CN110532989B - Automatic detection method for offshore targets - Google Patents

Automatic detection method for offshore targets Download PDF

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CN110532989B
CN110532989B CN201910833101.XA CN201910833101A CN110532989B CN 110532989 B CN110532989 B CN 110532989B CN 201910833101 A CN201910833101 A CN 201910833101A CN 110532989 B CN110532989 B CN 110532989B
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frame
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CN110532989A (en
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谭立国
宋审民
于志刚
霍建文
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Harbin Institute of Technology
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    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
    • G06V10/267—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion by performing operations on regions, e.g. growing, shrinking or watersheds
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/10—Terrestrial scenes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/10—Terrestrial scenes
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Abstract

An automatic detection method for an offshore target relates to an automatic detection method for a target. The invention provides an automatic marine target detection method based on space-time analysis of a halftone image stream in a visible light range of an onboard photoelectric system of an unmanned aerial vehicle, and without presetting a hard-coded reference image for target detection. The detection method comprises the following steps: s1, acquiring an offshore target video sequence of an airborne photoelectric system of the unmanned aerial vehicle; s2, constructing a key target model M of the ocean scene O : s3, obtaining a frame target vector of a suspicious target in the first frame image of the video sequence in the S1
Figure DDA0002191359920000011
S4, updating the key target model M O (ii) a S5, obtaining a frame target vector of a suspicious target in the next frame of image of the video sequence in the S1
Figure DDA0002191359920000012
S6, updating the key target model M O : s7, target vector of slave model
Figure DDA0002191359920000013
Found weight value in W max As detected object, W max Is the maximum allowed weight of the target.

Description

Automatic detection method for offshore targets
Technical Field
The invention relates to an automatic target detection method, in particular to an automatic offshore target detection method, and belongs to the technical field of automatic detection.
Background
The use of machine vision techniques to achieve automatic detection of targets is one of the problems that is urgently needed to be solved in the civilian/military field. This problem has not found an explicit general solution at the present stage, but only partially under certain specific conditions. Therefore, automatic target detection techniques are well developed in radar systems and thermal imaging systems for airborne target detection.
However, objects of interest may not be extracted from the background in the infrared band range and active radar may not be used in certain tasks due to noise of the radio channel or privacy requirements of the detection. Furthermore, in many cases it is not possible to preset reference images of all objects of interest, which imposes additional limitations on the automatic object detection system.
Disclosure of Invention
In view of the above disadvantages, the present invention provides an automatic marine target detection method based on the space-time analysis of halftone image stream in the visible light range of the optoelectronic system on board the unmanned aerial vehicle, and without the need of presetting a hard-coded reference image for target detection.
The invention discloses an automatic detection method of an offshore target, which comprises the following steps:
s1, acquiring an offshore target video sequence of an airborne photoelectric system of the unmanned aerial vehicle;
s2, constructing a key target model M of the ocean scene O ：
Figure BDA0002191359900000011
Wherein the content of the first and second substances,
Figure BDA0002191359900000012
a target vector representing the model;
Figure BDA0002191359900000013
representing target vectors corresponding to a model
Figure BDA0002191359900000014
The weight vector of each target;
s3, obtaining a frame target vector of a suspicious target in the first frame image of the video sequence in the S1
Figure BDA0002191359900000015
S4, updating the key target model M O ：
The frame target vector obtained in S3
Figure BDA0002191359900000016
Input to the Key object model M O Vector of middle model
Figure BDA0002191359900000017
And combine the vectors
Figure BDA0002191359900000018
Setting the corresponding weight value to 1;
s5, obtaining a frame target vector of a suspicious target in the next frame image of the video sequence in the S1
Figure BDA0002191359900000021
S6, updating the key target model M O ：
The frame target vector obtained in S5
Figure BDA0002191359900000022
Input to the Key object model M O Vector of middle model
Figure BDA0002191359900000023
And combine the vectors
Figure BDA0002191359900000024
Increasing or decreasing the corresponding weight, judging whether all frames of the offshore target video sequence are processed or not, if not, turning to S5, and if so, turning to S7;
s7, target vector of slave model
Figure BDA0002191359900000025
Found weight value in W max Determined as a detected object, W max Is the maximum allowed weight of the target.
Preferably, in S5, the frame object vector of the suspicious object in the frame image
Figure BDA0002191359900000026
The acquisition method comprises the following steps:
s51, preprocessing, half-tone erosion and expansion are carried out on the frame image;
s52, carrying out image binarization;
s53, finding the sea horizon position;
s54, searching and dividing the independent target or the target on the horizon to obtain a frame target vector
Figure BDA0002191359900000027
S55, filtering and analyzing the acquired frame target vector to obtain a frame target vector
Figure BDA0002191359900000028
And determining a frame target vector
Figure BDA0002191359900000029
The center of mass and eccentricity of the cylinder.
Preferably, in S52, the method for binarizing the image includes:
based on the integral image, the luminance average m (x, y) and the standard deviation σ (x, y) in the local neighborhood are calculated within a window W × W centered on the point (x, y):
m(x,y)＝(I int (x+W/2,y+W/2)+I int (x-W/2,y-W/2)--I int (x-W/2,y+W/2)-I int (x+W/2,y-W/2))/W 2
I int (x, y) denotes an integral image I int The pixel intensity at the midpoint (x, y) is equal to the intensity of all pixels along the row and column before the point (x, y) in the original imageAnd (3) the sum:
Figure BDA00021913599000000210
i (u, v) denotes the integral image I int A middle pixel luminance;
Figure BDA0002191359900000031
T sq represents the sum of all pixels of the quadratic integral image in a window of size W × W centered on point (x, y):
Figure BDA0002191359900000032
Figure BDA0002191359900000033
representing a quadratic integral image
Figure BDA0002191359900000034
The luminance of the pixel at the midpoint (x, y) is equal to the sum of the squares of the luminance of all pixels along the row and column before the point (x, y) in the original image:
Figure BDA0002191359900000035
I 2 (u, v) represents the integral image I int A medium pixel luminance;
determining a pixel contrast threshold t (x, y) in the local neighborhood from the obtained m (x, y) and σ (x, y):
Figure BDA0002191359900000036
wherein k represents a fine tuning parameter and takes a value in the range of [0.2,0.5], and R represents the maximum value of the standard deviation;
and determining local contrast areas under different illumination scenes according to t (x, y).
