Airfield runway foreign matter detection radar image target automatic alignment method

Abstract

The invention provides an airport runway foreign matter detection radar image target automatic alignment method, which comprises the following steps of: installing side lamp type runway foreign matter detection equipment at an airport, and selecting punctuation points on an airport runway; driving a radar and a camera of the side lamp type runway foreign matter detection equipment to obtain a radar imaging target position and a camera imaging target position of each punctuation point location; the radar imaging target position and the camera imaging target position form a punctuation position pair, and a plurality of optimal position pairs are screened out from the punctuation position pair; according to the optimal position pair, constructing a radar image target conversion function, and calculating a camera imaging target position; and when the foreign object FOD exists, calculating a camera imaging target position by using a radar image target conversion function, and driving the camera to move to an FOD specified position. According to the method, automatic alignment of the radar image target can be effectively realized, and the target is arranged in the middle of the image, so that the subsequent visual detection capability is improved, and airport workers can conveniently handle FOD events in time.

Description

Technical Field

[0001] This invention relates to an automatic target alignment method for images, and more particularly to an automatic target alignment method for foreign object detection radar images of airport runways. Background Technology

[0002] Foreign Object Debris (FOD) on airport runways is one of the major safety threats facing aircraft today. With the recovery and rapid development of the global civil aviation industry, FOD poses a serious safety threat to aircraft, causing significant economic losses to airports and airlines annually, and also seriously threatening flight safety and personnel safety. Therefore, the International Civil Aviation Organization (ICAO) has developed international standards for airport FOD detection systems, establishing standards applicable to actual airport operations. Currently, some large airports worldwide have introduced FOD detection systems. According to the redundancy coverage installation requirements for detection range, a 3600m runway requires at least 120 edge-lamp detection devices. However, due to differences in internal assembly processes, actual installation and trim errors, and variations in runway slope in different directions, existing technologies using parameter compensation correction struggle to achieve accurate target alignment at various detection ranges and orientations, making it difficult to obtain the ideal effect of the target being centered in the image. Summary of the Invention

[0003] Purpose of the invention: The technical problem to be solved by the present invention is to provide an automatic target alignment method for foreign object detection radar images of airport runways, which addresses the shortcomings of the existing technology.

[0004] To address the aforementioned technical problems, this invention discloses an automatic target alignment method for foreign object detection radar images on airport runways, comprising the following steps:

[0005] Step 1: Install edge-light type runway foreign object detection equipment at the airport and select markers on the airport runway;

[0006] Step 2: Drive the radar of the side-light type runway foreign object detection device to obtain the radar imaging target position of each marker point;

[0007] Step 3: Drive the camera of the side-light type runway foreign object detection device to obtain the camera image target position at each marker point;

[0008] Step 4: Combine the radar imaging target position and the camera imaging target position into punctuation position pairs, and select several optimal position pairs from them;

[0009] Step 5: Based on the optimal position pair, construct the radar image target transformation function and calculate the target position captured by the camera;

[0010] Step 6: When a foreign object (FOD) is present, use the radar image target conversion function to calculate the target position of the camera and drive the camera to move to the designated position of the FOD.

[0011] Furthermore, the step 1 of selecting markers on the airport runway includes:

[0012] Select several points with distinct features and even distribution on the airport runway as punctuation marks.

[0013] Furthermore, step 2, which involves obtaining the radar imaging target position of each marker point, includes:

[0014] Let p be the number of punctuation marks obtained from right to left. Let the first The positions of the punctuation marks on the radar image are: ), denoted as ,in For the first The azimuth coordinates of each punctuation point , For the first Distance coordinates of points, 0 , For the first The signal-to-noise ratio coordinates of each punctuation point ; These are extreme values ​​of azimuth coordinates. It is the effective detection range of the radar. It is the extreme value of the signal-to-noise ratio in radar imaging.

