CN105335688B - A kind of aircraft model recognition methods of view-based access control model image - Google Patents
A kind of aircraft model recognition methods of view-based access control model image Download PDFInfo
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Abstract
A kind of aircraft model recognition methods of view-based access control model image, including：Foreground target mask is obtained using frame difference method extraction moving target edge；It is in circular using aeroengine and almost non-reflective characteristic, extracts position and the size of engine；The straight line where fitting wing is converted using Hough line, will be less than at longitudinal gradient significant decrease of the moving target edge extent and is used as tip location；It determines cabin left and right edges position, both wings cusp height mean value is taken to do horizontal linear, by the straight line for being less than the moving target edge extent and the left and right marginal position as cabin at cabin side transverse gradients wave crest；Using the engine radius of aircraft to be identified as scaling factor, calculate separately two engine spacing of the aircraft to be identified, the ratio of wing span, cabin width and the engine radius, with preset standard type data match, type of the corresponding type of maximum matching value as the aircraft to be identified is taken.
Description
Technical field
The present invention relates to a kind of berth Plane location and bootstrap technique, it is especially a kind of for aircraft direct picture based on
The aircraft model recognition methods of visual pattern.
Background technology
Characteristic area extraction in visual pattern is widely used in the tasks such as industrial detection, camera calibration, target detection.
For more complex figure, the target detection technique based on machine learning is needed, it is complete to give the power to make decision of decision to computer
At for such technology still in laboratory stage, reliability is unable to get guarantee.But to some simple applications, such as to simple
Shape, the target detection of solid color, it is already possible to be used in industrialization demand.
Simple target detection method is divided into two kinds of region detection method and Edge Detection Using.Region detection utilizes target to be detected
Certain color or texture on characteristic, itself and background are distinguished, region is screened by areal shape, last profit
Target region is extracted with information such as the shape in region, size, spatial positions.Border detection, which then focuses on color, to be had
The place of apparent drop, has certain stability under different illumination conditions, utilizes the shape of certain stability boundaris of target
The position of target is determined with relative position relation.Application No. is " 200510016267 ", entitled " airplane berth plane type is known automatically
Not with instruction system " Chinese invention patent disclosed in the recognition methods for berth aircraft, need to for side number into
Row identification, and the related data of binding site and speed detector is identified and indicates that recognition efficiency and accuracy rate are equal
There are certain defects.
Invention content
Technical problem to be solved by the invention is to provide a kind of flying for view-based access control model image for aircraft direct picture
Machine plane type recognition method, rapidly and accurately to identify berth aircraft model.
To achieve the goals above, the present invention provides a kind of aircraft model recognition methods of view-based access control model image, wherein
Include the following steps：
S1, moving target recognition obtain foreground target mask, to establish constraint using frame difference method extraction moving target edge
Condition simultaneously reduces the influence that background is brought；
S2, aeroengine extraction, are in circular using aeroengine and almost non-reflective characteristic, extract the position of engine
It sets and size；
S3, airplane wingtip position detection are converted the straight line where fitting wing using Hough line, will be less than the movement
Tip location is used as at longitudinal gradient significant decrease of object edge range；
S4, cabin width detection determine cabin left and right edges position, and both wings cusp height mean value is taken to do horizontal linear, will not
Straight line more than the moving target edge extent and the left and right margin location as cabin at cabin side transverse gradients wave crest
It sets, cabin width is the distance between described left and right edge；
S5, aircraft model identification, using the engine radius of aircraft to be identified as scaling factor, calculate separately this and wait knowing
Two engine spacing of other aircraft, the ratio of wing span, cabin width and the engine radius, with preset standard machine type data
Match, takes type of the corresponding type of maximum matching value as the aircraft to be identified.