Preferably, in S52, the method for finding the offshore horizon position includes:
scanning the binary image from top to bottom in a given rotation angle range of the line relative to the horizon, and searching only the line L with blank space y,α Each line L described by the equation of a straight line f (x, y) y,α The length of which is defined as the bright pixel I of the binary image on the line bin And (3) the sum:
Figure BDA0002191359900000037
wherein, the line L y,α The displacement along the vertical direction relative to the coordinate origin is y, and the rotation angle is alpha;
when traversing the image sequentially in the vertical direction, the following form of weight function is constructed for all lines:
Figure BDA0002191359900000038
wherein k is 1 ，k 2 ，k 3 Respectively show the coefficients of the working environment of the photoelectric system, and k is taken 1 +k 2 +k 3 ＝1，R y,α Representing the number of broken points on the line when the length of the line is calculated;
the length of the line with the maximum weight exceeds a set threshold value L min I.e. L y,α ≥L min The horizon is considered to be detected.
Preferably, in S54, the target on the horizon is searched and divided to obtain a frame target vector
Figure BDA0002191359900000049
The method comprises the following steps:
scanning along the horizon, during which the average thickness H of the horizon is calculated avg And constructing a binary object in the neighborhood of the horizonAn elevation map of (a);
the construction method of the elevation map comprises the following steps:
for all x e [0,M]M is the width of the image, from point y x+ ＝y 0 +h y To point y x- ＝y 0 -h y Go through traversal, wherein y 0 Denotes the horizon ordinate at a given x, h y Representing the height of the search area; in the scanning process from top to bottom, determining the first bright spot and the last bright spot, wherein the difference between the vertical coordinates is the height of the binary target at a given x position;
scanning and analyzing the elevation map according to the average thickness of the horizon and the elevation map of the binary target on the horizon:
if the height h at a given x i Is greater than the standard value h thr Then it can be assumed that a certain target will appear at this point on the horizon; if the target is the 1 st target detected during scanning, the standard value h thr ＝H avg +h k Wherein h is k Represents a minimum threshold constant, otherwise, h thr ＝H avg +h i-1 Wherein h is i-1 Height of the last column representing the previous target; if a starting point of the object is detected, then subsequent scans will begin to calculate the average, minimum and maximum heights of the object; height h of the target i Less than or equal to the standard value h thr The end of the target is determined and the criterion h is recalculated thr ＝H avg +h i-1 (ii) a If the width of the object detected in this way is greater than the minimum possible value and the aspect ratio of the object described by the rectangle R falls within a certain range set for detecting the object, the object o is set i Input to frame object vector
Figure BDA0002191359900000041
In (1).
Preferably, in S6, the key object model M is updated O The method of (1), comprising:
s61, after every n frames, each target
Figure BDA0002191359900000042
Corresponding weight of
Figure BDA0002191359900000043
Decrease by 1 if the target vector of the model
Figure BDA0002191359900000044
If the target is not empty and the weight is negative, then
Figure BDA0002191359900000045
In deleting the object and in
Figure BDA0002191359900000046
Deleting the target vector of the corresponding weight and model
Figure BDA0002191359900000047
And
Figure BDA0002191359900000048
1 is reduced;
s62, frame target vector
Figure BDA0002191359900000051
Input to the Key target model M O Vector of (5)
Figure BDA0002191359900000052
The method comprises the following steps:
if the target vector of the model
Figure BDA0002191359900000053
Null, the target vector will be at the current frame
Figure BDA0002191359900000054
Is input to the vector
Figure BDA0002191359900000055
In a vector
Figure BDA0002191359900000056
The corresponding weight in (1) is set to 1;
if the target vector of the model
Figure BDA0002191359900000057
Is not empty, for
Figure BDA0002191359900000058
In that
Figure BDA0002191359900000059
In the presence of a target o j ，o i And o j The euclidean distance between the centroids will be the smallest:
Figure BDA00021913599000000510
will D min (o i ,o j ) And a set threshold value epsilon d max Comparison update of
Figure BDA00021913599000000511
If D is min (o i ,o j )＞ε d max Target vector of frame
Figure BDA00021913599000000512
O in (1) i Adding to vectors
Figure BDA00021913599000000513
The preparation method comprises the following steps of (1) performing;
if D is min (o i ,o j )≤ε d max Evaluating frame object vectors
Figure BDA00021913599000000514
O in (1) i And the target vector of the model
Figure BDA00021913599000000515
Zhong o j If the similarity meets the standard, merging the targets o i And o j Object o j All parameters of (1) are determined by purposeMark o i After parameter replacement from the vector
Figure BDA00021913599000000516
In delete target o i ；
If w is j ＜W max Then object o j Weight w of j Increase to w j ＝w j +1；W max Is the maximum allowed weight of the target;
if w is j ＝W max Then by describing the object o j Is rectangular R j The defined halftone image is stored as a reference image for the target and added to the target vector of the model
Figure BDA00021913599000000517
Performing the following steps;
s63, target vector of slave model
Figure BDA00021913599000000518
Found weight value in W max Is determined as the detected object.
