CN116953674A - Rapid target detection algorithm in sonar imaging - Google Patents
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/52—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S15/00
- G01S7/523—Details of pulse systems
- G01S7/526—Receivers
- G01S7/527—Extracting wanted echo signals
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- G—PHYSICS
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- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
- G01S15/88—Sonar systems specially adapted for specific applications
- G01S15/89—Sonar systems specially adapted for specific applications for mapping or imaging
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- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/52—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S15/00
- G01S7/534—Details of non-pulse systems
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/52—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S15/00
- G01S7/539—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S15/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
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Abstract
The invention relates to a rapid target detection algorithm in sonar imaging, which belongs to the technical field of sonar imaging and comprises the steps of data acquisition, data preprocessing, beam forming, sonar image denoising, target detection tracking and the like. The method and the steps for converting the received acoustic echo signals into the sonar images by the rapid target detection algorithm in the sonar imaging not only remove noise, but also retain structural information such as textures of the images, realize the balance of denoising and retaining details, improve the detection rate by utilizing the principle of an integral graph, and reduce the detection false alarm rate.
Description
Technical Field
The invention relates to the technical field of sonar imaging, in particular to a rapid target detection algorithm in sonar imaging.
Background
Because of the increasing opening of international situation, competition among countries is also increasingly vigorous, ocean resources and ports are always the focus of international disputes due to the importance of the ocean resources and ports, so that the underwater safety problem of the offshore areas of the countries is particularly prominent, sonar technology is widely applied to the fields of modern military, ocean exploration, threat identification and the like, the sonar technology is used as an important method for underwater exploration, imaging of underwater exploration environment is carried out by utilizing a sonar system, the construction of an underwater monitoring system of the ports of the offshore areas is facilitated, and corresponding technical support is provided for the underwater safety of the countries.
When an underwater operation type aircraft executes an underwater operation task, the surrounding environment needs to be perceived, the multi-beam forward-looking sonar can detect a front fan-shaped area, but because of the influence of factors such as water flow disturbance, foreign matter interference and the like, particularly under the working conditions of near bottom, near water surface and near shore, reverberation and side lobe effects are serious, so that sonar image noise is serious, more accurate image perception data cannot be directly obtained, therefore, only a single picture is processed, misjudgment is easy to be carried out, each picture is processed, the pressure of a processor is easy to be increased, and a rapid target detection algorithm in sonar imaging is provided to solve the problems.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a rapid target detection algorithm in sonar imaging, has the advantages of high-precision target detection, capability of reducing the detection false alarm rate and the like, and solves the problems that the prior art is influenced by factors such as water flow disturbance, foreign matter interference and the like, particularly under the working conditions of near bottom, near water surface and near shore, reverberation and sidelobe effect are serious, so that sonar image noise is serious, more accurate image sensing data cannot be directly obtained, only a single picture is easy to process, misjudgment is easy to occur, each picture is processed, and the pressure of a processor is easy to increase, thereby reducing the target detection precision.
In order to achieve the above purpose, the present invention provides the following technical solutions: the fast target detection algorithm in sonar imaging comprises the following steps:
step 1: data acquisition, namely receiving acoustic echo signals by a sonar equipment receiving matrix;
step 2: data preprocessing, namely performing data format conversion, quadrature modulation, matched filtering and downsampling on the received echo signals;
step 3: beamforming, namely obtaining directivity in a preset direction by processing the received data of each array element, responding to a signal in a certain direction of a space, and inhibiting directivity in other directions;
step 4: denoising the sonar image, and removing noise and clutter in the sonar image by using a self-adaptive K-SVD method;
step 5: and (3) target detection tracking, namely performing threshold segmentation processing by utilizing an integral graph principle according to the characteristic that the echo intensity of a target in a sonar image is larger than the background echo intensity to obtain a preliminary detection target position, detecting continuous sonar image frames, and removing a false target to obtain a final target detection result.
Preferably, the data format conversion in step 2 is used for performing format conversion of data on the read binary bin file, that is, converting binary codes into decimal data, so that the decimal data is convenient for display processing, and meanwhile, the binary data is saved as a two-dimensional data matrix for display.
The quadrature modulation in the step 2 is used for performing quadrature demodulation on the received acoustic echo signal to obtain a complex signal form thereof, extracting the complex envelope of the signal, and the matched filtering in the step 2 is used for improving the signal-to-noise ratio of the sampled data signal.