The aircraft model recognition methods of above-mentioned view-based access control model image, wherein use frame difference method extraction moving target edge
Including：
The direct picture of S11, a series of aircrafts to be identified of acquisition, calculating t frames image are absolute with t-1 frame images
Difference calculates the standard deviation of t frame images, using the 1/4 of standard deviation as the threshold value for dividing moving target and noise, obtains movement mesh
Target bianry image, calculation formula are as follows：
Wherein,For the gray value at t frame coordinates (x, y), std (I(t)) be t frame gray value of images standard deviation,
Mx,yFor the bianry image of moving target.
The aircraft model recognition methods of above-mentioned view-based access control model image, wherein use frame difference method extraction moving target edge
Further include：
S12, hole in the bianry image of the moving target is closed using closed operation, actionradius is 5 circular shuttering
Expansion 3 times corrodes 2 times and obtains modified foreground target mask.
The aircraft model recognition methods of above-mentioned view-based access control model image, wherein aeroengine extraction step S2 includes：
S21, the grey level histogram cumulative distribution for calculating the lower visual pattern of modified foreground target mask covering, record
1% and 99% corresponding gray level in lower cumulative distribution, most dark/most bright gray level as image；
S22, pole black region and other regions are divided the image into using separation threshold value, the separation threshold value is described extremely black
Region ratio shared in the foreground target mask；
S23, class loop truss is carried out to the pole black region using round decision threshold, extracts all pole black regions
All outer boundaries calculate its barycentric coodinates to each boundary, and the calculation formula of center of gravity is：
Wherein,
The all pixels point edge { x, y } for enumerating current region boundary, calculates it at a distance from center of gravity, and constantly updates most
Greatly/minimum range then judges the pole black region to be non-once maximum distance divided by minimum range are more than the circle decision threshold
Circle, into the judgement of next pole black region；
S24, the detection of pairs of engine, screen the class entelechy black region of judgement, it is assumed that detect M class circle region,
Generate a M*M upper triangular matrix S, S in each element calculation formula be：
S (i, j)=abs (Wi-Wj-Tij)*abs(Hi-Hj)*abs(Ri-Rj)
Tij=3* (Ri+Rj)
Wherein WiIndicate the abscissa at i-th of pole black region center, HiIndicate the ordinate at i-th of pole black region center, Ri
Indicate that i-th of extremely black class justifies the radius in region, TijFor the minimum spacing of two engines, the subscript i and j of a minimum element in S
For the pairs of engine detected.
The aircraft model recognition methods of above-mentioned view-based access control model image, wherein aeroengine extraction step S2 further includes：
S25, it detects again, if step S22-S24 can not find engine, threshold value will be separated and round decision threshold is distinguished
Expand a grade, repeats step S22-S24 and detected again.
The aircraft model recognition methods of above-mentioned view-based access control model image, wherein the step S3 includes：
S31, the longitudinal gradient G calculated in area-of-interest take 1.5 engine height above engine by machine type data library,
The region of the width of 8 engine diameters is as area-of-interest, gradient calculation formula：
Gy(x, y)=2*I (x, y)-I (x, y-1)-I (x, y+1)
Wherein Gy(x, y) is longitudinal gradient at (x, y) coordinate, and I (x, y) is corresponding gray value at (x, y) coordinate；
S32, straight line where point slope form fitting wing is used；
S33, the gradient that wing line correspondence passes through is scanned by lateral wings point direction in aircraft, calculates the total flat of process on the way
The average gradient of equal gradient and nearest 5 pixels scans position if the gradient of nearest 5 pixels is less than the 1/3 of overall average gradient
It sets and has exceeded tip location, and scan position is moved back into 5 pixels as the tip location of current straight line.
The aircraft model recognition methods of above-mentioned view-based access control model image, wherein the step S3 further includes：
Signal lamp is had in S34, evening images, at airplane wingtip to light, if scanning element is apparent highlighted continuously across 2
Pixel, then the high luminance pixels position is tip location.
