CN112966636A - Automatic identification method for passenger elevator car approach aircraft in flight area of civil aviation airport - Google Patents
Automatic identification method for passenger elevator car approach aircraft in flight area of civil aviation airport Download PDFInfo
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- CN112966636A CN112966636A CN202110299915.7A CN202110299915A CN112966636A CN 112966636 A CN112966636 A CN 112966636A CN 202110299915 A CN202110299915 A CN 202110299915A CN 112966636 A CN112966636 A CN 112966636A
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- 238000013459 approach Methods 0.000 title claims abstract description 18
- 238000001514 detection method Methods 0.000 claims description 15
- 238000013528 artificial neural network Methods 0.000 claims description 6
- 238000013527 convolutional neural network Methods 0.000 claims description 6
- 238000010801 machine learning Methods 0.000 claims description 6
- 238000003032 molecular docking Methods 0.000 claims description 6
- 238000003062 neural network model Methods 0.000 claims description 6
- 239000013598 vector Substances 0.000 claims description 6
- 238000012544 monitoring process Methods 0.000 claims description 5
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- 238000004458 analytical method Methods 0.000 abstract description 4
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
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- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
Abstract
The invention discloses an automatic identification method for a passenger elevator car in a flight area of a civil aviation airport to approach an aircraft, which identifies whether the approach between the aircraft and the passenger elevator car meets the specification through videos and algorithms, and specifically comprises the following process nodes: the method comprises the following steps of occurrence of an aircraft, parking and stopping of the aircraft, position interpretation of a passenger elevator car, moving state of the passenger elevator car, stopping and moving of the passenger elevator car and distance judgment, and specifically comprises the following steps: s1, identifying the apron area, including a safety area and a stop line; s2, identifying the aircraft and the passenger elevator car; s3, positioning the aircraft and the passenger elevator car; s4, judging the states of the aircraft and the passenger elevator car; and S5, identifying whether the butt joint is normal or not, and alarming and recording if the butt joint is illegal, wherein the content is corresponding screenshot and video. The invention judges whether the whole process of the passenger elevator car for approaching the aircraft meets the standard or not by combining the camera with the video analysis algorithm.
Description
Technical Field
The invention relates to an automatic identification method, in particular to an automatic identification method for a passenger ladder car in a flight area of a civil aviation airport to approach an aircraft.
Background
The rapid development of artificial intelligence in recent years enables the AI technology to be more and more accessed into the actual life, the automatic identification technology of the AI has wide application in various fields, the life production efficiency is improved, and a large amount of manpower and material resource costs can be saved. With the rapid development of global economy, people have higher and higher requirements on efficient travel, and the civil aviation industry becomes more and more prosperous under the background. Nowadays, each airport has a great number of flights to take off and land every day, and in order to guarantee the safety of the aircrafts and the specifications of entering and exiting ports, the airports need to spend a great number of resources to monitor the state of the aircrafts, and at present, the traditional manual supervision mode is mainly adopted to record the current state of the aircrafts.
Disclosure of Invention
The invention aims to solve the technical problem of providing an automatic identification method for the approach of a passenger elevator car to an aircraft in a flight area of a civil aviation airport, wherein a camera is combined with a video analysis algorithm to judge whether the whole approach process of the passenger elevator car to the aircraft meets the specification or not.
The invention discloses an automatic identification method for a passenger elevator car in a flight area of a civil aviation airport to approach an aircraft, which is realized by the following technical scheme: whether the approach connection between the aircraft and the passenger elevator car meets the specification or not is identified through videos and an algorithm, and the method specifically comprises the following process nodes: the method comprises the following steps of occurrence of an aircraft, parking and stopping of the aircraft, position interpretation of a passenger elevator car, moving state of the passenger elevator car, stopping and moving of the passenger elevator car and distance judgment, and specifically comprises the following steps:
s1, identifying the apron area, including a safety area and a stop line;
s2, identifying the aircraft and the passenger elevator car;
s3, positioning the aircraft and the passenger elevator car;
s4, judging the states of the aircraft and the passenger elevator car;
s5, identifying whether normal docking is performed, and alarming and recording if illegal docking is performed, wherein the content is corresponding screenshot and video;
the method for identifying the aircraft and the passenger elevator car is characterized in that the target detection comprises the identification of the aircraft and the passenger elevator car, a classification model is built by applying a Tensorflow machine learning frame and a neural network algorithm to identify whether the current object is the aircraft, and a target detection model is used in the process.
As a preferred technical scheme, a target detection model algorithm is used for target detection and is an SSD-Mobile model, before the target detection is used, the data are converted into compressed data in a specific format, then the compressed data are used as the input of the SSD-Mobile, parameters of a neural network model are continuously adjusted in a machine learning mode, and the structure of the neural network model is correspondingly changed.
