CN113822352A - Infrared dim target detection method based on multi-feature fusion - Google Patents

Infrared dim target detection method based on multi-feature fusion Download PDF

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CN113822352A
CN113822352A CN202111078520.0A CN202111078520A CN113822352A CN 113822352 A CN113822352 A CN 113822352A CN 202111078520 A CN202111078520 A CN 202111078520A CN 113822352 A CN113822352 A CN 113822352A
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蔺素珍
武杰
禄晓飞
李大威
余东
张海松
樊小宇
赵亚丽
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Abstract

The invention provides an infrared small and weak target detection method based on multi-feature fusion. Secondly, detecting the target by calculating the distance between edge pixel points and central pixel points of the infrared dim target and the covariance of gray difference by utilizing the characteristic that the gray information of the infrared dim target accords with two-dimensional Gaussian distribution, and obtaining a first significant image. Thirdly, detecting the target by calculating the similarity factor by utilizing the characteristic that the infrared small target has low similarity with the neighborhood to obtain a second significant image. And finally, performing dot multiplication on the first significant map and the second significant map, obtaining a final significant map by fusing multiple characteristics of the infrared weak and small target, and calculating a simple threshold value of the final significant map and segmenting the final significant map to obtain a final detection result. The method effectively inhibits the complex clutter in the infrared image and simultaneously improves the precision of the detection of the weak and small targets.