The aircraft model recognition methods of above-mentioned view-based access control model image, wherein the step S3 further includes：
If the wing tip of S35, aircraft side are blocked by connecting bridge, the symmetric relation of wing is utilized, is connected with two engine cores
The perpendicular bisector of line is fictionalized a wing and is calculated its correlation and join in the aircraft as line of symmetry by the side that connecting bridge blocks
Number.
The aircraft model recognition methods of above-mentioned view-based access control model image, wherein the step S4 includes：
S41, two engine cores are taken, are highly the region of 7 engine radiuses as area-of-interest, calculate area-of-interest
Interior horizontal gradient Gx, formula is as follows：
Gx(x, y)=2*I (x, y)-I (x-1, y)-I (x+1, y)
Wherein, Gx(x, y) is the horizontal gradient at (x, y) coordinate, and I (x, y) is corresponding gray value at (x, y) coordinate；
G is counted in S42, the moving target mask described in step S1xThe histogram of (x, y), by GxMaximum 30% gradient portion
It is divided into 1, rest part is set to 0, and forms a width binary map；
Area of all 1 pixel values connection institute at region is counted, the area filter that area is less than to 50 falls；
And closed operation is carried out to ensure that the edge of cabin fully comes together to the binary map for getting rid of noise；
S43, in the wing tip average height that step S3 is extracted, by the inside scanning element point in both sides, when pixel is in institute
It is 1 to state the pixel value in moving target mask and in the binary map, stops scanning, using two points scanning as cabin
Left and right edge.
The aircraft model recognition methods of above-mentioned view-based access control model image, wherein the step S5 includes：
S51, engine radius, engine spacing, span-width and the cabin width information for storing all target types；
S52, using the aeroengine extraction, tip location detection and cabin width detection as a result, obtaining engine
Mean radius and engine spacing, two wing tip spacing and cabin width, the engine spacing, wing tip spacing, cabin is wide
Degree is respectively divided by engine mean radius, generation three-dimensional feature vector F are compared with the information stored in step 51, compares public
Formula is：
Wherein, StFor the similarity of aircraft in type t and image, FiFor the ith feature extracted from image,For
The ith feature of type t in database, the maximum type of similarity are the type identified.
The technical effects of the invention are that：
The present invention extracts the letters such as engine radius, engine spacing, wing length, cabin width on the direct picture of aircraft
Breath, is compared with the corresponding information to prestore in database, the final type for determining captured aircraft.Make full use of aircraft each
Relativeness between component, and the position of component is constrained by the moving target recognition based on frame difference method, simply, have
Effect, the relevant information that aircraft is steadily obtained from image, to accomplish accurately to identify.
Below in conjunction with the drawings and specific embodiments, the present invention will be described in detail, but not as a limitation of the invention.
Description of the drawings
Fig. 1 is the flow chart of one embodiment of the invention；
Fig. 2 is frame difference method design sketch when not doing any processing of one embodiment of the invention；
Fig. 3 is to use the frame difference method design sketch after Morphological scale-space to Fig. 2；
Fig. 4 is the aeroengine overhaul flow chart of one embodiment of the invention；
Fig. 5 A are the grey level histogram under the night condition of one embodiment of the invention；
Fig. 5 B are the grey level histogram under the conditions of the morning of one embodiment of the invention；
Fig. 6 is the pole black region extraction effect figure of one embodiment of the invention；
Fig. 7 is the engine boundary extracted and left side aircraft wing schematic shapes of one embodiment of the invention.