Preferably, the similarity criteria are:
Figure BDA00021913599000000519
wherein, P S 、P R 、P e Representing a similarity criterion; s i And S j Are respectively a target o i And o j The area of (d); r is i And r j Respectively, the aspect ratio of the rectangle describing each target; e.g. of the type i And e j Respectively the target eccentricity.
Preferably, S6 further includes, if it cannot be confirmed whether the target exists in the current frame image, targeting the target
Figure BDA0002191359900000061
Its weight value w k ＝W max -1 case using the target vectors stored in the model
Figure BDA0002191359900000062
Target of (1) k Automatically tracking the reference image.
The method has the advantages that based on the space-time analysis of the halftone image flow in the visible light range of the airborne photoelectric system of the unmanned aerial vehicle, two stages of time and space analysis are involved in processing each image, and in the space analysis stage, the current frame of a video sequence is processed to obtain the vector of a suspicious target. In the time analysis stage, the result of the spatial analysis is compared with a key target model of the current scene, and then the model is refined and updated without presetting a hard-coded reference image for target detection. The simulation experiment result verifies the performance and the effectiveness of the method provided by the invention.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a diagram of the results of multi-target and horizon automatic detection based on a test video sequence;
FIG. 3 is a single-target automatic detection result based on an image of an airborne optoelectronic system of the unmanned aerial vehicle;
FIG. 4 is a multi-target automatic detection result based on an image of an airborne optoelectronic system of the unmanned aerial vehicle.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
The invention is further described with reference to the following drawings and specific examples, which are not intended to be limiting.
The method for automatically detecting the marine target in the embodiment is to construct a key target model M of a scene based on data of the number, the position and the characteristics of the targets obtained by sequentially processing each frame of a video sequence O . The method involves two phases of temporal and spatial analysis when processing each image. In the spatial analysis stage, the current frame of the video sequence is processed to obtain the vector of the suspicious target. And in the time analysis stage, comparing the result of the spatial analysis with a key target model of the current scene, and further refining and updating the model.
As shown in fig. 1, the method for automatically detecting an offshore object according to the present embodiment includes:
s1, obtaining an offshore target video sequence of an airborne photoelectric system of the unmanned aerial vehicle:
and establishing a typical ocean scene model. The water surface and the sky are assumed to intersect at a surface with uneven illumination, and the intersection line is a straight line.
Objects of interest to the automatic detection system may be on the water surface or in the sky and their position, size, shape and illumination are approximately maintained in two adjacent frames taken over a short period of time. At the same time, various disturbing factors (glare and waves) on the sea surface can rapidly change their shape and illuminance.
S2, constructing a key target model M of the ocean scene O ：
Figure BDA0002191359900000071
Wherein the content of the first and second substances,
Figure BDA0002191359900000072
a target vector representing the model;
Figure BDA0002191359900000073
representing target vectors corresponding to a model
Figure BDA0002191359900000074
The weight vector of each target;
s3, obtaining a frame target vector of a suspicious target in the first frame image of the video sequence in the S1
Figure BDA0002191359900000075
S4, updating the key target model M O ：
The frame target vector obtained in S3
Figure BDA0002191359900000076
Input to the Key target model M O Vector of middle model
Figure BDA0002191359900000077
And combine the vectors
Figure BDA0002191359900000078
Setting the corresponding weight value to 1;
s5, obtaining a frame target vector of a suspicious target in the next frame image of the video sequence in the S1
Figure BDA0002191359900000079
S6, updating the key target model M O ：
The frame target vector obtained in S5
Figure BDA00021913599000000710
Input to the Key target model M O Vector of middle model
Figure BDA00021913599000000711
And combine the vectors
Figure BDA00021913599000000712
Increasing or decreasing the corresponding weight, judging whether all frames of the offshore target video sequence are processed, if not, turning to S5, and if so, turning to S7;
s7, target vector of slave model
Figure BDA00021913599000000713
Found weight value in W max Determined as a detected object, W max Is the maximum allowed weight of the target. In this embodiment, vectors are processed in the first frame
Figure BDA00021913599000000714
And
Figure BDA00021913599000000715
each component of (a) is null. In the spatial analysis stage, suspicious target vectors of frames are formed
Figure BDA00021913599000000716
(hereinafter referred to as frame object vector). In the time analysis phase, a model M is formed O From vectors to a first approximation
Figure BDA00021913599000000717
Is inputted to the vector
Figure BDA00021913599000000718
In a vector
Figure BDA00021913599000000719
Set the corresponding weight to 1. By each new input vector when processing subsequent frames of the video sequence
Figure BDA00021913599000000720
Updating model M O . During the model update, the weight of the target may be increased/decreased. When the weight of the target reaches the set maximum value W max The object is deemed to be reliably detected.
1. Spatial analysis of image to obtain frame target vector of suspicious target in frame image
Figure BDA00021913599000000721
The preferred embodiment includes:
1.1, preprocessing, half-tone erosion and expansion are carried out on a frame image;
1.2, carrying out image binarization;
1.3, searching the position of the sea horizon;
1.4, searching and dividing the target to obtain the frame target vector
Figure BDA0002191359900000081
1.5, filtering and analyzing the acquired frame target vector to obtain a frame target vector
Figure BDA0002191359900000082
And determining a frame target vector
Figure BDA0002191359900000083
The center of mass and eccentricity of the cylinder.
1.1 pretreatment, halftone etching and dilation:
in the present embodiment, a halftone image is regarded as a discrete luminance function in a two-dimensional space:
I＝i(x,y),x∈[0,M],y∈[0,N] (2)
wherein i (x, y) is E [ K min ,K max ]Represents the image brightness at the (x, y) point; k min Represents the minimum value of the image brightness; k max Represents the maximum value of the image brightness; m and N represent the width and height of the image, respectively.