[0015] Furthermore, step 3, obtaining the camera imaging target position for each marker point, includes:

[0016] Let the first The position of each punctuation mark on the camera image is: , recorded as ,in For the first The turntable orientation coordinates of each punctuation point , For the first The distance coordinates of each punctuation point, 0 , For the first The signal-to-noise ratio coordinates of each punctuation point ; It transforms into a directional extremum. For the camera to effectively detect distance, It is the extreme value of the camera's imaging signal-to-noise ratio; among which, the first Distance to each punctuation point in coordinate system The focal length calibration method is as follows:

[0017] At focal length Below, the known dimensions are... The object is placed at a known radar distance At this location, the measured pixel height is Calculate system constants , represented as:

[0018]

[0019] Assuming pure optical zoom The value is constant, depending on the focal length. and the pixel height of the target Calculate distance coordinates , means as follows:

[0020] Furthermore, step 4 involves selecting several optimal points from the acquired punctuation marks, including:

[0021] Step 4-1: Randomly select 3 pairs of non-collinear punctuation marks. Position , and This forms a new coordinate system, in which Construct a target location model to calculate the coordinates of any other point in the new coordinate system, specifically including:

[0022] Step 4-1-1, construct the radar target model, as shown below:

[0023]

[0024]

[0025]

[0026]

[0027]

[0028] in, punctuation The radar imaging target position, For iteration counting, , means as follows:

[0029]

[0030] in, The center of gravity orientation component, For the centroid distance component, For the signal-to-noise ratio component towards the center of gravity;

[0031] , and Let them be unit vectors in each direction, represented as follows:

[0032]

[0033]

[0034]

[0035] in, , , Punctuation marks unit vector , , The azimuth component, , , The unit vectors of punctuation j are respectively , , The distance component, , , The unit vectors of punctuation j are respectively , , The signal-to-noise ratio component;

[0036] , and The coordinates represent punctuation marks. Position in the new coordinate system;

[0037] Step 4-1-2, construct the image target model, as shown below:

[0038]

[0039]

[0040]

[0041]

[0042]

[0043] in, Calculate the position of the camera image for radar imaging point j;

[0044] in remember ,in, The center of gravity orientation component, For the centroid distance component, For the signal-to-noise ratio component towards the center of gravity;

[0045] , , Let them be unit vectors in each direction, represented as follows:

[0046]

[0047]

[0048]

[0049] in, , , Punctuation marks unit vector , , The azimuth component, , , The unit vectors of punctuation j are respectively , , The distance component, , , The unit vectors of punctuation j are respectively , , The signal-to-noise ratio component;

[0050] Step 4-2, based on the constructed target location model, for the remaining... The position of each punctuation mark , According to the target position in radar imaging Calculate the corresponding image target location And calculate the true position of the target imaged by the camera. and distance , means as follows:

[0051]

[0052] Step 4-3, settings As a distance threshold, records that meet the conditions Number of punctuation marks ;

[0053] Step 4-4, repeat Execute steps 4-1 to 4-3 to find the number. The maximum value is recorded as the iteration number at this point. for The optimal point is denoted as , , .

[0054] Furthermore, the construction of the radar image target transformation function and the calculation of the camera-imported target position described in step 5 are as follows:

[0055]

[0056]

[0057]

[0058]

[0059]

[0060] For target coordinates in radar images Calculate the corresponding camera image target coordinates :

[0061]

[0062]

[0063]

[0064] in,

[0065]

[0066]

[0067]

[0068]

[0069]

[0070]

[0071]

[0072]

[0073] .

[0075] Furthermore, in step 6, when FOD exists, the radar image target transformation function is used to calculate the image target position, and the camera is driven to move to the FOD position; based on the known radar target turntable angle... Calculate the target turntable angle in the image , After obtaining the target turntable angle, drive the camera to rotate to the target angle.

[0076] Beneficial effects:

[0077] 1. This invention solves the problem of automatic alignment of foreign object detection (FOD) radar images on airport runways. It involves installing edge-light type FOD detectors at the airport, selecting multiple points with distinct and uniformly distributed features on the runway; driving the radar to operate and acquiring the radar image target position at each point; driving the camera to move to each point and acquiring the camera image target position; then performing turntable calibration on the acquired radar and image target positions; calculating the radar image target transformation function based on the calibration results; when FOD exists, using the radar image target transformation function to calculate the image target position, and driving the camera to move to the FOD position.