Wherein, reference numeral
S1-S5 steps
Specific implementation mode
The structural principle and operation principle of the present invention are described in detail below in conjunction with the accompanying drawings：
It is the flow chart of one embodiment of the invention referring to Fig. 1, Fig. 1.The aircraft model of the view-based access control model image of the present invention is known
Other method, includes the following steps：
Step S1, moving target recognition obtains foreground target mask, to establish using frame difference method extraction moving target edge
Constraints simultaneously reduces the influence that background is brought；
Step S2, aeroengine detects, and is in circular using aeroengine and almost non-reflective characteristic, detects engine
Position and size；
Step S3, airplane wingtip position detection is converted the straight line where fitting wing using Hough line, will be less than described
Tip location is used as at longitudinal gradient significant decrease of moving target edge extent；
Step S4, cabin width detection determines cabin left and right edges position, and both wings cusp height mean value is taken to do horizontal linear,
By the straight line for being less than the moving target edge extent and the left and right edge as cabin at cabin side transverse gradients wave crest
Position, cabin width are the distance between described left and right edge；
Step S5, aircraft model identifies, using the engine radius of aircraft to be identified as scaling factor, calculates separately this
Two engine spacing of aircraft to be identified, the ratio of wing span, cabin width and the engine radius, with preset standard type
Data match takes type of the corresponding type of maximum matching value as the aircraft to be identified.
Wherein, include using frame difference method extraction moving target edge：
Step S11, it acquires a series of direct picture of aircrafts to be identified by photographic device, calculates t frames image and the
The absolute difference (i.e. absolute value of the difference) of t-1 frame images calculates the standard deviation of t frame images, is transported using the 1/4 of standard deviation as segmentation
The threshold value of moving-target and noise, obtains the bianry image of moving target, and calculation formula is as follows：
Wherein,For the gray value at t frame coordinates (x, y), std (I(t)) be t frame gray value of images standard deviation,
Mx,yFor the bianry image of moving target.
The M obtained by step S11x,yAlthough having filtered out noise, a part of useful information is also lost, by image
On see as with a large amount of holes white area (referring to Fig. 2).These can be closed using the closed operation on morphological image
Hole, closed operation are considered as the once expansion to image white region and once to the corrosion of white area.Therefore, this implementation
In example, further include using frame difference method extraction moving target edge：
Step S12, the hole in the bianry image of the moving target is closed using closed operation, actionradius is the circle of R
Template expands M times, and corrosion n times obtain modified foreground target mask, and preferably R is 5, M 3, N 2.The modified foreground target
Mask is a secondary foreground target mask slightly widened, hole is less.Radius is excessive, expansion number can excessively cause mask to lose
Original shape is gone, it is very few inside to be caused multiple hole occurred.Corrode number less than expansion number, to ensure obtained mask
Target to be detected (i.e. aircraft, referring to Fig. 3) is covered all as far as possible.
It is the aeroengine overhaul flow chart of one embodiment of the invention referring to Fig. 4, Fig. 4.In the present embodiment, aeroengine carries
The step S2 is taken to include：
Step S21, the grey level histogram cumulative distribution of the visual pattern under the modified foreground target mask covering is calculated,
1% and 99% corresponding gray level in cumulative distribution is recorded, most dark/most bright gray level as image is (referring to Fig. 5 A, 5B, figure
5A is the grey level histogram under the night condition of one embodiment of the invention, under the conditions of Fig. 5 B is the mornings of one embodiment of the invention
Grey level histogram), if most bright gray level is less than 60, judge image for evening images.
Step S22, pole black region and other brighter areas are divided the image into using a fixed separation threshold value, it is described
The physical significance for separating threshold value is pole black region ratio shared in the foreground target mask (aircraft front shape)
(being the pole black region extraction effect figure of one embodiment of the invention referring to Fig. 6, Fig. 6)；In evening images, background is changed into black
Threshold value raising 0.4 is detected by color at this time.
