CN110033490B - Airport low-slow small target prevention and control method based on photoelectric image automatic identification - Google Patents

Airport low-slow small target prevention and control method based on photoelectric image automatic identification Download PDF

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CN110033490B
CN110033490B CN201910291490.8A CN201910291490A CN110033490B CN 110033490 B CN110033490 B CN 110033490B CN 201910291490 A CN201910291490 A CN 201910291490A CN 110033490 B CN110033490 B CN 110033490B
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常韶飞
王军
李新磊
刘思宇
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Nanjing University of Science and Technology
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Abstract

The invention discloses a method for preventing and controlling low and slow small targets of an airport based on automatic photoelectric image recognition, which comprises the following steps: acquiring a plurality of images of each low-slow small target from a network, and actually measuring and acquiring a plurality of images of each low-slow small target on site; extracting the characteristics of each image, classifying the target types corresponding to the images according to the extracted image characteristics, and obtaining a low-low small target classifier; preprocessing an image of a target to be prevented and controlled, extracting image characteristics, and then inputting the characteristics into a classifier to acquire the type of the target to be prevented and controlled; and determining the position of the target to be prevented and controlled, and driving corresponding prevention and control equipment to work by combining the type of the target to be prevented and controlled. According to the method, multiple prevention and control strategies of the airport area are realized by classifying different targets, the optimal combination of different low-slow small prevention and control devices is realized, the influence of the low-slow small targets on the airport airspace is greatly reduced, the defects of the traditional manual airspace monitoring and the limitation of a single control strategy are overcome, and the efficiency and the precision of the prevention and control of the low-slow small targets are improved.