A simpler planar square structural element b is introduced, defined as follows:
Figure BDA0002191359900000084
where B denotes a two-dimensional area (sliding window) on the image.
The width of the square window B will be referred to as the morphologically operated aperture. At this time, the erosion I (x, y) of the planar square structural element B at each point I (x, y) is the minimum value of the image brightness of the window B determined by the planar square structural element B centered at the I (x, y) point:
[I-B](x,y)＝min (bx,by)∈B {I(x+bx,y+by)} (4)
also, the present embodiment may introduce the concept of image expansion:
[I+B](x,y)＝min (bx,by)∈B {I(x-bx,y-by)} (5)
in the same or different apertures, a combination of erosion and dilation operations may reduce noise interference in the image and highlight potential targets.
1.2, image binarization:
the image is binarized to separate scene object information from the image background according to some criteria. In a binary image I bin Its element can only take one of two possible values. In this embodiment, the background pixel is referred to as a dark pixel, and the binary target pixel is referred to as a bright pixel.
If the performance of the computing system is high enough, then a variety of binary images may be constructed to achieve efficient detection of the target. For example, the contour binary image is used to search the horizon, or a binary image is formed by a method based on local contrast analysis to realize the search and segmentation of the target. If the onboard optoelectronic system computing power of the unmanned aerial vehicle does not allow the construction of two binary images in real time, only the contouring method can be used to detect the object.
Contour segmentation on the preprocessed image is achieved by vertical and horizontal convolution of the image with one or two differential matrices. Convolution can yield a gradient value for each point of the image in two directions:
Figure BDA0002191359900000091
the matrix that performs the convolution may have different forms, for example:
Figure BDA0002191359900000092
the total gradient at each point is:
G(x,y)＝|G x (x,y)|+|G y (x,y)| (7)
the binary contour image is:
Figure BDA0002191359900000093
where T denotes a threshold value determined based on the average gradient value.
T＝G avg *T c (9)
Wherein, T c Representing a constant value for a given photovoltaic system calculated empirically.
The following two methods can be selected to construct a binary image: constructing a binary image based on gradient calculation, searching for a horizon, and setting a low enough binarization threshold value to reliably segment the horizon; a binary image is constructed based on local contrast analysis and used for searching a limited region with uniform contrast point brightness, and compared with a target contour method, the method can provide more valuable information. In a preferred embodiment, the second method is selected to construct a binary image in this embodiment.
Halftone image I (x, y), where I (x, y) is ∈ [0,255], i.e., the luminance of the pixel at point (x, y). The goal of local threshold classification is to determine the threshold t (x, y) for each pixel, i.e.
Figure BDA0002191359900000094
In the present embodiment, it is proposed to determine the threshold t (x, y) based on the mean deviation m (x, y) and the standard deviation σ (x, y) calculated within a window centered on the point (x, y):
Figure BDA0002191359900000101
wherein the k-fine tuning parameter is taken within the range of [0.2,0.5], and the maximum value of the R-standard deviation (R =128 for halftone images).
The threshold value can be finely adjusted according to the pixel contrast in the local neighborhood by using the brightness average value and the standard deviation in the local neighborhood, and then the local contrast area can be determined under different illumination scenes.
However, it is extremely difficult to calculate local characteristics in the vicinity of each pixel. Without any improvement, for an image of resolution M × N, a square window of size W × W, the method approximates to a computational complexity of O (W) 2 MN). In order to speed up the calculation of local features, the present embodiment proposes a method based on integral image calculation.
For integral image I int In other words, the pixel luminance at point (x, y) is equal to the sum of the luminance of all pixels of the original image along the rows and columns before point (x, y):
Figure BDA0002191359900000102
under the condition that the integral image is obtained, the local luminance average value in the vicinity of an arbitrary point can be calculated by only several arithmetic operations:
Figure BDA0002191359900000103
also, the standard deviation is:
Figure BDA0002191359900000104
after finishing, obtaining:
Figure BDA0002191359900000105
to calculate the quadratic total luminance in the window, a second integral image needs to be constructed in which the luminance of the pixel at point (x, y) is equal to the sum of the squares of the luminances of all pixels of the original image along the rows and columns before point (x, y):
Figure BDA0002191359900000111
by T sq Represents the sum of all pixels of the quadratic integral image in a window of size W × W centered on point (x, y):
Figure BDA0002191359900000112
at this time, the following were obtained:
Figure BDA0002191359900000113
after finishing, obtaining:
Figure BDA0002191359900000114
therefore, calculating the threshold t (x, y) according to equation (11) can be translated into the calculation of the normalized sum of the two integral images.
Integrating the image I while processing a frame int And
Figure BDA0002191359900000115
is only calculated once, so the use of an integral image can significantly reduce the computational complexity of the method of constructing a binary image based on locally adaptive thresholds. The binarization method provided by the embodiment has a better effect in actual target detection than a gradient-based binarization contour method.
1.3 finding the position of the horizon:
with the parameters of the horizon, the object on the horizon can be separated from the background, and even the type of the detected object can be inferred according to the position of the object relative to the horizon.
Currently, there are some good algorithms for finding lines on an image, for example, those based on the hough transform. However, for object detection problems in complex sea states, the number of lines segmented according to such an algorithm may be very large, and the longest of these lines is not necessarily the desired horizon. This will result in longer bright areas on the binary image due to the waves and undulations creating fairly large uniform high and low brightness areas.
For the above reasons, the present embodiment proposes a new line search algorithm that scans a binary image from top to bottom within a given rotation angle range of the line with respect to the horizon (d e [ - α, + α ]), searching only those lines in which a blank space (dark pixels on the binary image) exists in some region.
