CN112967305B - Image cloud background detection method under complex sky scene - Google Patents
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- 238000003708 edge detection Methods 0.000 claims description 19
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Abstract
The invention provides an image cloud background detection method under a complex sky scene, which aims at an empty application scene of a photoelectric search system, and comprises the steps of firstly constructing a neighborhood convolution kernel template, extracting first and second neighborhood envelope features, and carrying out relevant neighborhood filtering on an original image; secondly, constructing a horizontal, vertical, opposite diagonal and secondary diagonal multidirectional morphological gradient direction structure operator, extracting contour edges of the image, and fusing multidirectional detection results by adaptively constructing a direction edge weight factor to obtain a contour edge image; and finally, carrying out neighborhood connected domain marking on the edge image to realize extraction of cloud background contours.
Description
Technical Field
The invention relates to the field of image processing and computer vision, in particular to an image cloud background detection method under a complex sky scene.
Background
With the increasing complexity of modern war environment, the photoelectric searching and tracking system has wider requirements in various battlefield defense scenes due to the characteristics of passivity, uneasiness of environmental influence and the like. Aiming at the problem that a small target is detected by photoelectric paired air and has a thicker cloud layer for shielding the target, the existing solution mainly comprises a neural network, morphological filtering, an interframe difference method and the like, which have good performance when the background changes smoothly, but the detection performance is greatly affected when the frame frequency is low under the complex cloud layer background, and the algorithm false alarm is higher.
Disclosure of Invention
The invention aims to: aiming at the defects of the prior art, the invention provides an image cloud background detection method under a complex sky scene, which comprises the following steps:
step 1, constructing a 5*5 neighborhood convolution kernel template S, extracting relevant first and second neighborhood envelope feature medians, and carrying out neighborhood convolution operation on an original Image1 to obtain a relevant neighborhood filtering Image2;
step 2, constructing a direction gradient edge operator, and extracting morphological direction gradient contour edges of the Image2;
step 3, extracting the contour detail edge to obtain a unidirectional edge detection result, and constructing a fusion edge weight factor lambda i Edge detection and calculation are carried out on the horizontal, vertical, main diagonal and secondary diagonal directions to obtain an edge Image3;
and 4, carrying out connected domain marking on the edge Image3, and extracting to obtain a cloud background connected domain contour.
The step 1 comprises the following steps:
step 1-1, defining 5*5 neighborhood convolution kernel templates as S, wherein the initial value of an S matrix is 0;
step 1-2, calculating a first neighborhood W of a window of a center f (x, y) of the current image 1 Median m of (2) 1 I.e. m 1 ＝median(W 1 ) Where mean is the median operation of the calculated vector, x is the horizontal coordinate, y is the vertical coordinate, W 1 The method comprises the following steps:
W 1 ＝[f(x-1,y)，f(x,y+1)，f(x,y)，f(x+1,y)，f(x,y-1)]
step 1-3, calculating a second neighborhood W of the current image center f (x, y) window 2 Median m of (2) 2 I.e. m 2 ＝median(W 2 )，W 2 The method comprises the following steps:
step 1-4, set convolution kernel template center pixel S (2, 2) =max (m 1 ,m 2 ) A neighborhood convolution kernel template S is obtained, where Max is the maximum operation of the calculated vector.
In step 2, the constructing a directional gradient edge operator includes:
respectively establishing a horizontal, vertical, main diagonal and secondary diagonal direction gradient structure operator U 1 、U 2 、U 3 、U 4 Directional gradient structure operator U i The method comprises the following steps:
where i=1, 2,3,4.
In step 2, the step of performing morphological direction gradient contour edge extraction on the Image2 includes:
the morphological direction gradient contour edge extraction is carried out by adopting the following formula:
ZI i ＝f·U i -fΘU i ·L
wherein ZE i ZI is the result of rough extraction of the edge of the outer contour line i For the inner contour edge coarse extraction result, f is the Image2,for morphological dilation operation, Θ is morphological erosion operation,/->For morphological open operation, for morphological close operation, the structure operator L is:
in step 3, the step of extracting the contour detail edge to obtain a unidirectional edge detection result includes:
extracting the contour detail edge by adopting the following formula to obtain a unidirectional edge detection result:
ZX i ＝Max(ZE i ,ZI i )-Min(ZE i ,ZI i )
Z i ＝ZE i +ZI i +ZX i
wherein ZX i For contour detail edges, Z i For the unidirectional edge detection result, min represents the minimum value of the calculation vector.