Description

Airport low-slow small target prevention and control method based on photoelectric image automatic identification
Technical Field
The invention belongs to the technical field of intelligent low-speed small target prevention and control, and particularly relates to an airport low-speed small target prevention and control method based on automatic photoelectric image recognition.
Background
The low-slow small target refers to an aircraft and a floater which have the characteristics of low altitude, ultra-low altitude flight, slow speed, small volume, difficulty in detection and the like. Mainly comprises light and ultra-light airplanes, light helicopters, gliders, delta wings, power umbrellas, paragliders, hot air balloons, airships, unmanned aerial vehicles, aviation models, aerospace models, airborne balloons, captive balloons and the like. The objects have low cost, simple operation and control, convenient carrying and easy acquisition, have strong lifting sudden property and difficult finding and disposal, are easy to be used as tools for carrying explosive articles, putting in biochemical toxicants and disseminating leaflets, and seriously threaten the safety guarantee work of major activities and key areas.
Bird strike refers to an accident caused by collision of an airplane running in the air or on the ground at a high speed with birds in the air. During the collision process, the birds die and the limbs are split because the great change of the motion state of the birds exceeds the bearing degree of the bodies of the birds, so that the bird collision is more similar to soft collision. Bird strikes have the characteristics of paroxysmal and variability, flight accidents caused by bird strikes not only bring economic loss, but also bring greater life safety hidden dangers, threaten the personal safety of passengers and aircrews all the time, damage to airplane parts is caused by slight bird strikes, and damage to people and death are caused by serious bird strikes.
Traditional airport airspace detection usually adopts radar detection, optical detection, radio signal detection and other methods. However, the radar reflection areas of the low and slow small targets and birds are small, and the Doppler effect caused by the slow flying speed is not obvious, so that target misjudgment is easily caused. In addition, the optical detection mainly comprises an infrared automatic search technology, an image processing technology and a high-precision turntable control technology, but has the defects of low image acquisition and processing speed, low scanning efficiency on a large airspace, incapability of accurately calculating the target position and the like. The existing low-slow bird and bird detection is mature day by day, including American Mellin radar system, Canadian eagle radar system, Beijing Zhongkou Robin radar system, Wuhan leading navigation airport bird detection radar system, and the like, and part of the radar system realizes detection and driving-away linkage, but a classification prevention and control strategy and a method are not provided.
Disclosure of Invention
The invention aims to provide a method for preventing and controlling low-slow small targets of an airport, which can overcome the defects of the traditional manual monitoring airspace and the limitation of a single control strategy and improve the efficiency and the precision of preventing and controlling the low-slow small targets.
The technical solution for realizing the purpose of the invention is as follows: a method for preventing and controlling low and slow small targets of an airport based on photoelectric image automatic identification comprises the following steps:
step 1, acquiring a plurality of images of each low-slow small target from a network, and carrying out field actual measurement to acquire a plurality of images of each low-slow small target;
step 2, extracting the characteristics of each image obtained in the step 1, and classifying the target types corresponding to the images according to the extracted image characteristics to obtain a low-slow small target classifier;
step 3, preprocessing the image of the target to be prevented and controlled, extracting image characteristics, and then inputting the characteristics into the classifier to acquire the type of the target to be prevented and controlled;
and 4, determining the position of the target to be prevented and controlled, and driving corresponding prevention and control equipment to work by combining the type of the target to be prevented and controlled obtained in the step 3.
Compared with the prior art, the invention has the following remarkable advantages: 1) different prevention and control devices are driven according to different types of low and slow small targets, so that the automation level of the prevention and control of the low and slow small targets is improved; 2) by the method for extracting the image features, the automatic discrimination and prevention and control of the target type are realized, the linkage integration of detection and prevention and control is realized, and the labor cost can be reduced; 3) the training set is expanded through a web crawler technology, more training images can be obtained, and therefore the precision of the classifier model is improved; 4) by carrying out a series of preprocessing such as denoising, smoothing and background segmentation on the target image, the influence of noise on the result is reduced, and the accuracy of prevention and control is improved; 5) and the prevention and control equipment is driven to transmit to the future position of the target by predicting the position of the target at the next moment, so that the hit rate is improved.
The present invention is described in further detail below with reference to the attached drawing figures.
Drawings
FIG. 1 is a flow chart of the airport low-slow small target prevention and control method based on photoelectric image automatic identification.
Fig. 2 is a gray scale diagram of an image of a target to be controlled according to an embodiment of the present invention.
Fig. 3 is a filtered image of fig. 2 according to an embodiment of the present invention.
FIG. 4 is a diagram illustrating a result of the object type determination according to an embodiment of the present invention.
Fig. 5 is a schematic diagram of the spatial coordinates established in the embodiment of the present invention.
FIG. 6 is a flowchart illustrating the prevention and control process after detecting a target according to the present invention.
Detailed Description
With reference to fig. 1, the method for preventing and controlling the low, slow and small airport targets based on automatic photoelectric image recognition of the present invention comprises the following steps:
step 1, acquiring a plurality of images of each low-slow small target from a network, and carrying out field actual measurement to acquire a plurality of images of each low-slow small target;
step 2, extracting the characteristics of each image obtained in the step 1, and classifying the target types corresponding to the images according to the extracted image characteristics to obtain a low-slow small target classifier;
step 3, preprocessing the image of the target to be prevented and controlled, extracting image characteristics, and then inputting the characteristics into the classifier to acquire the type of the target to be prevented and controlled;
and 4, determining the position of the target to be prevented and controlled, and driving corresponding prevention and control equipment to work by combining the type of the target to be prevented and controlled obtained in the step 3.
Further preferably, the low and slow small targets include drones, balloons, birds.
Further preferably, in step 1, a plurality of images of each low-slow small target are acquired from the network, specifically: and crawling a plurality of images of each low-slow small target from the network through a web crawler.