For each line L described by the equation of a straight line f (x, y) y,α (displacement in the vertical direction with respect to the origin of coordinates is y, rotation angle is α), the length of which is defined as the sum of the bright pixels of the binary image on the line:
Figure BDA0002191359900000121
when traversing the image sequentially in the vertical direction, the following form of weight function is constructed for all lines:
Figure BDA0002191359900000122
wherein k is 1 ，k 2 ，k 3 Taking k as a function of the coefficients of the environment in which the photovoltaic system operates (whether equipped with a gyrostabiliser, sea wave intensity, visibility conditions, etc.) 1 +k 2 +k 3 ＝1；R y,α -calculating the number of break points on the line at the length of the line.
Thus, the line with the longest length and the least number of break points will have the greatest weight and be closest to the horizon. The straight line with the greatest weight is taken as the horizon. If the length of the line with the greatest weight exceeds a set threshold, i.e., L y,α ≥L min The horizon is considered to be detected.
1.4 searching and segmenting of objects
In this step, the present embodiment introduces the concept of a binary target. Binary object
Figure BDA0002191359900000123
Is a binary image I bin The total number (area) of bright pixels in the pixel group is S, by means of the rectangle R and the target feature vector (center of mass, eccentricity, etc.)
Figure BDA0002191359900000124
To describe.
All binary objects detected on the current frame are input to the frame object vector
Figure BDA0002191359900000125
In (1). At the initial moment of analyzing the frame, the frame target vector
Figure BDA0002191359900000126
Is empty.
In the present embodiment, the following two types of target search problems on a binary image are mainly studied. If the horizon is detected on the image of the previous step, the objects on the horizon are first analyzed. Otherwise, searching and dividing the independent target.
1.4.1 target search and segmentation on horizon:
in general, the contour of the potential object blends with the horizon and appears as a long binary object in the binary image. To determine whether an object is present on the horizon, a scan is performed along the horizon y = kx + b, where the parameters k and b were determined in the previous step. During scanning, the average thickness H of the horizon is calculated avg And constructing an elevation map of the binary target in a certain neighborhood of the horizon.
The elevation map is constructed as follows. For all x e [0,M](M is the width of the image), from point y x+ ＝y 0 +h y To point y x- ＝y 0 -h y Go through traversal, wherein y 0 -given the horizon ordinate at x，h y -the height of the search area. During the top-down scan, the 1 st and last bright spot are determined, and the difference between their ordinate is the height of the binary target at a given x. Thus, if an object is present in a certain neighborhood, the constructed elevation map will have a defined form, e.g., h = [ K,3, 4,6,8,13, 14,12,8,6,5, 3, K = [ K,3, 4,6, 5, 8,13, 14,12, 3, K ] K]At this time, the average thickness of the horizon will be H avg ＝3。
After the average thickness of the horizon and the elevation map of the binary target on the horizon are obtained, the elevation map can be scanned and analyzed in detail. If the elevation deviation at a given x is larger than a certain criterion value h i ＞h thr Then it can be assumed that a certain target will be present at this point on the horizon. If the target is the 1 st target detected during scanning, the standard value h thr ＝H avg +h k Wherein h is k -a minimum threshold constant. Otherwise, h thr ＝H avg +h i-1 Wherein h is i-1 Height of the last column (right side when scanning from left to right) of the previous target. If a starting point of the object is detected, then subsequent scans will begin to calculate the average, minimum and maximum heights of the object. Once the height of the target is less than the threshold h i ≤h thr The end of the target is determined and the threshold h is recalculated thr ＝H avg +h i-1 . If the width of the object detected in this way is greater than the minimum possible value and the aspect ratio of the object described by the rectangle R falls within a certain range set for detecting the object (i.e., the object cannot be too long in the vertical or horizontal direction), the object is o i Input to frame object vector
Figure BDA0002191359900000131
In (1).
Furthermore, the target may be limited by other conditions, for example, the degree of unevenness in the target height must be greater than a certain threshold to reduce the effects of waves, glare and coastal infrastructure on the horizon.
1.4.2 search and segmentation of independent targets
In addition to considering objects on the horizon and the case where the horizon position cannot be determined, the more general case of object search and segmentation must also be considered.
For labeling and segmentation of related objects on a binary image, a recursive filling algorithm, a one-pass mask algorithm, etc. may be used. The computation speed of the marking algorithm may vary greatly but it works essentially the same. The image is traversed in rows and columns. If an unmarked pixel is detected at a certain point (x, y), the pixel is marked and all neighboring pixels are traversed and marked according to the criterion of 4-connectivity or 8-connectivity. In the marking process, the area of a binary target rectangle description area is determined, and then threshold value checking is carried out:
Figure BDA0002191359900000141
wherein R is X -a width of a rectangle describing the object; r Y -a height of a rectangle describing the object; x min And Y min -minimum width and height of the target, respectively; s min And S max -a minimum and a maximum of the binary target area, respectively; r is min And r max Minimum and maximum values, respectively, of the aspect ratio of the rectangle describing the object.
The threshold is adjusted according to the parameters of the photovoltaic system and the expected size of the target, and most of small noises, waves and the like can be filtered. If the target is binary o i (S, R) satisfies the selected criterion, and is input to the frame target vector
Figure BDA0002191359900000142
In (1).
1.5 Filtering and analysis of target vectors
In this step, the target vectors are obtained for the previously obtained frames
Figure BDA0002191359900000143
For further analysis. Contour of objects in binary imagesOften can be decomposed into contours of different sub-objects, which will result in a frame object vector
Figure BDA0002191359900000144
Of (2) is redundant.
To eliminate this redundancy, the frame object vector is subjected to
Figure BDA0002191359900000145
Traversal is performed and all objects stored therein are compared pairwise. For each pair of targets o i And o j The following conditions were checked:
a) Describing object o i Should at least include a description object o j And vice versa;
b) Describing object o i And o j The euclidean distance between the centers of the two rectangles being smaller than a certain threshold value D < epsilon. In this case, the value of ε may be determined, for example, based on the diagonal length of the largest rectangle.