In step 3, the fusion edge weight factor lambda is constructed i Edge Image3 is obtained by detecting and calculating the edges of the horizontal, vertical, main diagonal and secondary diagonal directions, and the method comprises the following steps:
the fusion edge weight factor lambda is constructed by the following formula i ：
D i ＝∑|d i -d 0 |
Wherein D is i Represents the gray level difference value of each direction gradient, and d is when the i value is 1,2,3 and 4 respectively i Pixel values respectively representing horizontal, vertical, primary diagonal, secondary diagonal, d 0 Is the center point pixel value;
extracting the multi-direction edge detection result to generate a final edge detection result Z, namely an edge Image3:
step 4 comprises: traversing the edge Image3, extracting pixel points with pixel values of 1 in the Image3 as seed pixel points, marking all foreground pixel points with adjacent 4 neighborhood or 8 neighborhood pixel values of 1 of the seed pixel points as L, and generating a connected domain contour Q by using all pixel forming areas with the same mark as L 1 Repeating the above steps until the whole image is traversed to obtain the final connected domain vector (Q 1 ,Q 2 ,...Q i ),i∈(1,2,3...)，Q i Representing the i-th connected domain contour.
The cloud layer and background edge features are extracted mainly by providing improved morphological gradient edge features, and the cloud layer background region can be effectively detected and the target shielded region can be prejudged by means of morphological gradient fusion self-adaptive weighting in all directions.
The beneficial effects are that: the invention provides an image cloud background detection method under a complex sky scene aiming at a scene of cloud interference of an image on an empty detection target. The method can effectively detect the cloud background by testing various sky backgrounds, and can effectively detect the cloud background by testing and verifying on a domestic FT-2000 platform, and the average frame rate of processing reaches 35Hz.
Drawings
The foregoing and/or other advantages of the invention will become more apparent from the following detailed description of the invention when taken in conjunction with the accompanying drawings and detailed description.
Fig. 1 is a flowchart of image cloud background detection in a complex sky scene according to an embodiment of the present invention.
FIG. 2 is a schematic diagram of a first neighborhood in an embodiment of the present invention.
FIG. 3 is a schematic diagram of a second neighborhood in an embodiment of the present invention.
FIG. 4 is a schematic representation of a morphological directional gradient in an embodiment of the invention.
Fig. 5 is an input image in an embodiment of the invention.
Fig. 6 is a graph showing the result of image edge detection in an embodiment of the present invention.
Fig. 7 shows the contour detection result in the embodiment of the present invention.
Detailed Description
As shown in fig. 1, the present invention provides a method for detecting an image cloud background in a complex sky scene, including:
a) As shown in fig. 5, the infrared image data image1 is acquired, a 5*5 neighborhood convolution kernel template S is constructed, all matrix values are set to 0, and as shown in fig. 2 and 3, a 5*5 pixel matrix fw of the current image center f (x, y) is extracted 1 ：
Computing a first neighborhood W of a current window 1 Median m of = (172,166,178,176,174) 1 Similarly calculate the second neighborhood W of the current window =174 2 Median m of (2) 2 =170, the convolution kernel template center pixel S (2, 2) =max (m 1 ,m 2 ) S (2, 2) =170 can be obtained, where Max is the maximum value operation of the calculated vector, and the neighborhood convolution S operation is performed on the original Image1 traversal to obtain the relevant neighborhood filtered Image2;
b) Constructing a direction gradient edge operator, and respectively constructing a horizontal, vertical, main diagonal and secondary diagonal direction gradient structure operator U as shown in figure 4 for further enhancing the extraction of cloud background edges 1 、U 2 、U 3 、U 4 Directional gradient structure operator U i (i=1, 2,3, 4) is:
morphological direction gradient contour edge extraction includes:
ZI i ＝f·U i -fΘU i ·L
wherein ZE i ZI is the result of rough extraction of the edge of the outer contour line i The rough extraction result of the inner contour line edge is f is Image2, U i I is a directional gradient edge operator, the values of i are 1,2,3 and 4,for morphological dilation operation, Θ is morphological erosion operation,/->For morphological open operation, for morphological close operation, the structure operator L is:
c) Extracting the contour detail edge to obtain a unidirectional edge detection result Z i ：
ZX i ＝Max(ZE i ,ZI i )-Min(ZE i ,ZI i )
Z i ＝ZE i +ZI i +ZX i
Wherein ZX i For contour detail edges, Z i For the unidirectional edge detection result, max is the maximum value of the calculated vector, and Min is the minimum value of the calculated vector. As shown in fig. 6, the final edge detection result Z is generated by extracting the multi-directional edge detection result, that is, the edge Image3, specifically:
wherein the edge weighting factor lambda is fused i Comprising the following steps:
D i ＝∑|d i -d 0 |
wherein D is i Represents the gray level difference value of each direction gradient, d i Representing the respective pixel values of the horizontal, vertical, primary diagonal, secondary diagonal, d 0 The values of i are 1,2,3 and 4 for the pixel value of the central point.
d) Traversing the edge Image3, extracting a pixel point with a pixel value of 1 in the Image3 as a seed pixel point, marking L on all foreground pixel points with adjacent 4-neighborhood or 8-neighborhood pixel values of 1, and generating a connected domain Q by using all pixel forming areas with the same mark as L 1 Repeating the above steps until the whole image is traversed to obtain the final connected domain vector (Q 1 ,Q 2 ,...Q i ) I.e. (1, 2, 3.). The final results are shown in FIG. 7.