Further preferably, in step 1, a plurality of images of each low-slow small target are acquired from the network, specifically: and (4) crawling a plurality of images of each low-speed small target from the network by using the name of the low-speed small target as a keyword through a Python crawler.
Further preferably, in step 2, feature extraction is performed on each image obtained in step 1, specifically: and extracting image features by adopting a Haar feature extraction method.
Further, before the feature extraction in step 2, the method further includes preprocessing each image obtained in step 1, where the preprocessing includes: denoising, smoothing and background segmentation.
Further preferably, in step 2, the target types corresponding to the images are classified according to the extracted image features, and specifically, a support vector machine SVM is used for classification.
Further, the step 4 of determining the position of the target to be prevented and controlled, and driving the corresponding prevention and control device to work by combining the type of the target to be prevented and controlled obtained in the step 3, specifically:
step 4-1, establishing a space coordinate system for an airport area, wherein the airport area comprises a runway and areas along two sides of the runway; placing photoelectric detection equipment at any position of two side areas of an airport runway, taking the position as an original point O, taking the direction along the airport runway as a y axis, taking the horizontal direction vertical to the y axis as an x axis, and taking the direction vertical to the xOy plane and pointing to the sky as a z axis; thereby determining the position of the current position of the target to be controlled in the space coordinate system;
step 4-2, determining corresponding prevention and control equipment according to the type of the target to be prevented and controlled, wherein the corresponding relation between the type of the target to be prevented and controlled and the prevention and control equipment is customized by a user;
4-3, determining the position of the prevention and control equipment corresponding to the target to be prevented and controlled in the space coordinate system;
4-4, acquiring the current position and the speed of the target to be prevented and controlled by the photoelectric detection equipment, and estimating the position of the target to be prevented and controlled in the space coordinate system at the next moment;
and 4-5, moving the original point O of the space coordinate system to the position of the prevention and control equipment to obtain a new space coordinate system, converting the position of the target to be prevented and controlled in the original space coordinate system at the next moment into the new space coordinate system, then converting the position into a spherical coordinate system with the prevention and control equipment as the original point, and interfering the target to be prevented and controlled by the equipment to be prevented and controlled according to the converted position.
Further preferably, the step 4-2 defines, by a user, a correspondence between the type of the target to be controlled and the control device, specifically as follows: if the type of the target to be prevented and controlled is an unmanned aerial vehicle, the corresponding prevention and control equipment is unmanned aerial vehicle interference equipment; if the type of the target to be prevented and controlled is a balloon, the corresponding prevention and control equipment is laser equipment; if the type of the target to be prevented and controlled is birds, the corresponding prevention and control equipment is bird repelling equipment.
Further preferably, in step 4-4, the position of the target to be controlled at the next moment in the spatial coordinate system is estimated, and a least square filtering algorithm is specifically adopted.
The present invention will be described in further detail with reference to examples.
Examples
With reference to fig. 1, the airport low-slow small target prevention and control method based on photoelectric image automatic identification of the present invention includes the following contents:
1. and acquiring a plurality of images of each low-speed small target from the network through a web crawler, and carrying out actual measurement on the images on site to acquire a plurality of images of each low-speed small target.
2. And (3) performing feature extraction on each image obtained in the step (1) by adopting a Haar feature extraction method, and classifying the target types corresponding to the images according to the extracted image features to obtain a low-slow small target classifier.
3. In this embodiment, the target image to be controlled is set to 128 × 128 pixels, and then the target image is converted into a gray-scale image as shown in fig. 2, and then the gray-scale image is subjected to median filtering, and the filtering result is shown in fig. 3.
4. The method includes preprocessing an image of a target to be prevented and controlled, extracting image features, and then inputting the features into a type of the target to be prevented and controlled obtained by a low-slow small target classifier, wherein the type of the target to be prevented and controlled obtained in the embodiment is an unmanned aerial vehicle, as shown in fig. 4.
5. With reference to fig. 5, a spatial coordinate system is established for an airport area, wherein the airport area includes runways and areas along both sides of the runways; placing photoelectric detection equipment at any position of two side areas of an airport runway, taking the position as an original point O, taking the direction along the airport runway as a y axis, taking the horizontal direction vertical to the y axis as an x axis, and taking the direction vertical to an xOy plane and pointing to the sky as a z axis; thereby determining the position of the current position of the target to be controlled in the space coordinate system.
6. With reference to fig. 6, when different target types are detected, different prevention and control devices need to be selected, and the prevention and control device corresponding to the type of the target to be prevented and controlled in this embodiment is an unmanned aerial vehicle jamming device.
7. Obtaining interference equipment of unmanned aerial vehicle in space coordinate systemThe position of the interference equipment of the unmanned aerial vehicle in the embodiment is [ 010000 ]] T 。
8. The photoelectric detection equipment acquires the current position of the target to be prevented and controlled [ 100200050] T And speed [ 515-8 ]] T m/s, thereby estimating the position of the target to be controlled in the space coordinate system at the next moment in time as [ 105201542 ]] T 。
9. Translating the coordinate system to enable the origin of the coordinate to be the position of the interference equipment of the unmanned aerial vehicle, defining the step length of the running clock of the equipment to be 1 second, and setting the position of the future point of the target in the new coordinate system to be [ 105101542 ]] T Converting the position to a spherical coordinate system as an azimuth angle
Figure BDA0002025063720000051
High low angle
Figure BDA0002025063720000052
The unmanned aerial vehicle interference equipment points to the direction to transmit interference.
The method and the device have the advantages that the characteristics of the target images are acquired on the basis of the acquired multiple images to train the target type classifier, then the target type is judged according to the actually detected target images, the position of the target under the detection coordinate system and the position of the future point are converted into the coordinate system of the prevention and control equipment according to the established space rectangular coordinate system, the emission direction of the prevention and control equipment is adjusted according to the position, the errors of manual operation are reduced, the integration of detection and prevention and control is realized, and the labor cost is greatly reduced.