[0078] 2. This invention uses a model prediction method to select the optimal calibration parameters, which solves the problem of non-centering of target images at various detection distances and azimuths caused by factors such as differences in the internal assembly process of the detection equipment, actual installation and balancing errors, and differences in the geographical slope of the runway in various directions.

[0079] 3. This invention has been tested on multiple side-light runway foreign object detection devices and can be applied to the entire runway scenario. It can effectively achieve automatic target alignment in radar images, placing the target in the center of the image, which helps to improve subsequent visual detection capabilities and facilitates airport staff to handle FOD incidents in a timely manner, fully verifying the effectiveness of this invention. Attached Figure Description

[0080] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments, and the advantages of the present invention in the above and / or other aspects will become clearer.

[0081] Figure 1 This is a flowchart illustrating the method of an embodiment of this application.

[0082] Figure 2 These are radar target images collected at various locations in the embodiments of this application.

[0083] Figure 3 This refers to the image target imaging acquired at various points in the embodiments of this application.

[0084] Figure 4 This is a schematic diagram of the camera driving to the FOD position in an embodiment of this application. Detailed Implementation

[0085] The edge-light type runway foreign object detection device set in this application consists of radar and camera. It is generally installed on both sides of the runway and has an effective detection range of about 0~90m. It quickly detects and reports FOD targets by scanning the runway with radar. The FOD detection system quickly drives the camera to automatically align with the target according to the location reported by the radar, placing the foreign object in the center of the image, which helps to improve the subsequent visual detection capability and facilitates the timely confirmation and handling of FOD by airport staff.

[0086] This invention discloses an automatic target alignment method for foreign object detection radar images on airport runways, comprising the following steps:

[0087] Step 1: Install edge-light type runway foreign object detection equipment at the airport. Select multiple points on the airport runway that are evenly distributed with distinct characteristics, such as edge lights, ground lights, or place standard samples.

[0088] Step 2 drives the side-lamp detection device radar to operate, acquiring the radar imaging target positions at each point. Let p be the number of points acquired from right to left. The location of the first point on the radar image is: The position of the p-th point is ), denoted as ,in Azimuth coordinates , The distance of the point to coordinate 0 , Point signal-to-noise ratio coordinates . These are extreme values ​​of azimuth coordinates. It is the effective detection range of the radar. It is the extreme value of the signal-to-noise ratio in radar imaging.

[0089] Step 3: Drive the edge-lamp detection device camera to each point, acquire the camera's image target position, and record the position of each point on the camera's image as... , recorded as in The azimuth coordinates of the turntable , The distance of the point to coordinate 0 , Point signal-to-noise ratio coordinates . It transforms into a directional extremum. For the camera to effectively detect distance, This is the extreme value of the camera's signal-to-noise ratio. Here Coordinate calculation requires a focal length-distance calibration, the calibration method is as follows:

[0090] At focal length Below, the known dimensions The object is placed at a known radar distance At that location, the pixel height was measured. This yields a system constant that integrates sensor pixel size and internal conversion parameters. :

[0091]

[0092] Pure optical zoom The value is constant for any camera distance. and any focal length Target pixel height ,have:

[0093]

[0094] Step 4: From the obtained points, use the model prediction difference method to select 3 pairs of optimal points.

[0095] Randomly select 3 pairs of corresponding non-collinear points , , , in Construct a target location model and conduct k trials. .

[0096] Assuming a radar target model:

[0097]

[0098]

[0099]

[0100]

[0101]

[0102] in ,

[0103] , , Let them be the unit vectors in each direction, denoted as... , . , , These are local coordinates.

[0104] Assume the image target model:

[0105]

[0106]

[0107]

[0108]

[0109]

[0110] in remember ,

[0111] , , Let them be the unit vectors in each direction, denoted as... .