The beam forming in the step 3 is to perform different weighting and delay on the received data, and sum the received data in different preset directions to form directivity, and the calculation process is as follows:
the receiving matrix is a linear matrix with equally-spaced matrix elementsN) The included angle between the incidence direction of the echo signal and the normal direction of the matrix isθThe array element interval isdThentThe output of each array element at the moment is as follows:
in the method, in the process of the invention,φis the phase difference between the received signals of adjacent array elements, which is calculated as follows:
。
the output of the matrix is as follows:
in the method, in the process of the invention,Nrepresenting the number of array elements of the receiving array,trepresenting the time delay of echo signals between adjacent array elements,θindicating the direction of incidence of the plane wave,φthe real part of the sum formula is used for simplifying the phase difference between the received signals of adjacent array elements as follows:
。
the self-adaptive K-SVD method in the step 4 is divided into two stages:
the first stage is sparse coding: assume thatFor an overcomplete dictionary, for the source image +.>And (3) performing sparse representation decomposition processing, wherein a redundant sparse representation model is as follows:
in the method, in the process of the invention,Xthe sparse representation coefficients representing the image are presented,Lis the maximum sparsity, and the sparse coding matrix is calculated by using an orthogonal matching pursuit algorithmXObtainingXThen, the second stage is entered:
the second stage is dictionary learning: if the matrix D and the matrix X are represented by column vectors and row vectors, respectively, the following formula is:
。
and (3) threshold segmentation processing in the step (5), determining a principle of pixel gray value imaging according to echo intensity aiming at a sonar image, wherein points with foreground estimated values larger than background estimated values belong to points of interest, reserving all the points of interest, and determining a threshold value of preliminary target detection according to the points of interest.
The removal of the false target in the step 5 is divided into the following two steps:
1) Respectively removing false alarms according to the width and the height of the target area;
2) In the thresholding process, the shrinkage or block dispersion phenomenon of the target area may occur, the areas of the targets in the image are accumulated, the result and the value are compared, and the points meeting the conditions are reserved, so that the purpose of further removing the pseudo targets is achieved, and the final detection target is obtained in the detection area.
Compared with the prior art, the invention provides a rapid target detection algorithm in sonar imaging, which has the following beneficial effects:
the method comprises the steps of preprocessing data, combining multi-channel received data by utilizing a beam forming technology to generate echo data in each designated direction, correlating the intensity of the echo signal with the brightness of a pixel value, finally converting the acoustic echo signal into a sonar image, removing noise and clutter in the sonar image by utilizing a self-adaptive K-SVD method, finally filtering a preliminary denoising result by utilizing an original image, recovering texture information to obtain a final denoising image, and realizing the balance of denoising and retaining details by utilizing an integral graph principle.
Drawings
FIG. 1 is a flow chart of a rapid object detection process of the present invention;
FIG. 2 is a block diagram of an adaptive K-SVD method in the present invention;
FIG. 3 is a schematic block diagram of an integrated circuit in accordance with the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-3, a fast target detection algorithm in sonar imaging includes the following steps:
step 1: data acquisition, namely receiving acoustic echo signals by a sonar equipment receiving matrix;
step 2: data preprocessing, namely performing data format conversion, quadrature modulation, matched filtering and downsampling on the received echo signals;
step 3: beamforming, namely obtaining directivity in a preset direction by processing the received data of each array element, responding to a signal in a certain direction of a space, and inhibiting directivity in other directions;
step 4: denoising the sonar image, and removing noise and clutter in the sonar image by using a self-adaptive K-SVD method;
step 5: and (3) target detection tracking, namely performing threshold segmentation processing by utilizing an integral graph principle according to the characteristic that the echo intensity of a target in a sonar image is larger than the background echo intensity to obtain a preliminary detection target position, detecting continuous sonar image frames, and removing a false target to obtain a final target detection result.
In this embodiment, the data format conversion in step 2 is used to perform format conversion of data on the read binary bin file, that is, converting binary codes into decimal data, so that display processing is facilitated, and the binary data is saved as a two-dimensional data matrix for display.
In this embodiment, the quadrature modulation in step 2 is used to perform quadrature demodulation on the received acoustic echo signal to obtain a complex signal form thereof, extract the complex envelope of the signal, and the matched filtering in step 2 is used to improve the signal-to-noise ratio of the sampled data signal.