Step S23, class loop truss is carried out to the pole black region using round decision threshold, extracts all pole black areas
All outer boundaries in domain calculate its barycentric coodinates to each boundaryThe calculation formula of center of gravity is：
0,1 calculating is wherein taken respectively by j and i to following formula,
The all pixels point edge { x, y } for enumerating current region boundary, calculates it at a distance from center of gravity, and constantly updates most
Greatly/minimum range is then sentenced once maximum distance divided by minimum range are more than the circle decision threshold (preset value is preferably 1.2)
The disconnected pole black region is not rounded, is directly entered the judgement of next pole black region；
Step S24, pairs of engine detection, is screened the class entelechy black region of judgement, is always gone out in pairs using engine
(part type is 3 engines to existing characteristic, ignores the third engine at empennage at this time, only detects two hung at wing and draws
Hold up) class circle region is screened.Assuming that detecting M class circle region, generate each in the upper triangular matrix S, S of a M*M
Element calculation formula is：
S (i, j)=abs (Wi-Wj-Tij)*abs(Hi-Hj)*abs(Ri-Rj)
Tij=3* (Ri+Rj)
Wherein, S (i, j) indicates that the i-th row of upper triangular matrix, the element of jth row, abs expressions remove absolute value, Wi、WjIt indicates
The abscissa at the i-th, j pole black region center, Hi、HjIndicate the ordinate at i-th, j pole black region center, Ri、RjExpression i-th,
J extremely black classes justify the radius in region, TijFor the minimum spacing of two engines, it is defaulted as 3 engine diameters, which should be with waiting for
Different numerical value is arranged in the difference of berth aircraft model, and absolute value is removed in abs expressions.Take an element minimum in S subscript i and
J is the pairs of engine detected.
Wherein, aeroengine extraction step S2 may also include：
Step S25, it detects again, if step S22-S24 can not find engine, then it is assumed that parameter current may be slightly tight
It is severe, two parameters are expanded into a grade respectively at this time, i.e., will separate threshold value and round decision threshold expands a grade respectively,
Step S22-S24 is repeated to be detected again.Widened number is no more than 2 times.
Referring to Fig. 7, Fig. 7 is that the engine boundary extracted of one embodiment of the invention and left side aircraft wing shape are illustrated
Figure.Wherein, the step S3 includes：
Step S31, longitudinal gradient G in area-of-interest is calculated, by machine type data library, takes 1.5 engines above engine
Highly, as area-of-interest, this is highly common Boeing-737 and Air Passenger with width in the region of the width of 8 engine diameters
Parameter used in A320 passenger planes, other types should be according to the different height of the parameter setting in machine type data library and width models
It encloses.Gradient calculation formula is：
Gy(x, y)=2*I (x, y)-I (x, y-1)-I (x, y+1)
Wherein Gy(x, y) be (x, y) coordinate at longitudinal gradient, I (x, y), I (x, y-1), I (x, y+1) be respectively (x,
Y), corresponding gray value at (x, y-1) and (x, y+1) coordinate；
Step S32, using straight line where point slope form fitting wing, all pixels of the area-of-interest rightmost side, mistake are enumerated
The every 1 ° of slope as straight line of each 0~15 ° of pixel, all straight lines are drawn under black background using antialiasing algorithm.Assuming that
There are 50 pixels in the rightmost side, and 0~15 ° shares 16 angles, then needs to draw 50*16=800 straight line in total.
If the straight line image corresponding to i-th straight line is Li, calculate the votes V of i-th straight linei：
Wherein Li(x, y) is straight line image L at (x, y) coordinateiGray value, Gy(x, y) is the longitudinal direction at (x, y) coordinate
Gradient, x and y take all over area-of-interest all the points.Then ViThe corresponding straight line of maximum value be straight line where wing.Note that
The position of scanning element is no more than moving target edge described in step S1, to avoid the high gradient value interference in complex background.
Step S33, by the gradient that lateral wings point direction scanning wing line correspondence passes through in aircraft, process is calculated on the way
The average gradient of overall average gradient and nearest 5 pixels is swept if the gradient of nearest 5 pixels is less than the 1/3 of overall average gradient
It retouches position and has exceeded tip location, and scan position is moved back into 5 pixels as the tip location of current straight line.