[0112] For the remainder Point , ,calculate Corresponding coordinate system points ,calculate and distance

[0113]

[0114] As a distance threshold, records that meet the conditions Number of points ,repeat Next time, found Remember this moment for The three optimal points are denoted as , , .

[0115] Step 5: Construct a radar image target transformation function using 3 pairs of optimal points, and calculate the target position captured by the camera.

[0116]

[0117]

[0118]

[0119]

[0120]

[0121] For target coordinates in radar images Calculate the corresponding camera image target coordinates :

[0122]

[0123]

[0124]

[0125] in,

[0126]

[0127]

[0128]

[0129]

[0130]

[0131]

[0132]

[0133]

[0134]

[0135] Step 6: When FOD exists, use the radar image target transformation function to calculate the image target position and drive the camera to the FOD position. Based on the known radar target turntable angle... Calculate the target turntable angle in the image , After obtaining the target turntable angle, drive the camera to rotate to the target angle.

[0136] Example:

[0137] This invention discloses an automatic target alignment method for foreign object detection radar images on airport runways, such as... Figure 1 As shown, it includes the following steps:

[0138] Step 1: Install edge-light type runway foreign object detection equipment at the airport. Select multiple points on the airport runway that are evenly distributed with distinct characteristics, such as edge lights, ground lights, or place standard samples.

[0139] Step 2 drives the side-lamp detection device radar to operate, acquiring the radar imaging target positions at each point. Let p be the number of points acquired from right to left. The location of the first point on the radar image is: The position of the p-th point is ), denoted as ,in Azimuth coordinates , The distance of the point to coordinate 0 , Point signal-to-noise ratio coordinates . These are extreme values ​​of azimuth coordinates. It is the effective detection range of the radar. This is the extreme value of the radar imaging signal-to-noise ratio. The acquired radar target image is as follows: Figure 2 As shown.

[0140] The locations of the radar targets collected are shown in Table 1.

[0141] Table 1 Radar Target Location Table

[0142] name coordinate ) ) (159.538498,16.2816,15.447) (20.964149, 19.5072,13.461)

[0143] Step 3: Drive the edge-lamp detection device camera to each point, acquire the camera's image target position, and record the position of each point on the camera's image as... , recorded as in The azimuth coordinates of the turntable , The distance of the point to coordinate 0 , Point signal-to-noise ratio coordinates . It transforms into a directional extremum. For the camera to effectively detect distance, This is the extreme value of the camera's signal-to-noise ratio. Here Coordinate calculation requires a focal length-distance calibration, the calibration method is as follows:

[0144] At focal length Below, the known dimensions The object is placed at a known radar distance At that location, the pixel height was measured. This yields a system constant that integrates sensor pixel size and internal conversion parameters. :

[0145]

[0146] Pure optical zoom The value is constant; in this example... For any camera distance and any focal length Target pixel height ,have:

[0147]

[0148] Acquired image target imaging, such as Figure 3 As shown.

[0149] The target locations in the acquired images are shown in Table 2.

[0150] Table 2 Image Target Location Table

[0151] name coordinate ) ) (199.22,17.36,27.880) (67.7977,20.625,21.375)

[0152] Step 4: From the obtained points, use the model prediction difference method to select 3 pairs of optimal points.

[0153] Randomly select 3 pairs of corresponding non-collinear points , , , in Construct a target location model and conduct k trials. .

[0154] Assuming a radar target model:

[0155]

[0156]

[0157]

[0158]

[0159]

[0160] in ,

[0161] , , Let them be the unit vectors in each direction, denoted as... , . , , These are local coordinates.

[0162] Assume the image target model:

[0163]

[0164]

[0165]

[0166]

[0167]

[0168] in remember ,

[0169] , , Let them be the unit vectors in each direction, denoted as... .

[0170] For the remainder Point , ,calculate Corresponding coordinate system points ,calculate and distance

[0171]

[0172] As a distance threshold, records that meet the conditions Number of points ,repeat Next time, found Remember this moment for The three optimal points are denoted as , , .