As a preferred technical scheme, for the classification and identification of the aircraft and the passenger elevator cars, the core of the model is to apply a convolutional neural network, adopt openCV to read a monitoring video, input a certain frame in the video into the neural network in the form of a picture, and carry out convolution operation on the input picture by the convolutional neural network; after convolution and pooling for many times, the extracted high-dimensional feature maps are smaller and smaller in size, namely the high-dimensional vectors are straightened into one-dimensional vectors and input into the full-connected layer, and then the probability of the categories of the articles is output through calculation of the softmax function.
As a preferable technical solution, the object recognition includes identifying the operation of the passenger train, and a determination model in a space is established to draw a triangular region between the passenger train and the aircraft, and determine whether or not a person is present in the region.
As a preferred technical solution, identification of apron area, safety zone and stop line: the angle and the position of the camera are fixed, the customization line of the safety zone of the parking apron is fixed, the position of each zone in the video is obtained in the form of preset value parameters, and whether the aircraft normally stops is judged through a safety point drawn in advance and the current position of the aircraft.
The invention has the beneficial effects that: the invention judges whether the whole process of the passenger elevator car for approaching the aircraft meets the standard or not by combining the camera with the video analysis algorithm.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a shutdown flow chart of the present invention.
Detailed Description
All of the features disclosed in this specification, or all of the steps in any method or process so disclosed, may be combined in any combination, except combinations of features and/or steps that are mutually exclusive.
Any feature disclosed in this specification (including any accompanying claims, abstract and drawings), may be replaced by alternative features serving equivalent or similar purposes, unless expressly stated otherwise. That is, unless expressly stated otherwise, each feature is only an example of a generic series of equivalent or similar features.
In the description of the present invention, it is to be understood that the terms "one end", "the other end", "outside", "upper", "inside", "horizontal", "coaxial", "central", "end", "length", "outer end", and the like, indicate orientations or positional relationships based on those shown in the drawings, and are used only for convenience in describing the present invention and for simplicity in description, and do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed in a particular orientation, and be operated, and thus, should not be construed as limiting the present invention.
Further, in the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
The use of terms such as "upper," "above," "lower," "below," and the like in describing relative spatial positions herein is for the purpose of facilitating description to describe one element or feature's relationship to another element or feature as illustrated in the figures. The spatially relative positional terms may be intended to encompass different orientations of the device in use or operation in addition to the orientation depicted in the figures. For example, if the device in the figures is turned over, elements described as "below" or "beneath" other elements or features would then be oriented "above" the other elements or features. Thus, the exemplary term "below" can encompass both an orientation of above and below. The device may be otherwise oriented and the spatially relative descriptors used herein interpreted accordingly.
In the present invention, unless otherwise explicitly specified or limited, the terms "disposed," "sleeved," "connected," "penetrating," "plugged," and the like are to be construed broadly, e.g., as a fixed connection, a detachable connection, or an integral part; can be mechanically or electrically connected; they may be directly connected or indirectly connected through intervening media, or they may be connected internally or in any other suitable relationship, unless expressly stated otherwise. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
As shown in fig. 1, according to the automatic identification method for the approach of the passenger stairway to the aircraft in the flight area of the civil airport, the following process nodes appear in the approach of the passenger stairway to the aircraft: the method comprises the following steps of aircraft occurrence, aircraft parking stop, passenger elevator car position interpretation, passenger elevator car moving state, passenger elevator car stop moving and distance judgment;
according to the process, each operation node is identified through a video algorithm, the time of the node is recorded, a complete approach process is formed, if the corresponding node has an illegal event, an alarm is given immediately, and meanwhile, the record is carried out in a process node report, as shown in the following table 1:
the method comprises the following specific steps:
s1, identification of apron area, including safety zone and stop line
S2 identification of aircraft and passenger stairs
S3, positioning of aircraft and passenger stairs
S4, judging the state of the aircraft and the passenger elevator car
S5, identifying whether normal docking is performed, and alarming and recording if illegal docking is performed, wherein the content is corresponding screenshot and video;
the identification of the object is mainly the identification of the aircraft and the passenger elevator car, the identification method of the aircraft is to use a Tensorflow machine learning frame and a neural network algorithm to build a classification model, identify whether the current object is the aircraft, and use a target detection model in the process.
In the embodiment, a target detection model algorithm is used for object detection as an SSD-Mobile model, real-time monitoring videos of an airport are used as training data of the SSD-Mobile model of the target detection algorithm, the data are converted into compressed data in a specific format before use, the compressed data are used as input of the SSD-Mobile, parameters of a neural network model are continuously adjusted in a machine learning mode, and the structure of the neural network model is correspondingly changed, so that the model can better complete a target detection task in the airport in a specific scene.