Claims (9)

1. A method for preventing and controlling low and slow small targets of an airport based on photoelectric image automatic identification is characterized by comprising the following steps:
step 1, acquiring a plurality of images of each low-slow small target from a network, and carrying out field actual measurement to acquire a plurality of images of each low-slow small target;
step 2, extracting the characteristics of each image obtained in the step 1, and classifying the target types corresponding to the images according to the extracted image characteristics to obtain a low-slow small target classifier;
step 3, preprocessing the image of the target to be prevented and controlled, extracting image characteristics, and then inputting the characteristics into the classifier to acquire the type of the target to be prevented and controlled;
step 4, determining the position of the target to be prevented and controlled, and driving corresponding prevention and control equipment to work by combining the type of the target to be prevented and controlled obtained in the step 3; the method specifically comprises the following steps:
step 4-1, establishing a space coordinate system for an airport area, wherein the airport area comprises a runway and areas along two sides of the runway; placing photoelectric detection equipment at any position of two side areas of an airport runway, taking the position as an original point O, taking the direction along the airport runway as a y axis, taking the horizontal direction vertical to the y axis as an x axis, and taking the direction vertical to the xOy plane and pointing to the sky as a z axis; thereby determining the position of the current position of the target to be controlled in the space coordinate system;
step 4-2, determining corresponding prevention and control equipment according to the type of the target to be prevented and controlled, wherein the corresponding relation between the type of the target to be prevented and controlled and the prevention and control equipment is customized by a user;
4-3, determining the position of the prevention and control equipment corresponding to the target to be prevented and controlled in the space coordinate system;
4-4, acquiring the current position and the speed of the target to be prevented and controlled by the photoelectric detection equipment, and estimating the position of the target to be prevented and controlled in the space coordinate system at the next moment;
and 4-5, moving the original point O of the space coordinate system to the position of the prevention and control equipment to obtain a new space coordinate system, converting the position of the target to be prevented and controlled in the original space coordinate system at the next moment into the new space coordinate system, then converting the position into a spherical coordinate system with the prevention and control equipment as the original point, and interfering the target to be prevented and controlled by the equipment to be prevented and controlled according to the converted position.
2. The airport low and slow small target prevention and control method based on automatic photoelectric image recognition is characterized in that the low and slow small targets comprise unmanned planes, balloons and birds.
3. The airport low-slow small target prevention and control method based on photoelectric image automatic identification as claimed in claim 1 or 2, wherein step 1 is to obtain a plurality of images of each low-slow small target from the network, specifically: and crawling a plurality of images of each low-slow small target from the network through a web crawler.
4. The airport low-slow small target prevention and control method based on photoelectric image automatic identification as claimed in claim 3, wherein step 1 is to obtain a plurality of images of each low-slow small target from the network, specifically: and (4) crawling a plurality of images of each low-slow small target from the network by a Python crawler by taking the name of the low-slow small target as a keyword.
5. The airport low-slow small target prevention and control method based on photoelectric image automatic identification as claimed in claim 4, wherein step 2 performs feature extraction on each image obtained in step 1, specifically: and extracting image features by adopting a Haar feature extraction method.
6. The airport low-slow small target prevention and control method based on automatic photoelectric image recognition as claimed in claim 5, wherein before the feature extraction in step 2, each image obtained in step 1 is preprocessed, and the preprocessing comprises: denoising, smoothing and background segmentation.
7. The airport low-slow small target prevention and control method based on photoelectric image automatic identification as claimed in claim 6, wherein in step 2, the target types corresponding to the images are classified according to the extracted image features, specifically by using a Support Vector Machine (SVM).
8. The airport low-slow small target prevention and control method based on photoelectric image automatic identification as claimed in claim 1, wherein the corresponding relationship between the type of target to be prevented and controlled and the prevention and control device in step 4-2 is customized by a user, specifically: if the type of the target to be prevented and controlled is an unmanned aerial vehicle, the corresponding prevention and control equipment is unmanned aerial vehicle interference equipment; if the type of the target to be prevented and controlled is a balloon, the corresponding prevention and control equipment is laser equipment; if the type of the target to be prevented and controlled is birds, the corresponding prevention and control equipment is bird repelling equipment.
9. The airport low-slow small target prevention and control method based on automatic photoelectric image identification as claimed in claim 1, wherein in step 4-4, the position of the target to be prevented and controlled in the space coordinate system at the next moment is estimated, specifically using a least square filtering algorithm.
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