[0173] Step 5: Construct a radar image target transformation function using 3 pairs of optimal points, and calculate the target position captured by the camera.

[0174]

[0175]

[0176]

[0177]

[0178]

[0179] For target coordinates in radar images Calculate the corresponding camera image target coordinates :

[0180]

[0181]

[0182]

[0183] in,

[0184]

[0185]

[0186]

[0187]

[0188]

[0189]

[0190]

[0191]

[0192]

[0193] Step 6: When FOD exists, use the radar image target transformation function to calculate the image target position and drive the camera to the FOD position. Based on the known radar target turntable angle... Calculate the target turntable angle in the image , After obtaining the target turntable angle, drive the camera to rotate to the target angle.

[0194] The results of the radar camera drive for the edge-lamp detection equipment are as follows: Figure 4 As shown.

[0195] In its specific implementation, this application provides a computer storage medium and a corresponding data processing unit. The computer storage medium is capable of storing a computer program, which, when executed by the data processing unit, can run the invention's content regarding an automatic target alignment method for foreign object detection radar images on airport runways, as well as some or all of the steps in various embodiments. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0196] Those skilled in the art will clearly understand that the technical solutions in the embodiments of the present invention can be implemented using computer programs and their corresponding general-purpose hardware platforms. Based on this understanding, the technical solutions in the embodiments of the present invention, or the parts that contribute to the prior art, can be embodied in the form of computer programs, i.e., software products. These computer program software products can be stored in a storage medium and include several instructions to cause a device containing a data processing unit (which may be a personal computer, server, microcontroller, MCU, or network device, etc.) to execute the methods described in various embodiments or certain parts of the embodiments of the present invention.

[0197] This invention provides a concept and method for automatic target alignment in radar images used for foreign object detection on airport runways. Many methods and approaches exist for implementing this technical solution; the above description is merely a preferred embodiment of the invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this invention, and these improvements and modifications should also be considered within the scope of protection of this invention. All components not explicitly stated in this embodiment can be implemented using existing technologies.

Claims

1. A method for automatic target alignment in airport runway foreign object detection radar images, characterized in that, Includes the following steps: Step 1: Install edge-light type runway foreign object detection equipment at the airport and select markers on the airport runway; Step 2: Drive the radar of the side-light type runway foreign object detection device to obtain the radar imaging target position of each marker point; Step 3: Drive the camera of the side-light type runway foreign object detection device to obtain the camera image target position at each marker point; Step 4: Combine the radar imaging target position and the camera imaging target position into punctuation position pairs, and select several optimal position pairs from them; Step 5: Based on the optimal position pair, construct the radar image target transformation function and calculate the target position captured by the camera; Step 6: When a foreign object (FOD) is present, use the radar image target conversion function to calculate the target position of the camera and drive the camera to move to the designated position of the FOD.

2. The method for automatic target alignment in an airport runway foreign object detection radar image according to claim 1, characterized in that, Step 1, which involves selecting markers on the airport runway, includes: Select several points with distinct features and even distribution on the airport runway as punctuation marks.

3. The method for automatic target alignment in airport runway foreign object detection radar images according to claim 2, characterized in that, Step 2, which involves obtaining the radar imaging target position of each marker point, includes: Let p be the number of punctuation marks obtained from right to left. Let the first The positions of the punctuation marks on the radar image are: ), denoted as ,in For the first The azimuth coordinates of each punctuation point , For the first Distance coordinates of points, 0 , For the first The signal-to-noise ratio coordinates of each punctuation point ; These are extreme values ​​of azimuth coordinates. It is the effective detection range of the radar. It is the extreme value of the signal-to-noise ratio in radar imaging.