In the present embodiment, the step S3 may also include：
Step S34, in evening images, there is signal lamp to light at airplane wingtip, determine whether night using S21 the methods
Late image.In evening images, if scanning element is continuously across 2 apparent high luminance pixels, (gray value of high luminance pixels is more than it
2 times of the average gray value of preceding scanning element), then V in the ballot value described in S32iIn addition 10000, with other not through excessively high
The straight line of bright pixel distinguishes.Evening images are not necessarily to calculate tip location by mode described in S33, can directly enable the high luminance pixels institute
It is set to tip location in place.
The step S3 may also include：
If step S35, the wing tip of aircraft side is blocked by connecting bridge, the symmetric relation of wing is utilized, in two engines
The perpendicular bisector of heart line fictionalizes a wing by the side that connecting bridge blocks in the aircraft and calculates its correlation as line of symmetry
Parameter simultaneously provides parameter for S4 processes.The wing shapes extracted are as shown in Figure 5.
In the present embodiment, the step S4 includes：
Step S41, it takes two engine cores, is highly the region of 7 engine radiuses as area-of-interest, calculate interested
Horizontal gradient G in regionx, formula is as follows：
Gx(x, y)=2*I (x, y)-I (x-1, y)-I (x+1, y)
Wherein, Gx(x, y) be (x, y) coordinate at horizontal gradient, I (x, y), I (x-1, y), I (x+1, y) be (x, y),
Corresponding gray value at (x-1, y) and (x+1, y) coordinate；
Step S42, G is counted in the moving target mask described in step S1xThe histogram of (x, y), by GxMaximum 30% ladder
Degree part is set as 1, and rest part is set to 0, and forms a width binary map；
Area of all 1 pixel values connection institute at region is counted, the area filter that area is less than to 50 falls；
And it is similar to step S12, closed operation is carried out to ensure to the binary map for getting rid of noise using the rectangle template of 5*5
The edge of cabin fully comes together；
Step S43, in the wing tip average height that step S3 is extracted, by the inside scanning element point in both sides, work as pixel
Pixel value in the moving target mask and in the binary map described in step S42 is 1, stops scanning, with scan two
Left and right edge of a point as cabin.Cabin width is the distance between 2 points.
In the present embodiment, the step S5 includes：
Step S51, it is wide that the engine radiuses of all target types, engine spacing, the span are stored in information processing centre
Degree and cabin width information；
Step S52, use aeroengine detection described in step S2, S3 and S4, tip location detection and cabin wide respectively
It spends detecting as a result, obtaining mean radius and engine spacing, the two wing tip spacing and cabin width of engine, draws described
It holds up spacing, wing tip spacing, cabin width respectively divided by engine mean radius, generation three-dimensional feature vector F is stored with step 51
Information be compared, compare formula be：
Wherein, StFor the similarity of aircraft in type t and image, FiFor the ith feature extracted from image,For
The ith feature of type t, S in databasetThe maximum type of similarity is the type identified.
The method of the present invention detecting and alarm, wing, cabin in aircraft direct picture passes through the parameter and number that detected
According to the aircraft model data in library compared to pair, the type of the aircraft in image is finally determined.
Certainly, the present invention can also have other various embodiments, without deviating from the spirit and substance of the present invention, ripe
It knows those skilled in the art and makes various corresponding change and deformations, but these corresponding changes and change in accordance with the present invention
Shape should all belong to the protection domain of appended claims of the invention.