If at least one condition is satisfied, the targets are merged, their areas and the described rectangles are added and written to target o i Simultaneous slave vector
Figure BDA0002191359900000146
In delete target o j 。
After the merging process of each target is completed, a new target vector described using a rectangle R is generated
Figure BDA0002191359900000147
Central moment mu pq And calculating to the second order, and determining the coordinates of the mass center of the target, the direction and the eccentricity of the target on the basis of the coordinates.
In picture I bin Initial moment of the target in (x, y):
Figure BDA0002191359900000148
center of mass of the target:
Figure BDA0002191359900000151
wherein the content of the first and second substances,
Figure BDA0002191359900000152
in picture I bin (x, y) the central moments of the targets are:
Figure BDA0002191359900000153
eccentricity can be determined by the image I in the region of (x, y) ∈ R bin The eigenvalues of the (x, y) covariance matrix determine:
Figure BDA0002191359900000154
wherein the content of the first and second substances,
Figure BDA0002191359900000155
at this time, the eccentricity is:
Figure BDA0002191359900000156
the algorithm executes to this end and the image space analysis phase ends, the image being analyzed temporally as follows.
2. Temporal analysis of images updating the Key object model M O
The image is analyzed temporally to construct a model at an initial stage
Figure BDA0002191359900000157
And further perfecting it. Updating a Key target model M O The method comprises the following steps: :
2.1 target vector of the if model
Figure BDA0002191359900000158
If not, a "kill" procedure is performed.
2.2 updating frame target vectors obtained based on image space analysis
Figure BDA0002191359900000159
Model M of O 。
2.3. If the presence of an object cannot be reliably confirmed in the current frame, the vector is paired by a specific condition
Figure BDA00021913599000001510
Automatically track the target in (1).
2.1 "subtractive" procedure
After every n frames, each target
Figure BDA00021913599000001511
Weight of (2)
Figure BDA00021913599000001512
Decrease by 1:
Figure BDA00021913599000001513
wherein, Z-vector
Figure BDA0002191359900000161
And
Figure BDA0002191359900000162
of (c) is used.
If the target o is reduced i Becomes negative (i.e., w) i < 0), then the slave vector
Figure BDA0002191359900000163
In delete target o i And from the vector of values
Figure BDA0002191359900000164
Deleting corresponding weight w i Vector of
Figure BDA0002191359900000165
And
Figure BDA0002191359900000166
is reduced by 1. Thus, over time, the weights of all targets will fade away, and if the weights of the targets are small enough, a decision is made to drop the targets. The choice of the "cut-off" frequency depends on the frame rate f of the photovoltaic system, determined by the following equation:
Figure BDA0002191359900000167
wherein k is f Constant, proportional to the frame rate of the photovoltaic system.
In the present embodiment, the onboard photovoltaic system frame rate f =15,k f The value of (c) is half of the frame frequency of the photovoltaic system: k is a radical of formula f =7.5, then n =1/3. At this value of n, every third frame weight is "subtracted" 1 time, which allows the algorithm to adapt to situations where the interval between input frames is large.
2.2 model M O Is updated
If the target vector of the model
Figure BDA0002191359900000168
Null, then all objects detected on the current frame are slave vectors
Figure BDA0002191359900000169
Is input to the vector
Figure BDA00021913599000001610
And add it to the vector
Figure BDA00021913599000001611
The corresponding weight in (1) is set to 1.
If vector
Figure BDA00021913599000001612
Already containing models of certain objects, then try to fit each object
Figure BDA00021913599000001613
Adding to vectors
Figure BDA00021913599000001614
In (1). For the
Figure BDA00021913599000001615
In the vector
Figure BDA00021913599000001616
In which such an object o is present j Object o i And o j The euclidean distance between centroids will be the smallest:
Figure BDA00021913599000001617
if the distance is greater than a certain maximum value D min ＞ε d max Then the target o i Adding to vectors
Figure BDA00021913599000001618
In the vector
Figure BDA00021913599000001619
To create a weight value w corresponding thereto i And =1. If D is min (o i ,o j )≤ε d max Then, the target o is evaluated i And o j The similarity of (2);
merging the targets o if the comparison targets meet all similarity criteria i And o j . Object o j By the target o i Is updated (replaced) after which the parameters are updated from the vector
Figure BDA00021913599000001620
In delete target o i . If w is j ＜W max Then, corresponds to the target o j Weight value w of j Increase to w j ＝w j +1 wherein, W max The maximum allowable weight of the target is obtained, and compromise is made between the target detection speed and the false alarm rate; w max Must be proportional to or equal to the frame rate of the photovoltaic system.
If the weight of the target reaches the maximum value allowed, the target o is described j Is rectangular R j The part of the defined halftone image is stored as a reference image for the target and added to the vectors of its model
Figure BDA0002191359900000171
In, the weight reaches the maximum value w j ＝W max Target o of j Is considered a reliable detection.
The similarity criteria of the present embodiment are:
Figure BDA0002191359900000172
wherein, P S 、P R 、P e Representing a similarity criterion; s i And S j Are respectively a target o i And o j The area of (d); r is i And r j Respectively, the aspect ratio of the rectangle describing each target; e.g. of the type i And e j Respectively the target eccentricity.
Similarity criterion P for a given photovoltaic system S ，P R ，P e Are empirically chosen based on the expected operating conditions of the system.