4. The method for automatic target alignment in an airport runway foreign object detection radar image according to claim 3, characterized in that, Step 3, which involves obtaining the camera imaging target position for each marker point, includes: Let the first The position of each punctuation mark on the camera image is: , recorded as ,in For the first The turntable orientation coordinates of each punctuation point , For the first The distance coordinates of each punctuation point, 0 , For the first The signal-to-noise ratio coordinates of each punctuation point ; It transforms into a directional extremum. For the camera to effectively detect distance, It is the extreme value of the camera's imaging signal-to-noise ratio; among which, the first Distance to each punctuation point in coordinate system The focal length calibration method is as follows: At focal length Below, the known dimensions are... The object is placed at a known radar distance At this location, the measured pixel height is Calculate system constants ; Assuming pure optical zoom The value is constant, depending on the focal length. and the pixel height of the target Calculate distance coordinates .

5. The method for automatic target alignment in an airport runway foreign object detection radar image according to claim 4, characterized in that, Calculate system constants The method is as follows: 。 6. The method for automatic target alignment in an airport runway foreign object detection radar image according to claim 5, characterized in that, Calculate distance coordinates The method is as follows: 。 7. The method for automatic target alignment in an airport runway foreign object detection radar image according to claim 6, characterized in that, Step 4 involves selecting several optimal points from the acquired punctuation marks, including: Step 4-1: Randomly select 3 pairs of non-collinear punctuation marks. Position , and This forms a new coordinate system, in which Construct a target location model to calculate the coordinates of any other point in the new coordinate system, specifically including: Step 4-1-1, construct the radar target model, as shown below: ； ； ； ； ； in, punctuation The radar imaging target position, For iteration counting, , means as follows: ； in, The center of gravity orientation component, For the centroid distance component, For the signal-to-noise ratio component towards the center of gravity; , and Let them be unit vectors in each direction, represented as follows: ； ； ； in, , , Punctuation marks unit vector , , The azimuth component, , , The unit vectors of punctuation j are respectively , , The distance component, , , The unit vectors of punctuation j are respectively , , The signal-to-noise ratio component; , and The coordinates represent punctuation marks. Position in the new coordinate system; Step 4-1-2, construct the image target model, as shown below: ； ； ； ； ； in, Calculate the position of the camera image for radar imaging point j; in remember ,in, The center of gravity orientation component, For the centroid distance component, For the signal-to-noise ratio component towards the center of gravity; , , Let them be unit vectors in each direction, represented as follows: ； ； ； in, , , Punctuation marks unit vector , , The azimuth component, , , The unit vectors of punctuation j are respectively , , The distance component, , , The unit vectors of punctuation j are respectively , , The signal-to-noise ratio component; Step 4-2, based on the constructed target location model, for the remaining... The position of each punctuation mark , According to the target position in radar imaging Calculate the corresponding image target location And calculate the true position of the target imaged by the camera. and distance , means as follows: ； Step 4-3, settings As a distance threshold, records that meet the conditions Number of punctuation marks ; Step 4-4, repeat Execute steps 4-1 to 4-3 to find the number. The maximum value is recorded as the iteration number at this point. for The optimal point is denoted as , , ;in, , and This is the number of the optimal point.

8. The method for automatic target alignment in an airport runway foreign object detection radar image according to claim 7, characterized in that, Calculate the true position of the target imaged by the camera. and distance , means as follows: ； in, This indicates that the distance between two points is calculated.

9. The method for automatic target alignment in an airport runway foreign object detection radar image according to claim 8, characterized in that, The construction of the radar image target transformation function and the calculation of the target position in camera imaging described in step 5 are as follows: ； ； ； ； ； For target coordinates in radar images Calculate the corresponding camera image target coordinates : ； ； ； in, ； ； ； ； ； ； ； ； 。 10. The method for automatic target alignment in an airport runway foreign object detection radar image according to claim 9, characterized in that, Step 6 involves moving the camera to the designated FOD position, including: When FOD (Foreign Object Dependence) is present, the target position in the image is calculated using the radar image target transformation function, and the camera is driven to move to the FOD position; based on the known radar target turntable angle... Calculate the target turntable angle in the image. , After obtaining the target turntable angle, drive the camera to rotate to the target angle.

Images