Claims (10)
1. a kind of aircraft model recognition methods of view-based access control model image, which is characterized in that include the following steps：
S1, moving target recognition obtain foreground target mask, to establish constraints using frame difference method extraction moving target edge
And reduce the influence that background is brought；
S2, aeroengine extraction, using aeroengine in circular and almost non-reflective characteristic, extract engine position and
Size；
S3, airplane wingtip position detection are converted the straight line where fitting wing using Hough line, will be less than the moving target
Tip location is used as at longitudinal gradient significant decrease of edge extent；
S4, cabin width detection determine cabin left and right edges position, take both wings cusp height mean value to do horizontal linear, will be less than
The straight line of the moving target edge extent and the left and right marginal position as cabin at cabin side transverse gradients wave crest, machine
Cabin width is the distance between described left and right edge；
It is to be identified winged to calculate separately this using the engine radius of aircraft to be identified as scaling factor for S5, aircraft model identification
Two engine spacing of machine, the ratio of wing span, cabin width and the engine radius, with preset standard machine type data phase
Match, takes type of the corresponding type of maximum matching value as the aircraft to be identified.
2. the aircraft model recognition methods of view-based access control model image as described in claim 1, which is characterized in that carried using frame difference method
The moving target edge is taken to include：
The direct picture of S11, a series of aircrafts to be identified of acquisition, calculate the absolute difference of t frames image and t-1 frame images,
The standard deviation for calculating t frame images obtains moving target using the 1/4 of standard deviation as the threshold value for dividing moving target and noise
Bianry image, calculation formula is as follows：
Wherein,For the gray value at t frame coordinates (x, y), std (I(t)) be t frame gray value of images standard deviation, Mx,yFor
The bianry image of moving target, abs expressions take absolute value.
3. the aircraft model recognition methods of view-based access control model image as claimed in claim 2, which is characterized in that carried using frame difference method
The moving target edge is taken to further include：
S12, hole in the bianry image of the moving target is closed using closed operation, actionradius is the circular shuttering expansion P of R
Secondary, corrosion n times obtain modified foreground target mask.
4. the aircraft model recognition methods of view-based access control model image as claimed in claim 3, which is characterized in that aeroengine extracts
Step S2 includes：
S21, the grey level histogram cumulative distribution for calculating visual pattern under modified foreground target mask covering, are recorded tired
1% and 99% corresponding gray level in cloth is integrated, most dark/most bright gray level as image；
S22, pole black region and other regions are divided the image into using separation threshold value, the separation threshold value is the pole black region
The shared ratio in the foreground target mask；
S23, class loop truss is carried out to the pole black region using round decision threshold, extracts all of all pole black regions
Outer boundary calculates its barycentric coodinates to each boundary, and the calculation formula of center of gravity is：
Wherein, j' and i' are taken into 0 and 1 respectively, calculated
The all pixels point for enumerating presently described pole black region boundary, calculates it at a distance from center of gravity, and constantly update it is maximum/most
Small distance then judges the pole black region to be not rounded once maximum distance divided by minimum range are more than the circle decision threshold, entrance
The judgement of next pole black region；
S24, the detection of pairs of engine, screen the class entelechy black region of judgement, it is assumed that detect M class circle region, generate
Each element calculation formula in the upper triangular matrix S, S of one M*M is：
S (i, j)=abs (Wi-Wj-Tij)*abs(Hi-Hj)*abs(Ri-Rj)
Tij=3* (Ri+Rj)
Wherein S (i, j) indicates that the i-th row of upper triangular matrix, the element of jth row, abs expressions take absolute value, Wi、WjIndicate i-th, j
The abscissa at a pole black region center, Hi、HjIndicate the ordinate at i-th, j pole black region center, Ri、RjIndicate i-th, j class
The radius of entelechy black region, TijFor the minimum spacing of two engines, in S the subscript i and j of a minimum element be detect at
To engine.
5. the aircraft model recognition methods of view-based access control model image as claimed in claim 4, which is characterized in that aeroengine extracts
Step S2 further includes：
S25, it detects again, if step S22-S24 can not find engine, threshold value will be separated and round decision threshold expands respectively
One grade repeats step S22-S24 and is detected again.