In this embodiment, the beamforming in step 3 performs different weighting and delay on the received data, and sums the received data in different predetermined directions to form directivity, and the calculation process is as follows:
the receiving matrix is a linear matrix with equally-spaced matrix elementsN) The incidence direction of echo signals and the normal direction of the matrixThe included angle isθThe array element interval isdThentThe output of each array element at the moment is as follows:
in the method, in the process of the invention,φis the phase difference between the received signals of adjacent array elements, which is calculated as follows:
。
in this embodiment, the output of the matrix is as follows:
in the method, in the process of the invention,Nrepresenting a receive matrix is used for the number of array elements,trepresenting the time delay of echo signals between adjacent array elements,θindicating the direction of incidence of the plane wave,φthe real part of the sum formula is used for simplifying the phase difference between the received signals of adjacent array elements as follows:
。
in this embodiment, the adaptive K-SVD method in step 4 is divided into two stages:
the first stage is sparse coding: assume thatFor an overcomplete dictionary, for the source image +.>And (3) performing sparse representation decomposition processing, wherein a redundant sparse representation model is as follows:
in the method, in the process of the invention,Xthe sparse representation coefficients representing the image are presented,Lis the maximum sparsity, and the sparse coding matrix is calculated by using an orthogonal matching pursuit algorithmXObtainingXThen, the second stage is entered:
the second stage is dictionary learning: if the matrix D and the matrix X are represented by column vectors and row vectors, respectively, the following formula is:
。
in this embodiment, the implementation steps of the adaptive K-SVD method are as follows:
1) Performing mean filtering on the sonar image to obtain a cartoon-like image (low-frequency component), subtracting the cartoon-like image from the source image to obtain a residual image (high-frequency component), and performing experiment to verify that the noise component in the high-frequency component accords with the additive generalized Gaussian distribution, fitting the noise component by using normal distribution, and estimating the noise variance in the residual image;
2) Removing a generalized Gaussian noise component from the high-frequency component by using a self-adaptive K-SVD method, and combining the denoised high-frequency component with the low-frequency component obtained in the first step to obtain a preliminary denoised image;
3) And (3) performing guide filtering on the result obtained in the step (2) by utilizing the sonar source image to recover the edge information of the image, so as to obtain a final edge-preserving denoising sonar image.
In this embodiment, the threshold segmentation processing in step 5 determines, according to the principle of imaging the pixel gray value according to the echo intensity, for the sonar image, points with foreground estimation values greater than background estimation values belong to points of interest, all the points of interest are reserved, and a threshold value for preliminary target detection is determined according to the points of interest.
In the present embodiment, the removal of the false object in step 5 is divided into the following two steps:
1) And respectively removing false alarms according to the width and the height of the target area, wherein the removing method comprises the following steps:
in the method, in the process of the invention,the minimum and maximum values of the width and height of the target area, respectively.
2) In the thresholding process, the shrinkage or block dispersion phenomenon of the target area may occur, the areas of the targets in the image are accumulated, the result and the value are compared, and the points meeting the conditions are reserved, so that the purpose of further removing the pseudo targets is achieved, and the final detection target is obtained in the detection area.
In this embodiment, the detection rate, the position deviation (including the horizontal direction deviation and the vertical direction deviation) and the time four objective indexes are adopted to evaluate the detection rate, the position deviation and the time, and the meaning and the calculation method of the objective indexes are as follows:
1) Detection rate [ ]DP) The larger the value of the detection effect used for the beam algorithm is, the better the detection effect is, and the DP is defined as follows:
in the method, in the process of the invention,Crepresenting the number of image frames in which the algorithm detects the target,Nrepresenting the total number of sonar image frames required to be detected.
2) The position deviation adopts average absolute value deviation in horizontal and vertical directions for measuring the target position, and the calculation method comprises the following steps:
wherein, in the formula, wherein,Drepresenting the deviation of the position of the absolute value,Trepresenting the position of the object that is detected,Ras an actual position of the object,irepresenting the direction [ (]HRepresenting the horizontal direction of the vehicle,Vrepresenting the vertical direction);Nthe number of frames representing the image used,Drepresenting the mean absolute position deviation.
3) The detection time is used for measuring the real-time performance of the algorithm, and the shorter the algorithm detection time is, the better the real-time performance is.
In this embodiment, the implementation steps using the principle of the integral diagram are as follows:
1) Firstly, calculating a sonar original image to obtain a corresponding integral image;
2) Estimating the background and the foreground by utilizing integral image calculation of the sonar image, comparing the estimation results, and further determining the gray threshold of the detection target according to the comparison results;
3) Comparing the sonar original image with the gray threshold value obtained in the step 2) to obtain a potential target image;
4) And under the condition that false alarms exist in the potential target graph, removing the false targets by utilizing the geometric features of the targets to obtain a final target detection graph.
In conclusion, the method and the steps for converting the received acoustic echo signals into the sonar images by the rapid target detection algorithm in the sonar imaging comprise the steps of preprocessing data, combining the multichannel received data by utilizing a beam forming technology to generate echo data in each appointed direction, then correlating the intensity of the echo signals with the brightness of pixel values, finally converting the acoustic echo signals into the sonar images, removing noise and clutters in the sonar images by utilizing an adaptive K-SVD method, finally filtering a preliminary denoising result by utilizing an original image, recovering texture information to obtain the final denoising image, and realizing the balance of denoising and retaining details by utilizing an integral graph principle, simultaneously improving the detection rate, reducing the detection false alarm rate, improving the target detection effect.
