A quick method for judging the suspicious target in the field of view of side scan sonar image

Abstract

The application discloses a quick discrimination method for side scan sonar image field of view target suspicion, and relates to the technical field of image discrimination, comprising the following steps: S1, acquiring side scan sonar image data to generate an original image matrix; S2, performing two-dimensional discrete cosine transform operation based on the original image matrix to generate a transform coefficient matrix; S3, calculating the logarithmic transform of the coefficient absolute value based on the transform coefficient matrix to generate an intensity feature matrix; S4, extracting a plurality of regional feature matrices according to a preset segmentation rule based on the dimension information of the intensity feature matrix; S5, calculating the feature statistical value of each region based on the plurality of regional feature matrices to generate a regional feature value sequence; and S6, calculating the ratio relationship between the feature values based on the regional feature value sequence and generating a target existence judgment result according to the ratio judgment rule. According to the ratio of the feature values of different regions, whether the side scan sonar image exists target objects can be quickly and effectively discriminated.

Description

Technical Field

[0001] This invention relates to the field of image discrimination technology, and in particular to a rapid method for discriminating suspicious targets in the field of view of side-scan sonar images. Background Technology

[0002] Side-scan sonar, as an important underwater acoustic detection device, plays a vital role in underwater geological and geomorphological surveys, target search, obstacle detection, and marine 3D model inversion.

[0003] However, existing side-scan sonar image processing techniques have significant drawbacks. Current image segmentation methods primarily process partial regions from the overall image, resulting in extremely low image resolution and severely impacting subsequent image segmentation and target recognition. Local images directly extracted from side-scan sonar images generally suffer from overly concentrated pixel areas and poor image resolution. Even after gain correction, the image quality improvement remains unsatisfactory, failing to meet the demands for fast and accurate target recognition.

[0004] Therefore, there is an urgent need for a rapid method to identify suspicious targets in the field of view of side-scan sonar images. Summary of the Invention

[0005] This invention provides a rapid method for identifying suspicious targets in the field of view of side-scan sonar images, in order to solve the above-mentioned problems existing in the prior art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A rapid method for identifying suspicious targets in the field of view of side-scan sonar images includes:

[0008] S1: Acquire side-scan sonar image data and generate the original image matrix;

[0009] S2: Based on the original image matrix, perform a two-dimensional discrete cosine transform operation to generate a transform coefficient matrix;

[0010] S3: Based on the transformation coefficient matrix, calculate the logarithmic transformation of the absolute values ​​of the coefficients to generate the intensity feature matrix;

[0011] S4: Based on the dimensional information of the intensity feature matrix, extract multiple region feature matrices according to preset segmentation rules;

[0012] S5: Based on multiple regional feature matrices, calculate the feature statistics of each region and generate a sequence of regional feature values;

[0013] S6: Based on the regional feature value sequence, calculate the ratio relationship between feature values, and generate the target existence determination result according to the ratio determination rule.

[0014] Step S2 includes:

[0015] S21: Receive pixel data of the original image matrix;

[0016] S22: Perform a one-dimensional discrete cosine transform on the pixel data in the row direction to generate an intermediate matrix of the row transform;

[0017] S23: Based on the row transformation intermediate matrix, perform a one-dimensional discrete cosine transformation in the column direction to generate a transformation coefficient matrix.

[0018] Step S3 includes:

[0019] S31: Extract the coefficient values ​​at each position in the transformation coefficient matrix;

[0020] S32: Perform absolute value operation on each coefficient value to generate an absolute value matrix;

[0021] S33: Based on the absolute value matrix, perform logarithmic operations on each element to generate an intensity feature matrix.

[0022] Step S4 includes:

[0023] S41: Obtain the number of rows and columns of the intensity feature matrix;

[0024] S42: Select the smaller value between the number of rows and the number of columns as the splitting reference value;

[0025] S43: Determine the region size parameters according to a preset ratio based on the segmentation reference value;

[0026] S44: Based on the region size parameter, extract region feature matrices of the same size from the upper left, middle and lower right corners of the intensity feature matrix.

[0027] Step S5 includes:

[0028] S51: Receives multiple region feature matrices;

[0029] S52: Perform a summation operation on the elements within the feature matrix of each region to generate the region sum value;

[0030] S53: Divide the sum of each region by the number of elements in the corresponding region to generate a sequence of region feature values.

[0031] Step S6 includes:

[0032] S61: Extract the first feature value f1, the second feature value f2, and the third feature value f3 from the region feature value sequence;

[0033] S62: Calculate the ratio R1 of the first eigenvalue to the second eigenvalue, where R1 = f1 / f2;

[0034] S63: Calculate the ratio R2 of the first eigenvalue to the third eigenvalue, where R2 = f1 / f3;

[0035] S64: Perform a decision operation based on the ratios R1 and R2 to generate a result determining the existence of the target.

[0036] The decision operation in step S64 includes:

[0037] Operation condition 1: When R1 is greater than or equal to the first threshold T1, the result is that the target object exists;

[0038] Operation condition 2: When R1 is greater than or equal to the second threshold T2 and less than the first threshold T1, and R2 is greater than or equal to the third threshold T3, the result is that the target object exists.