2.3 auto-tracking
For all reliably detected objects
Figure BDA0002191359900000173
(i.e., those model objects of the scene whose weights reach the maximum allowed value at least once), whose weights are not updated in 2.2 steps, at w k ＝W max In the case of-1, the storage in the vector is used
Figure BDA0002191359900000174
The standard halftone image of the target model in (a) to perform an auto-tracking procedure.
In this case, any automatic tracking algorithm may be used. The integral image I is constructed in the binarization process of the present embodiment int It is convenient to use it in this step to speed up the calculation of the cross-correlation. If the target is found by the correlator, i.e. the maximum correlation coefficient in the search area is greater than a certain threshold c max ≥C min Then object o k Weight w of k Increases and becomes maximum:
w k ＝(W max -1)+1＝W max 。
3 analysis and decision
This step is the last step. Target vector from model
Figure BDA0002191359900000175
Of those weights equal to the maximum value w det ＝W max Target o of det . These objects are stored in a result vector
Figure BDA0002191359900000176
This is the result of the operation of the target automatic detection method of the present embodiment.
4. Simulation experiment
Most of the steps of the method proposed by the invention can be implemented in multiple threads. Therefore, the method is implemented by the massively parallel technology CUDA, which allows real-time processing of test images with a resolution of 1920 × 1080 at speeds exceeding 25 frames/sec.
Configuration parameters of the simulation computer:
a processor: intel core i 5-4440.1Ghz;
a display card: geForce GTX 750Ti (640 Cuda cores) 2GB;
the working result of a multi-threaded implementation of the method using a test video sequence is given in fig. 2. The 4 targets are automatically detected and the horizon position is determined on the image.
The automatic detection system for the offshore targets, which is realized by the method provided by the invention, is an important component of an onboard photoelectric system of the unmanned aerial vehicle, and the image display performance is as high as 100 frames/second under the resolution of 768 x 576.
The results of the object automatic detection method of the present invention are given on fig. 3 and 4.
And (4) conclusion: the simulation experiment result verifies the performance and the effectiveness of the method provided by the invention.

Claims (7)

1. An automatic offshore object detection method, comprising:
s1, acquiring an offshore target video sequence of an airborne photoelectric system of the unmanned aerial vehicle;
s2, constructing a key target model M of the ocean scene O ：
Figure FDA0003831980700000011
Wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003831980700000012
a target vector representing the model;
Figure FDA0003831980700000013
representing target vectors corresponding to a model
Figure FDA0003831980700000014
The weight vector of each target;
s3, obtaining a frame target vector of a suspicious target in the first frame image of the video sequence in the S1
Figure FDA0003831980700000015
S4, updating the key target model M O ：
The frame object vector obtained in S3Measurement of
Figure FDA0003831980700000016
Input to the Key target model M O Vector of middle model
Figure FDA0003831980700000017
And combine the vectors
Figure FDA0003831980700000018
Setting the corresponding weight value to 1;
s5, obtaining a frame target vector of a suspicious target in the next frame image of the video sequence in the S1
Figure FDA0003831980700000019
S6, updating the key target model M O ：
The frame target vector obtained in S5 is processed
Figure FDA00038319807000000110
Input to the Key target model M O Vector of middle model
Figure FDA00038319807000000111
And combine the vectors
Figure FDA00038319807000000112
Increasing or decreasing the corresponding weight, judging whether all frames of the offshore target video sequence are processed, if not, turning to S5, and if so, turning to S7;
s7, target vector of slave model
Figure FDA00038319807000000113
Found weight value in W max Determined as a detected object, W max Is the maximum allowed weight of the target;
wherein S6 updates the key target model M O The method comprises the following steps:
s61, after every n frames, each target
Figure FDA00038319807000000114
Corresponding weight of
Figure FDA00038319807000000115
Decrease by 1 if the target vector of the model
Figure FDA00038319807000000116
If the target is not empty and the weight is negative, then
Figure FDA00038319807000000117
In deleting the object and in
Figure FDA00038319807000000118
Deleting the target vector of the corresponding weight and model
Figure FDA00038319807000000119
And
Figure FDA00038319807000000120
1 is subtracted from the size of the target;
s62, frame target vector
Figure FDA00038319807000000121
Input to the Key target model M O Vector of (5)
Figure FDA00038319807000000122
The method comprises the following steps:
if the target vector of the model
Figure FDA00038319807000000123
Null, the target vector will be at the current frame
Figure FDA00038319807000000124
Input to vectorMeasurement of
Figure FDA00038319807000000125
And add it to the vector
Figure FDA00038319807000000126
The corresponding weight in (1) is set to 1;
if the target vector of the model
Figure FDA0003831980700000021
Is not empty, for
Figure FDA0003831980700000022
In that
Figure FDA0003831980700000023
In which there is an object o j ，o i And o j The euclidean distance between the centroids will be the smallest:
Figure FDA0003831980700000024
will D min (o i ,o j ) And a set threshold value epsilon dmax Comparison update of
Figure FDA0003831980700000025
If D is min (o i ,o j )＞ε dmax Target vector of frame
Figure FDA0003831980700000026
O in (1) i Adding to vectors
Figure FDA0003831980700000027
Performing the following steps;
if D is min (o i ,o j )≤ε dmax Evaluating the frame target vector
Figure FDA0003831980700000028
O in (1) i And the target vector of the model
Figure FDA0003831980700000029
Middle o j If the similarity meets the criterion, merging the target o i And o j Object o j By the target o i After parameter replacement from the vector
Figure FDA00038319807000000210
In delete target o i ；
If w is j ＜W max Then object o j Weight value w of j Increase to w j ＝w j +1；W max Is the maximum allowed weight of the target;
if w is j ＝W max Then by describing the object o j Is rectangular R j The defined halftone image is stored as a reference image for the target and added to the target vector of the model
Figure FDA00038319807000000211
Performing the following steps;
s63, target vector of slave model
Figure FDA00038319807000000212
Found weight value in W max Is determined as the detected object.