Description

Infrared dim target detection method based on multi-feature fusion
Technical Field
The invention relates to the field of infrared image target detection, in particular to an infrared small and weak target detection method based on multi-feature fusion.
Background
The infrared target detection is one of core technologies of systems such as remote early warning and accurate guidance, and the like, wherein the infrared weak and small target detection is a classical difficult problem. The main difficulties are as follows: 1) the imaging distance is far, and the target is not only lack of characteristic information in the image but also is easily confused with noise; 2) the clutter edges with high intensity are easily mistakenly detected as targets, so that the false alarm is increased; 3) weak and small objects tend to be submerged in a complex background, causing serious overloads. Therefore, infrared detection of small and weak targets in a complex background has been a very challenging task.
Currently, few studies are being made on the detection of infrared small and weak targets. The related infrared small target detection methods can be broadly divided into data-driven methods represented by deep learning and model-driven methods based on mathematical and physical knowledge modeling. The data driving method is less applied to actual engineering due to the problems of lack of infrared weak and small target image sets, high training overhead, poor real-time performance and the like. The model-driven infrared small target detection method can be further divided into a single-frame method and a multi-frame method. However, the multi-frame method has limited effect in infrared weak and small target detection because an accurate motion model of the target cannot be obtained. Therefore, a high-performance single-frame method is more important in infrared small target detection.
From the analysis of the aspect of processing thinking, the single-frame detection method of the infrared small target comprises three types: one type of method can be called as a background recovery method, and the idea is to recover the background based on a sparse matrix and a low-rank matrix and then subtract the background image from the original image so as to achieve the purpose of highlighting the target. The second type is a background suppression method, which mainly adopts a filtering technology to suppress a uniform background and clutter. However, the method is very sensitive to background edges, and the use of the method in complex scenes such as sky target detection is restricted. The third type is a new method for suppressing the background and enhancing the target, which mainly utilizes the local contrast sensitivity characteristic of human vision to enhance the target by extracting the local contrast between the target and the background. Most methods have good detection effect in a scene with stable background change, but when the methods are used for detecting infrared weak and small targets in a complex scene, the high-intensity clutter edges in the complex background cannot be effectively inhibited, so that the false alarm rate is high. Therefore, the infrared small target detection method under the complex background has a larger development space.
Disclosure of Invention
The invention provides an infrared dim target detection method based on multi-feature fusion, aiming at the problems of low detection rate and high false alarm rate of infrared dim targets under a complex background, which is used for detecting infrared image dim targets under the complex background and effectively improving the accuracy and robustness of detection.
The invention adopts the following technical scheme: the infrared dim target detection method based on multi-feature fusion comprises the following steps: firstly, based on the characteristic that the local gray value of an infrared weak target is large, the gray contrast ratio of the target and a neighborhood background is utilized to enhance a real target and inhibit a part of complex backgrounds; secondly, detecting the target by calculating the distance between edge pixel points and central pixel points of the infrared dim target and the covariance of gray difference by utilizing the characteristic that the gray information of the infrared dim target accords with two-dimensional Gaussian distribution to obtain a first significant image; thirdly, detecting the target by calculating a similarity factor by utilizing the characteristic that the similarity between the infrared weak small target and the neighborhood is low to obtain a second significant image; and finally, performing dot multiplication on the first significant map and the second significant map, obtaining a final significant map by fusing multiple characteristics of the infrared weak and small target, and segmenting a calculation threshold of the final significant map to obtain a final detection result.
The infrared dim target detection method based on multi-feature fusion utilizes the target and the neighborsThe method specifically comprises the following steps of enhancing a real target by the gray contrast of a domain background and inhibiting a part of complex backgrounds: to-be-detected infrared image IinConverting the image into a gray image I; constructing a sliding window by taking a pixel point S in a gray level image I as a center, wherein the center unit is S0The cells S around itiIs a local background region; taking a plurality of cells with the maximum gray average value in the local background area, and calculating the gray average value mu of the cellsmax(ii) a Comparing the mean gray level of the center cell
Figure BDA0003262939290000021
And mumaxSize of (1), if
Figure BDA0003262939290000022
The central unit pixel is enhanced and the grey value is set to be I (I, j) × (mu)0/μmax)2I (I, j) represents the pixel grey value at the (I, j) position in the central cell, otherwise the inhibition is performed, the grey value is set to 0; and moving the sliding window until all pixels of the gray-scale image I are subjected to the calculation to obtain a background suppression image T.
The method for detecting the infrared dim target based on the multi-feature fusion detects the target by calculating the distance between the edge pixel point and the center pixel point of the infrared dim target and the covariance of the gray level difference, and the step of obtaining the first significant image specifically comprises the following steps: constructing a sliding window by taking a pixel point T in the background suppression image T as a center, and if the pixel value in the sliding window is not 0, calculating the distance from each pixel in the sliding window to the center pixel and the gray difference, thereby calculating the covariance coefficient of the whole sliding window; the sliding window is moved until all pixels of the background suppression image T are subjected to the above calculation, and a covariance significance map COV composed of covariance coefficients of each sliding window is obtained.
The method for detecting the infrared dim target based on the multi-feature fusion detects the target by calculating the similarity factor by utilizing the characteristic that the infrared dim target has low similarity with the neighborhood, and the method for detecting the infrared dim target based on the multi-feature fusion specifically comprises the following steps of: suppressing an image in an image T with a backgroundConstructing a sliding window by taking a prime point p as a center, wherein the central unit is called SM0S background units SM around itijIs a local background region; separately calculate the background units SMijAnd a central unit SM0Similarity factor between SFi(ii) a Moving the sliding window until all pixels of the background suppression image T are subjected to the calculation to obtain S similarity matrixes, wherein the ith similarity matrix consists of similarity factors of a central unit and an ith background unit; minimum min (SF) of elements at the same position in S similarity matricesi) As the values of the elements at the corresponding positions of the similarity matrix SF, the similarity matrix SF is calculated as follows:
Figure BDA0003262939290000031
wherein, min (SF)i) Minimum value of elements at the same position in S similarity matrices, max (SF)i) The maximum value of the elements at the same position in the S similarity matrixes is obtained; the similarity matrix SF is the similarity significance map SF.