For the classification and identification of aircrafts and passenger stairs, the core of a target detection model is to use a convolutional neural network, use openCV to read a monitoring video, input a certain frame in the video into the neural network in the form of a picture, and carry out convolution operation on the input picture by the convolutional neural network, so as to extract a feature map of an object in the picture, and after one or more convolutions, carry out pooling operation on the feature map output by a convolutional layer, so as to reduce the size of the feature map, remove irrelevant backgrounds in the feature map, reduce parameters for subsequent algorithm calculation and improve the operation efficiency of the algorithm; after convolution and pooling for many times, the size of the extracted high-dimensional characteristic graph is smaller and smaller, so that the high-dimensional vectors are straightened into one-dimensional vectors and input into the full-connection layer, and then the probability of the categories of the articles is output through calculation of the sofmax function, so that whether the current object is an aircraft or a passenger ladder vehicle is judged, and subsequent judgment is facilitated; the extraction of the door image is a difficult point in the whole process, and the accuracy of the calculated positioning information in the subsequent processing process can be ensured only if the image of the aircraft door is accurately extracted.
In this embodiment, the recognizing of the object includes recognizing an operation of the passenger lift car, drawing a triangular region between the passenger lift car and the aircraft by establishing a judgment model in a space, and judging whether or not a person is present in the drawn triangular region.
In this embodiment, identification of the apron area, the safety zone, and the stop line: the angle position of the camera is fixed, the customization line of the safety zone of the parking apron is fixed, the position of each zone in the video can be obtained in the form of a preset value parameter, and whether the aircraft normally stops can be judged through a safety point which is drawn in advance and the current position of the aircraft; after the aircraft normally stops, the position of the aircraft is fixed, at the moment, the passenger elevator car is abutted to a cabin door of the aircraft, in the real-time monitoring video stream, the relative position of the passenger elevator car in the current frame and the next frame can be identified to record the current motion track of the passenger elevator car, and whether the passenger elevator car is normally close to the cabin door of the aircraft can be judged through the analysis of the track by an algorithm; whether the parking of the passenger lift car is within the normal range can also be determined by the relative distance between the current position of the passenger lift car and the ground pre-marked safety point.
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that are not thought of through the inventive work should be included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope defined by the claims.
Claims (5)
1. The automatic identification method for the approach of the passenger stairs in the flight area of the civil aviation airport to the aircraft is characterized in that whether the approach between the aircraft and the passenger stairs meets the specification or not is identified through videos and an algorithm, and the method specifically comprises the following process nodes: the method comprises the following steps of occurrence of an aircraft, parking and stopping of the aircraft, position interpretation of a passenger elevator car, moving state of the passenger elevator car, stopping and moving of the passenger elevator car and distance judgment, and specifically comprises the following steps:
s1, identifying the apron area, including a safety area and a stop line;
s2, identifying the aircraft and the passenger elevator car;
s3, positioning the aircraft and the passenger elevator car;
s4, judging the states of the aircraft and the passenger elevator car;
s5, identifying whether normal docking is performed, and alarming and recording if illegal docking is performed, wherein the content is corresponding screenshot and video;
the method for identifying the aircraft and the passenger elevator car is characterized in that the target detection comprises the identification of the aircraft and the passenger elevator car, a classification model is built by applying a Tensorflow machine learning frame and a neural network algorithm to identify whether the current object is the aircraft, and a target detection model is used in the process.
2. The method for the automatic identification of the approach of passenger stairs to aircraft in the flight area of civil airports according to claim 1, characterized in that: for target detection, a target detection model algorithm is used as an SSD-Mobile model, before use, the data are converted into compressed data in a specific format, then the compressed data are used as input of the SSD-Mobile, parameters of a neural network model are continuously adjusted in a machine learning mode, and the structure of the neural network model is correspondingly changed.
3. The method for the automatic identification of the approach of passenger stairs to aircraft in the flight area of civil airports according to claim 1, characterized in that: for the classification and identification of the aircraft and the passenger elevator car, the core of the model is to use a convolutional neural network, use openCV to read the monitoring video, input a certain frame in the video into the neural network in the form of a picture, and carry out convolution operation on the input picture by the convolutional neural network; after convolution and pooling for many times, the extracted high-dimensional feature maps are smaller and smaller in size, namely the high-dimensional vectors are straightened into one-dimensional vectors and input into the full-connected layer, and then the probability of the categories of the articles is output through calculation of the softmax function.
4. The method for the automatic identification of the approach of passenger stairs to aircraft in the flight area of civil airports according to claim 1, characterized in that: the object identification comprises the operation identification of the passenger elevator car, a triangular area is drawn between the passenger aircraft car and the aircraft through establishing a judgment model in a space, and whether a person exists in the area is judged.
5. The method for the automatic identification of the approach of passenger stairs to aircraft in the flight area of civil airports according to claim 1, characterized in that: identification of apron area, safety zone and stop line: the angle and the position of the camera are fixed, the customization line of the safety zone of the parking apron is fixed, the position of each zone in the video is obtained in the form of preset value parameters, and whether the aircraft normally stops is judged through a safety point drawn in advance and the current position of the aircraft.
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Cited By (1)
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