The related modules involved in the system are all hardware system modules or functional modules in the prior art combining computer software programs or protocols with hardware, and the computer software programs or protocols involved in the functional modules are all known technologies for those skilled in the art and are not improvements of the system; the system is improved in interaction relation or connection relation among the modules, namely, the overall structure of the system is improved, so that the corresponding technical problems to be solved by the system are solved.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (8)
1. The rapid target detection algorithm in sonar imaging is characterized by comprising the following steps of:
step 1: data acquisition, namely receiving acoustic echo signals by a sonar equipment receiving matrix;
step 2: data preprocessing, namely performing data format conversion, quadrature modulation, matched filtering and downsampling on the received echo signals;
step 3: beamforming, namely obtaining directivity in a preset direction by processing the received data of each array element, responding to a signal in a certain direction of a space, and inhibiting directivity in other directions;
step 4: denoising the sonar image, and removing noise and clutter in the sonar image by using a self-adaptive K-SVD method;
step 5: and (3) target detection tracking, namely performing threshold segmentation processing by utilizing an integral graph principle according to the characteristic that the echo intensity of a target in a sonar image is larger than the background echo intensity to obtain a preliminary detection target position, detecting continuous sonar image frames, and removing a false target to obtain a final target detection result.
2. A rapid object detection algorithm in sonar imaging according to claim 1, wherein the data format conversion in step 2 is used for converting the read binary bin file into data format, that is, converting the binary code into decimal data for convenient display processing, and storing the binary data as a two-dimensional data matrix for display.
3. The rapid target detection algorithm in sonar imaging according to claim 1, wherein the quadrature modulation in step 2 is used for performing quadrature demodulation on the received acoustic echo signal to obtain a complex signal form thereof, extracting a complex envelope of the signal, and the matched filtering in step 2 is used for improving the signal-to-noise ratio of the sampled data signal.
4. A rapid target detection algorithm in sonar imaging according to claim 1, wherein the beamforming in step 3 is implemented by performing different weights and delays on the received data, and summing the received data in different predetermined directions to form directivity, and the calculation process is as follows:
the receiving matrix is a linear matrix with equally-spaced matrix elementsN) The included angle between the incidence direction of the echo signal and the normal direction of the matrix isθThe array element interval isdThentThe output of each array element at the moment is as follows:
in the method, in the process of the invention,φis the phase difference between the received signals of adjacent array elements, which is calculated as follows:
。
5. a rapid object detection algorithm in sonar imaging according to claim 3, wherein the output of the matrix is of the formula:
in the method, in the process of the invention,Nrepresenting the number of array elements of the receiving array,trepresenting the time delay of echo signals between adjacent array elements,θindicating the direction of incidence of the plane wave,φthe real part of the sum formula is used for simplifying the phase difference between the received signals of adjacent array elements as follows:
。
6. a fast object detection algorithm in sonar imaging according to claim 1, wherein the adaptive K-SVD method in step 4 is divided into two phases:
the first stage is sparse coding: assume thatFor an overcomplete dictionary, for the source image +.>And (3) performing sparse representation decomposition processing, wherein a redundant sparse representation model is as follows:
in the method, in the process of the invention,Xthe sparse representation coefficients representing the image are presented,Lis the maximum sparsity, and the sparse coding matrix is calculated by using an orthogonal matching pursuit algorithmXObtainingXThen, the second stage is entered:
the second stage is dictionary learning: if the matrix D and the matrix X are represented by column vectors and row vectors, respectively, the following formula is:
。
7. the rapid target detection algorithm in sonar imaging according to claim 1, wherein the threshold segmentation process in step 5 determines the principle of pixel gray value imaging according to echo intensity for a sonar image, points with foreground estimation values larger than background estimation values belong to points of interest, all the points of interest are reserved, and a threshold value for preliminary target detection is determined according to the points of interest.
8. A rapid object detection algorithm in sonar imaging according to claim 1, wherein said removing of false objects in step 5 is divided into two steps:
1) Respectively removing false alarms according to the width and the height of the target area;
2) In the thresholding process, the shrinkage or block dispersion phenomenon of the target area may occur, the areas of the targets in the image are accumulated, the result and the value are compared, and the points meeting the conditions are reserved, so that the purpose of further removing the pseudo targets is achieved, and the final detection target is obtained in the detection area.
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| CN117538881A (en) * | 2024-01-10 | 2024-02-09 | 海底鹰深海科技股份有限公司 | Sonar water imaging beam forming method, system, equipment and medium |
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| CN117538881B (en) * | 2024-01-10 | 2024-05-07 | 海底鹰深海科技股份有限公司 | Sonar water imaging beam forming method, system, equipment and medium |
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