The invention provides a method for detecting image cloud background in complex sky scene, which has a plurality of methods and approaches for realizing the technical scheme, the above description is only a preferred embodiment of the invention, and it should be noted that, for those skilled in the art, a plurality of improvements and modifications can be made without departing from the principle of the invention, and the improvements and modifications should also be regarded as the protection scope of the invention. The components not explicitly described in this embodiment can be implemented by using the prior art.
Claims (6)
1. The image cloud background detection method under the complex sky scene is characterized by comprising the following steps of:
step 1, constructing a 5*5 neighborhood convolution kernel template S, extracting relevant first and second neighborhood envelope feature medians, and carrying out neighborhood convolution operation on an original Image1 to obtain a relevant neighborhood filtering Image2;
step 2, constructing a direction gradient edge operator, and extracting morphological direction gradient contour edges of the Image2;
step 3, extracting the contour detail edge to obtain a unidirectional edge detection result, and constructing a fusion edge weight factor lambda i Edge detection and calculation are carried out on the horizontal, vertical, main diagonal and secondary diagonal directions to obtain an edge Image3;
step 4, carrying out connected domain marking on the edge Image3, and extracting to obtain a cloud background connected domain contour;
in step 3, the fusion edge weight factor lambda is constructed i Edge Image3 is obtained by detecting and calculating the edges of the horizontal, vertical, main diagonal and secondary diagonal directions, and the method comprises the following steps:
the fusion edge weight factor lambda is constructed by the following formula i ：
D i ＝∑|d i -d 0 |
Wherein D is i Represents the gray level difference value of each direction gradient, and d is when the i value is 1,2,3 and 4 respectively i Pixel values respectively representing horizontal, vertical, primary diagonal, secondary diagonal, d 0 Is the center point pixel value;
extracting the multi-direction edge detection result to generate a final edge detection result Z, namely an edge Image3:
2. the method of claim 1, wherein step 1 comprises:
step 1-1, defining 5*5 neighborhood convolution kernel templates as S, wherein the initial value of an S matrix is 0;
step (a)1-2, calculating a first neighborhood W of a window of a center f (x, y) of the current image 1 Median m of (2) 1 I.e. m 1 ＝median(W 1 ) Where mean is the median operation of the calculated vector, x is the horizontal coordinate, y is the vertical coordinate, W 1 The method comprises the following steps:
W 1 ＝[f(x-1,y)，f(x,y+1)，f(x,y)，f(x+1,y)，f(x,y-1)]
step 1-3, calculating a second neighborhood W of the current image center f (x, y) window 2 Median m of (2) 2 I.e. m 2 ＝median(W 2 )，W 2 The method comprises the following steps:
step 1-4, set convolution kernel template center pixel S (2, 2) =max (m 1 ,m 2 ) A neighborhood convolution kernel template S is obtained, where Max is the maximum operation of the calculated vector.
3. The method according to claim 2, wherein in step 2, the constructing a directional gradient edge operator includes:
respectively establishing a horizontal, vertical, main diagonal and secondary diagonal direction gradient structure operator U 1 、U 2 、U 3 、U 4 Directional gradient structure operator U i The method comprises the following steps:
where i=1, 2,3,4.
4. A method according to claim 3, wherein in step 2, the step of performing morphological direction gradient contour edge extraction on the Image2 comprises:
the morphological direction gradient contour edge extraction is carried out by adopting the following formula:
ZI i ＝f·U i -fΘU i ·L
wherein ZE i ZI is the result of rough extraction of the edge of the outer contour line i For the inner contour edge coarse extraction result, f is the Image2,for morphological dilation operation, Θ is morphological erosion operation,/->For morphological open operation, for morphological close operation, the structure operator L is:
5. the method according to claim 4, wherein in step 3, the extracting the contour detail edge obtains a unidirectional edge detection result, including:
extracting the contour detail edge by adopting the following formula to obtain a unidirectional edge detection result:
ZX i ＝Max(ZE i ,ZI i )-Min(ZE i ,ZI i )
Z i ＝ZE i +ZI i +ZX i
wherein ZX i For contour detail edges, Z i For the unidirectional edge detection result, min represents the minimum value of the calculation vector.
6. The method according to claim 5Step 4 is characterized in that it comprises: traversing the edge Image3, extracting pixel points with pixel values of 1 in the Image3 as seed pixel points, marking all foreground pixel points with adjacent 4 neighborhood or 8 neighborhood pixel values of 1 of the seed pixel points as L, and generating a connected domain contour Q by using all pixel forming areas with the same mark as L 1 Repeating the above steps until the whole image is traversed to obtain the final connected domain vector (Q 1 ,Q 2 ,...Q i ),i∈(1,2,3...)，Q i Representing the i-th connected domain contour.
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