The method for detecting the infrared dim target based on multi-feature fusion includes the steps of performing dot multiplication on a first significant map and a second significant map, obtaining a final significant map by fusing multiple characteristics of the infrared dim target, and segmenting a calculation threshold of the final significant map to obtain a final detection result, and specifically includes the following steps: performing dot multiplication operation on the covariance significant map and the similarity significant map to obtain a final detection significant map SM: SM ═ COV [ < SF >, calculates the division threshold Th, performs threshold division on the final detection significance map SM to obtain the final detection result map Iout。
In general, the technical scheme provided by the invention fully utilizes local prior information of the infrared dim target, and has the following technical characteristics and beneficial effects:
(1) since the shape of the infrared small target is isotropic, the background edges are anisotropic. They have a difference in characteristics and therefore require a fine-partitioning of the window, i.e. forming cells.
(2) The gray difference exists between the target and the neighborhood background, so the target needs to be enhanced by the gray difference between the target and the background, and the background needs to be suppressed.
(3) In order to separate the target, not only the gray scale comparison between the target and the background needs to be considered, but also the prior knowledge such as covariance values of gray scale differences and distance differences between the central pixel and the edge pixel of the sliding window, similarity factors and the like needs to be used for segmenting the target, so as to inhibit the background.
(4) The saliency map may still have certain clutter or noise, but the target has been significantly enhanced, so the residual false alarm is removed by segmentation with a constant false alarm rate method.
The novel method for detecting the complex background infrared small and weak target based on multi-feature fusion effectively inhibits complex clutter in an infrared image and improves the precision of detecting the small and weak target.
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FIG. 1 is an overall framework of the present invention;
FIG. 2 is a basic flow diagram of the present invention;
FIG. 3 is a graph of a sliding window distribution for calculating gray contrast according to the present invention;
FIG. 4 is a sliding window distribution plot for calculating a similarity factor according to the present invention;
FIG. 5(a) is an infrared original image according to an embodiment of the present invention;
FIG. 5(b) is a final detection significance map obtained by the algorithm proposed by the present invention according to the embodiment of the present invention;
FIG. 5(c) is a graph showing the results of the detection according to the embodiment of the present invention.
Detailed Description
The invention will be further elucidated with reference to the drawings and the detailed description:
referring to fig. 1 and fig. 2, a method for detecting an infrared weak and small target under a complex background in this embodiment includes the following steps:
step 1: inputting an infrared image I to be detected with the size of H multiplied by Win；
Step 2: image IinConverting the image into a gray image I;
step 3.1: a19 multiplied by 19 sliding window is constructed by taking a pixel point S in a gray level image I as a center and is divided into 9 units, and a central unit S0The size is 5 × 5, the background region cells S1, S3, S6, S8 are all 7 × 7 in size, S2, S7 are 5 × 7 in size, and S4, S5 are 7 × 5 in size, as shown in fig. 3;
step 3.2: calculating the pooling brightness of all background area units, namely the average value of all pixel gray levels in the unit, wherein the specific calculation method comprises the following steps:
Figure BDA0003262939290000041
wherein HijThe gray value of the jth pixel of the ith background area unit in the local background is represented, then muiExpressing the gray value mean value of the ith background area unit, and expressing the number of pixel points in an area block by N;
step 3.3: taking 4 units with the maximum gray average value in 8 units in the background area, and calculating the gray average value mu of the 4 unitsmaxThe specific solution is as follows:
Figure BDA0003262939290000042
wherein, MAX4(. represents the maximum four gray values, μ, takenmaxRepresents the mean value thereof;
step 3.4: comparing the mean gray level of the center cell
Figure BDA00032629392900000510
And mumaxSize of (1), if
Figure BDA00032629392900000511
The central unit pixel is enhanced and the grey value is set to be I (I, j) × (mu)0/μmax)2(I, j) represents the gray value of the pixel at the (I, j) position in the central cell), otherwise, the suppression is performed, the gray value is set to 0, and the specific calculation method is as follows:
Figure BDA0003262939290000052
step 3.5: moving the sliding window from top to bottom from left to right, wherein the step length is 5, until all pixels of the gray level image I are calculated through the steps 3.1-3.4, and obtaining a background suppression image T;
step 4.1: a 5 × 5 sliding window is constructed by taking a pixel point T in the background suppression image T as a center, if the pixel value in the sliding window is not 0, the distance and the gray difference between each pixel in the sliding window and the center pixel are calculated, so that the covariance coefficient of the whole sliding window is calculated, the coefficient is taken as the value of the center pixel of the sliding window, and the specific calculation method of the distance mean value and the gray difference mean value between each pixel in the sliding window and the center pixel is as follows:
Figure BDA0003262939290000053
Figure BDA0003262939290000054
wherein i and j respectively represent x and y direction coordinates of pixel points in the sliding window,
Figure BDA0003262939290000055
taking 3, I as the mean valueijIs the gray value of the pixel with coordinates of (i, j) point,
Figure BDA0003262939290000056
taking the gray value of the pixel as the gray value of the center pixel point, the invention takes I3,3N is the number of pixels in the sliding window, and 25 are taken;
step 4.2: the specific solution of the covariance coefficient of the sliding window is as follows:
Figure BDA0003262939290000057
Figure BDA0003262939290000058
Figure BDA0003262939290000061
wherein Cov (Dis, GV) represents a covariance coefficient of the sliding window, GV represents a gray value difference between a center point of the sliding window and an edge pixel point, Dis represents a distance difference between the center point of the sliding window and the edge pixel point, and SL is a side length of the sliding window, which is 5;
step 4.3: moving the sliding windows from top to bottom from left to right, wherein the step length is 1, until all pixels of the background suppression image T are calculated through the steps 4.1-4.2, and obtaining a covariance significant map COV consisting of covariance coefficients of each sliding window;
step 5.1: constructing a 25 × 25 sliding window by taking a pixel point p in the background suppression image T as a center, wherein the windows are all 9 units, and the central unit is called SM0The cells SM around itijIs a local background region, as in fig. 4;
step 5.2: similarity factors SF between the 8 background cells and the center cell are calculated respectivelyiThe specific solution is as follows:
Figure BDA0003262939290000062
in the formula, T represents a background suppression image, (s, T) represents a pixel point coordinate, and T (s, T) represents a pixel gray value of the point;
step 5.3: moving the sliding window from left to right from top to bottom by a step size of 1 until all pixels of the image T are calculated in the above step 5.2, obtaining 8 similarity matrices, wherein an ith (i ═ 1,2, …,8) similarity matrix is composed of similarity factors of a central unit and an ith background unit;
step 5.4: taking the minimum value MIN (SF) of elements at the same position in 8 similarity matricesi) As the value of the element at the corresponding position of the similarity matrix SF, the specific calculation method of the similarity saliency map SF is as follows:
Figure BDA0003262939290000063
step 6: performing dot multiplication operation on the covariance significant map COV and the similarity significant map SF to obtain a final detection significant map:
SM＝COV⊙SF
and 7: calculating a segmentation threshold Th by using a formula, and performing threshold segmentation on the significance map SM to obtain a final detection result map Iout:
Th＝μ+λ×σ
Wherein mu and sigma are the mean and standard deviation of the final significant map SM, and lambda is a fixed parameter, and the invention takes 4.

