include a data store 9143, the data store 9143 being used to store data, such as contacts, digital data, pictures, sounds, and/or any other data used by an electronic device. The driver storage portion 9144 of the memory 9140 may include various drivers for the electronic device for communication functions and/or for performing other functions of the electronic device (e.g., messaging applications, contact book applications, etc.).
The communication module 9110 is a transmitter/receiver 9110 that transmits and receives signals via an antenna 9111. The communication module (transmitter/receiver) 9110 is coupled to the central processor 9100 to provide input signals and receive output signals, which may be the same as in the case of a conventional mobile communication terminal.
Based on different communication technologies, a plurality of communication modules 9110, such as a cellular network module, a bluetooth module, and/or a wireless local area network module, may be provided in the same electronic device. The communication module (transmitter/receiver) 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide audio output via the speaker 9131 and receive audio input from the microphone 9132, thereby implementing ordinary telecommunications functions. The audio processor 9130 may include any suitable buffers, decoders, amplifiers and so forth. In addition, the audio processor 9130 is also coupled to the central processor 9100, thereby enabling recording locally through the microphone 9132 and enabling locally stored sounds to be played through the speaker 9131.
Embodiments of the present application further provide a computer-readable storage medium capable of implementing all steps of the airport apron airplane state detection method with a server or a client as an execution subject in the above embodiments, where the computer-readable storage medium stores a computer program, and the computer program, when executed by a processor, implements all steps of the airport apron airplane state detection method with a server or a client as an execution subject in the above embodiments, for example, the processor implements the following steps when executing the computer program:
step S101: and acquiring a real-time video image of the airport network camera according to the camera SDK, and using the real-time video image for detecting and analyzing the airplane target.
Step S102: and drawing a mask image of the airplane apron analysis area according to the real-time video image.
Step S103: and carrying out deep learning airplane detection on the real-time video image.
Step S104: and performing queue caching on the detection result of the deep learning aircraft, analyzing, and judging whether the aircraft drives away from the parking apron.
As can be seen from the above description, the computer-readable storage medium provided in the embodiments of the present application obtains a real-time video image of an airport network camera according to the camera SDK, and is used for detecting and analyzing an airplane target; drawing a mask image of an analysis area of the airplane parking apron according to the real-time video image; performing deep learning airplane detection on the real-time video image; queue caching is carried out on the detection result of the deep learning airplane, analysis is carried out, and whether the airplane drives away from an apron is judged; the method and the device can improve the accuracy and the robustness of detection of the airplane state of the airport parking apron, can assist airport managers to monitor the state of the airport parking apron, and have important practical application value.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The principle and the implementation mode of the invention are explained by applying specific embodiments in the invention, and the description of the embodiments is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (10)
1. A method for detecting the status of an airport apron aircraft, the method comprising:
acquiring a real-time video image of the airport network camera according to the camera SDK, and detecting and analyzing an airplane target;
drawing a mask image of an analysis area of the airplane parking apron according to the real-time video image;
performing deep learning airplane detection on the real-time video image;
and performing queue caching on the detection result of the deep learning aircraft, analyzing, and judging whether the aircraft drives away from the parking apron.
2. The method for airport apron aircraft state detection according to claim 1, further comprising, after said mapping a mask of an airport apron analysis area from said real-time video images:
and selecting an aircraft analysis region according to the mask map for eliminating complex background interference.
3. The airport apron aircraft state detection method of claim 1, wherein said deep learning aircraft detection of the real-time video images comprises:
and (3) carrying out aircraft detection by adopting YoloV3, if the detection result is that the aircraft exists and the confidence coefficient is greater than a threshold value, the aircraft target exists, the returned result is True, and otherwise, the returned result is False.
4. The method of claim 1, wherein said queue buffering and analyzing the results of said deep learning aircraft detection to determine if the aircraft is off the tarmac comprises:
performing frame-by-frame caching on the detection result of the deep learning aircraft, when the length of a cache queue is less than N, performing enqueuing operation on the detection result of the deep learning aircraft from the tail of the cache queue, when the length of the cache queue is equal to N, performing dequeuing operation on the head of the queue, and performing enqueuing operation on the detection result of the deep learning aircraft from the tail of the cache queue in the next frame;
analyzing the cache queue, counting p elements with a True result in the cache queue, q elements with a False result in the cache queue, setting the current apron state variable as E, defaulting to False, setting the current apron state variable E to be equal to Ture if p is equal to N, judging that the airplane exists in the current apron, and judging that the airplane on the current apron moves and giving an alarm by the system if q is equal to N and the current apron state variable E is equal to Ture.
5. An airport apron aircraft state detection device, characterized by, includes:
the real-time video image acquisition module is used for acquiring a real-time video image of the airport network camera according to the camera SDK and detecting and analyzing an airplane target;
the mask image drawing module is used for drawing a mask image of an analysis area of the airplane apron according to the real-time video image;
the deep learning airplane detection module is used for carrying out deep learning airplane detection on the real-time video image;
and the airplane state judgment module is used for performing queue caching on the detection result of the deep learning airplane and analyzing the detection result to judge whether the airplane drives away from the parking apron.
6. The airport apron aircraft condition detection apparatus of claim 5, further comprising:
and the analysis region determining unit is used for selecting the airplane analysis region according to the mask map and eliminating complex background interference.
7. The airport apron aircraft state detection device of claim 5, wherein the deep learning aircraft detection module comprises:
and the YoloV3 detection unit is used for detecting the airplane by adopting YoloV3, if the detection result is the airplane and the confidence coefficient is greater than a threshold value, the airplane target exists, the returned result is True, and otherwise, the returned result is False.
8. The airport apron aircraft state detection device of claim 5, wherein the aircraft state judgment module comprises:
the queue caching unit is used for caching the detection result of the deep learning aircraft frame by frame, when the length of a cache queue is smaller than N, the detection result of the deep learning aircraft is subjected to enqueuing operation from the tail of the cache queue, when the length of the cache queue is equal to N, the head of the queue is subjected to dequeuing operation, and the detection result of the deep learning aircraft flies next frame is subjected to enqueuing operation from the tail of the cache queue;
and the queue analysis unit is used for analyzing the cache queue, counting p elements with a True result in the cache queue, q elements with a False result in the cache queue, setting the current apron state variable as E and default as False, setting the current apron state variable E to be equal to Ture if p is equal to N, judging that the airplane exists in the current apron, and judging that the airplane on the current apron has moved and giving an alarm by the system if q is equal to N and the current apron state variable E is equal to Ture.
9. 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 processor, when executing the program, carries out the steps of the method of airport apron aircraft state detection according to any one of claims 1 to 4.
10. 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 airport apron aircraft state detection method of any one of claims 1 to 4.
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