2. The method according to claim 1, wherein in S5, the frame object vector of the suspicious object in the frame image
Figure FDA00038319807000000213
The acquisition method comprises the following steps:
s51, preprocessing, half-tone erosion and expansion are carried out on the frame image;
s52, carrying out image binarization;
s53, finding the sea horizon position;
s54, searching and dividing the independent target or the target on the horizon to obtain a frame target vector
Figure FDA00038319807000000214
S55, filtering and analyzing the acquired frame target vector to obtain a frame target vector
Figure FDA00038319807000000215
And determining a frame target vector
Figure FDA00038319807000000216
The center of mass and eccentricity of the cylinder.
3. The offshore object automatic detection method according to claim 2, wherein in S52, the image binarization method comprises:
based on the integral image, the luminance average m (x, y) and the standard deviation σ (x, y) in the local neighborhood are calculated within a window W × W centered on the point (x, y):
m(x,y)＝(I int (x+W/2,y+W/2)+I int (x-W/2,y-W/2)--I int (x-W/2,y+W/2)-I int (x+W/2,y-W/2))/W 2
I int (x, y) denotes an integral image I int The pixel intensity at the midpoint (x, y), is equal to the sum of the intensities of all pixels along the rows and columns of the original image before the point (x, y):
Figure FDA0003831980700000031
i (u, v) denotes an integral image I int A medium pixel luminance;
Figure FDA0003831980700000032
T sq represents the sum of all pixels of the quadratic integral image in a window of size W × W centered on point (x, y):
Figure FDA0003831980700000033
Figure FDA0003831980700000034
representing a quadratic integral image
Figure FDA0003831980700000035
The luminance of the pixel at the midpoint (x, y) is equal to the sum of the squares of the luminance of all pixels along the row and column before the point (x, y) in the original image:
Figure FDA0003831980700000036
I 2 (u, v) represents the integral image I int A middle pixel luminance;
determining a pixel contrast threshold t (x, y) in the local neighborhood from the obtained m (x, y) and σ (x, y):
Figure FDA0003831980700000037
wherein k represents a fine tuning parameter and takes a value in the range of [0.2,0.5], and R represents the maximum value of the standard deviation;
and determining local contrast areas under different illumination scenes according to t (x, y).
4. The offshore object automatic detection method according to claim 2, wherein in S52, the method for finding the offshore horizon position is as follows:
scanning the binary image from top to bottom in a given rotation angle range of the line relative to the horizon, and searching only the lines with blank spaceStrip L y,α Each line L described by the equation of a straight line f (x, y) y,α The length of which is defined as the bright pixel I of the binary image on the line bin And (3) the sum:
Figure FDA0003831980700000041
wherein, the line L y,α The displacement along the vertical direction relative to the coordinate origin is y, and the rotation angle is alpha;
when traversing the image sequentially in the vertical direction, the following form of weight function is constructed for all lines:
Figure FDA0003831980700000042
wherein k is 1 ，k 2 ，k 3 Respectively showing coefficients of the working environment of the photoelectric system, and taking k 1 +k 2 +k 3 ＝1，R y,α Representing the number of broken points on the line when the length of the line is calculated;
the length of the line with the maximum weight exceeds a set threshold value L min I.e. L y,α ≥L min The horizon is considered to be detected.
5. The offshore object automatic detection method according to claim 2, wherein in S54, the objects on the horizon are searched and segmented to obtain frame object vectors
Figure FDA0003831980700000043
The method comprises the following steps:
scanning along the horizon, and calculating the average thickness H of the horizon during scanning avg Constructing an elevation map of the binary target in the neighborhood of the horizon;
the construction method of the elevation map comprises the following steps:
for all x e [0,M]M is the width of the image, from point y x+ ＝y 0 +h y To point y x- ＝y 0 -h y Go through traversal, wherein y 0 Denotes the horizon ordinate, h, at a given x y Representing the height of the search area; in the scanning process from top to bottom, determining the first bright spot and the last bright spot, wherein the difference between the vertical coordinates is the height of the binary target at a given x position;
scanning and analyzing the elevation map according to the average thickness of the horizon and the elevation map of the binary target on the horizon:
if the height h at a given x i Is greater than the standard value h thr Then it can be assumed that a certain target will appear at this point on the horizon; if the target is the 1 st target detected during scanning, the standard value h thr ＝H avg +h k Wherein h is k Represents a minimum threshold constant, otherwise, h thr ＝H avg +h i-1 Wherein h is i-1 Height of the last column representing the previous target; if a starting point of the object is detected, then subsequent scans will begin to calculate the average, minimum and maximum heights of the object; once height h of the target i Less than or equal to the standard value h thr The end of the target is determined and the standard value h is recalculated thr ＝H avg +h i-1 (ii) a If the width of the object detected in this way is greater than the minimum possible value and the aspect ratio of the object described by the rectangle R falls within a certain range set for detecting the object, the object o is set i Input to frame object vector
Figure FDA0003831980700000051
In (1).
6. The offshore object automatic detection method according to claim 1, wherein the similarity criterion is:
Figure FDA0003831980700000052
wherein, P S 、P R 、P e Representing a similarity criterion; s i And S j Are respectively a target o i And o j The area of (d); r is i And r j Respectively, the aspect ratio of the rectangle describing each target; e.g. of the type i And e j Respectively the target eccentricity.
7. The offshore object automatic detection method according to claim 6, wherein S6 further comprises, if the existence of the object cannot be confirmed in the current frame image, identifying the object
Figure FDA0003831980700000053
Its weight value w k ＝W max -1 case, using the target vectors stored in the model
Figure FDA0003831980700000054
Target of (1) k Automatically tracking the reference image.
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