6. the aircraft model recognition methods of the view-based access control model image as described in claim 1,2,3 or 5, which is characterized in that described
Step S3 includes：
S31, calculate area-of-interest in longitudinal gradient G, press machine type data library, take engine top 1.5 engine height, 8
The region of the width of engine diameter is as area-of-interest, gradient calculation formula：
Gy(x, y)=2*I (x, y)-I (x, y-1)-I (x, y+1)
Wherein Gy(x, y) is longitudinal gradient at (x, y) coordinate, and I (x, y) is corresponding gray value at (x, y) coordinate；
S32, straight line where point slope form fitting wing is used；
S33, the gradient that wing line correspondence passes through is scanned by lateral wings point direction in aircraft, calculates the overall average ladder of process on the way
The average gradient of degree and nearest 5 pixels, if the gradient of nearest 5 pixels is less than the 1/3 of overall average gradient, scan position is
5 pixels are moved back as the tip location of current straight line through exceeding tip location, and using scan position.
7. the aircraft model recognition methods of view-based access control model image as claimed in claim 6, which is characterized in that the step S3 is also
Including：
Signal lamp is had in S34, evening images, at airplane wingtip to light, if scanning element is continuously across 2 significantly highlighted pictures
Element, then the high luminance pixels position is tip location.
8. the aircraft model recognition methods of view-based access control model image as claimed in claim 6, which is characterized in that the step S3 is also
Including：
If the wing tip of S35, aircraft side are blocked by connecting bridge, the symmetric relation of wing is utilized, with two engine core lines
Perpendicular bisector fictionalizes a wing by the side that connecting bridge blocks in the aircraft and calculates its relevant parameter as line of symmetry.
9. the aircraft model recognition methods of the view-based access control model image as described in claim 1,2,3,5,7 or 8, which is characterized in that
The step S4 includes：
S41, two engine cores are taken, are highly the region of 7 engine radiuses as area-of-interest, are calculated in area-of-interest
Horizontal gradient Gx, formula is as follows：
Gx(x, y)=2*I (x, y)-I (x-1, y)-I (x+1, y)
Wherein, Gx(x, y) is the horizontal gradient at (x, y) coordinate, and I (x, y) is corresponding gray value at (x, y) coordinate；
G is counted in S42, the moving target mask described in step S1xThe histogram of (x, y), by GxMaximum 30% gradient part is set
It is 1, rest part is set to 0, and forms a width binary map；
Area of all 1 pixel values connection institute at region is counted, the area filter that area is less than to 50 falls；
And closed operation is carried out to ensure that the edge of cabin fully comes together to the binary map for getting rid of noise；
S43, in the wing tip average height that step S3 is extracted, by the inside scanning element point in both sides, when pixel is in the fortune
Pixel value in moving-target mask and in the binary map is 1, stops scanning, using two points scanning as cabin
Left and right edge.
10. the aircraft model recognition methods of view-based access control model image as claimed in claim 9, which is characterized in that the step S5
Including：
S51, engine radius, engine spacing, span-width and the cabin width information for storing all target types；
S52, using the aeroengine extraction, tip location detection and cabin width detection as a result, obtaining being averaged for engine
Radius and engine spacing, two wing tip spacing and cabin width, by the engine spacing, wing tip spacing, cabin width point
Not divided by engine mean radius, generation three-dimensional feature vector F are compared with the information stored in step 51, and comparing formula is：
Wherein, StFor the similarity of aircraft in type t and image, FiFor the ith feature extracted from image,For data
The ith feature of type t in library, the maximum type of similarity are the type identified.
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Effective date of registration: 20210615 Address after: 518103 No.9, Fuyuan 2nd Road, Fuyong street, Bao'an District, Shenzhen City, Guangdong Province Patentee after: SHENZHEN CIMC-TIANDA AIRPORT SUPPORT Co.,Ltd. Address before: Four No. four industrial road, Shekou Industrial Zone, Guangdong, Shenzhen 518067, China Patentee before: SHENZHEN CIMC-TIANDA AIRPORT SUPPORT Co.,Ltd. Patentee before: China International Marine Containers (Group) Co.,Ltd. |