Claims (5)

1. The infrared dim target detection method based on multi-feature fusion is characterized by comprising the following steps: the method comprises the following steps: firstly, based on the characteristic that the local gray value of an infrared weak target is large, the gray contrast ratio of the target and a neighborhood background is utilized to enhance a real target and inhibit a part of complex backgrounds; secondly, detecting the target by calculating the distance between edge pixel points and central pixel points of the infrared dim target and the covariance of gray difference by utilizing the characteristic that the gray information of the infrared dim target accords with two-dimensional Gaussian distribution to obtain a first significant image; thirdly, detecting the target by calculating a similarity factor by utilizing the characteristic that the similarity between the infrared weak small target and the neighborhood is low to obtain a second significant image; and finally, performing dot multiplication on the first significant map and the second significant map, obtaining a final significant map by fusing multiple characteristics of the infrared weak and small target, and segmenting a calculation threshold of the final significant map to obtain a final detection result.
2. The infrared small and weak target detection method based on multi-feature fusion as claimed in claim 1, characterized in that: the method for enhancing the real target and inhibiting part of the complex background by utilizing the gray contrast of the target and the neighborhood background specifically comprises the following steps: to-be-detected infrared image IinConverting the image into a gray image I; construction of slide by taking one pixel point s in gray level image I as centerA movable window with a central unit of S0The cells S around itiIs a local background region; taking a plurality of cells with the maximum gray average value in the local background area, and calculating the gray average value mu of the cellsmax(ii) a Comparing the mean gray level of the center cell
Figure FDA0003262939280000011
And mumaxSize of (1), if
Figure FDA0003262939280000012
The central unit pixel is enhanced and the grey value is set to be I (I, j) × (mu)0/μmax)2I (I, j) represents the pixel grey value at the (I, j) position in the central cell, otherwise the inhibition is performed, the grey value is set to 0; and moving the sliding window until all pixels of the gray-scale image I are subjected to the calculation to obtain a background suppression image T.
3. The infrared small and weak target detection method based on multi-feature fusion as claimed in claim 2, characterized in that: the method comprises the following steps of detecting a target by calculating the distance between edge pixel points and central pixel points of an infrared weak and small target and the covariance of gray level difference to obtain a first significant image: constructing a sliding window by taking a pixel point T in the background suppression image T as a center, and if the pixel value in the sliding window is not 0, calculating the distance from each pixel in the sliding window to the center pixel and the gray difference, thereby calculating the covariance coefficient of the whole sliding window; the sliding window is moved until all pixels of the background suppression image T are subjected to the above calculation, and a covariance significance map COV composed of covariance coefficients of each sliding window is obtained.
4. The infrared small and weak target detection method based on multi-feature fusion as claimed in claim 3, characterized in that: the method specifically comprises the following steps of detecting the target by calculating the similarity factor by utilizing the characteristic that the similarity between the infrared small target and the neighborhood is low: sliding is constructed by taking one pixel point p in background suppression image T as centerWindow, center unit called SM0S background units SM around itijIs a local background region; separately calculate the background units SMijAnd a central unit SM0Similarity factor between SFi(ii) a Moving the sliding window until all pixels of the background suppression image T are subjected to the calculation to obtain S similarity matrixes, wherein the ith similarity matrix consists of similarity factors of a central unit and an ith background unit; minimum min (SF) of elements at the same position in S similarity matricesi) As the values of the elements at the corresponding positions of the similarity matrix SF, the similarity matrix SF is calculated as follows:
Figure FDA0003262939280000021
wherein, min (SF)i) Minimum value of elements at the same position in S similarity matrices, max (SF)i) The maximum value of the elements at the same position in the S similarity matrixes is obtained; the similarity matrix SF is the similarity significance map SF.
5. The infrared small and weak target detection method based on multi-feature fusion as claimed in claim 4, characterized in that: performing point multiplication on the first significant map and the second significant map, obtaining a final significant map by fusing multiple characteristics of the infrared weak and small target, and segmenting a calculation threshold of the final significant map to obtain a final detection result, wherein the method specifically comprises the following steps: performing dot multiplication operation on the covariance significant map and the similarity significant map to obtain a final detection significant map SM: SM ═ COV [ < SF >, calculates the division threshold Th, performs threshold division on the final detection significance map SM to obtain the final detection result map Iout。
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