[0039] When neither operation condition one nor operation condition two is satisfied, the result is that the target object does not exist.

[0040] Among them, T1>T2>1, T3>1.

[0041] Step S44 includes:

[0042] S441: Take the top left corner of the intensity feature matrix as the starting position of the first region;

[0043] S442: Based on the number of rows M and columns N of the intensity feature matrix and the region size parameter d, the starting position of the central region is calculated as ((Md) / 2, (Nd) / 2);

[0044] S443: Calculate the starting position of the lower right corner region so that the lower right boundary of the region is aligned with the lower right boundary of the intensity feature matrix.

[0045] Step S43 includes:

[0046] S431: Receive segmentation reference value m;

[0047] S432: Divide the segmentation reference value by the preset segmentation coefficient K to generate the region side length value d, where d=m / K, and the preset segmentation coefficient K is 3;

[0048] S433: Use the region side length value d as the region size parameter for subsequent region extraction.

[0049] Compared with the prior art, the present invention has the following advantages:

[0050] This invention proposes an eigenvalue ratio discrimination algorithm based on two-dimensional cosine transform. It uses the coefficient matrix of the two-dimensional cosine transform of side-scan sonar as the feature matrix, selects the average coefficient of a specific region as the eigenvalue, and quickly and effectively determines whether there is a target object in the side-scan sonar image based on the ratio of the eigenvalues ​​of different regions.

[0051] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention.

[0052] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0053] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0054] Figure 1 This is a flowchart of a method for rapid identification of suspicious targets in the field of view of a side-scan sonar image, according to an embodiment of the present invention.

[0055] Figure 2 These are side-scan sonar images of the sunken ship in an embodiment of the present invention;

[0056] Figure 3 The F1, F2, and F3 feature matrices represent the regions in this embodiment of the invention;

[0057] Figure 4 This is a side-scan sonar image without a target in an embodiment of the present invention;

[0058] Figure 5 This is the coefficient intensity image 1 in the embodiment of the present invention;

[0059] Figure 6 These are side-scan sonar images of the sunken ship in an embodiment of the present invention;

[0060] Figure 7 Image 2 showing the coefficient intensity in this embodiment of the invention;

[0061] Figure 8 These are side-scan sonar images of the sunken ship in an embodiment of the present invention;

[0062] Figure 9 Image 3 is the coefficient intensity image in this embodiment of the invention;

[0063] Figure 10 This is a side-scan sonar image without a target in an embodiment of the present invention;

[0064] Figure 11 Image 4 shows the coefficient intensity in this embodiment of the invention. Detailed Implementation

[0065] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0066] The embodiments of the present invention provide, as follows Figure 1 As shown, a rapid method for identifying suspicious targets in the field of view of a side-scan sonar image includes:

[0067] S1: Acquire side-scan sonar image data and generate the original image matrix;

[0068] S2: Based on the original image matrix, perform a two-dimensional discrete cosine transform operation to generate a transform coefficient matrix;

[0069] S3: Based on the transformation coefficient matrix, calculate the logarithmic transformation of the absolute values ​​of the coefficients to generate the intensity feature matrix;

[0070] S4: Based on the dimensional information of the intensity feature matrix, extract multiple region feature matrices according to preset segmentation rules;

[0071] S5: Based on multiple regional feature matrices, calculate the feature statistics of each region and generate a sequence of regional feature values;

[0072] S6: Based on the regional feature value sequence, calculate the ratio relationship between feature values, and generate the target existence determination result according to the ratio determination rule.

[0073] The working principle and beneficial effects of the above technical solution are as follows: In step S1, the system acquires the acoustic echo signal of the underwater target area through the side-scan sonar device, converts the received analog acoustic signal into digital pixel data through the analog-to-digital converter, and arranges these digital data into an M×N dimensional original image matrix according to the two-dimensional coordinate relationship of the scan line sequence and the range gate, where M represents the number of scan lines and N represents the number of sampling points on each scan line.

[0074] In step S2, a two-dimensional discrete cosine transform operation is performed on the original image matrix. First, a one-dimensional DCT calculation is performed on each row of the matrix. The cosine basis function is used to decompose the spatial signal in the frequency domain, converting the pixel intensity information in the time domain into the coefficient representation in the frequency domain. Then, this process is repeated in the column direction, and finally a transform coefficient matrix Co with the same size as the original image is obtained. This matrix reflects the energy distribution characteristics of the image in different frequency components.

[0075] In step S3, for each complex coefficient at each position in the transform coefficient matrix, first calculate its absolute value to obtain amplitude information, eliminating the influence of phase on subsequent analysis. Then, perform a natural logarithm transformation on each absolute value, and generate an intensity feature matrix I using the formula I = log(abs(Co)). This logarithmic transformation can compress the dynamic range and highlight the differences in low-frequency components.

[0076] In step S4, obtain the number of rows M and the number of columns N of the intensity feature matrix I, select min(M,N) as the segmentation reference value m, divide this reference value by the preset segmentation coefficient K = 3 to obtain the region side length d = m / 3, and extract sub-matrices with a size of d×d from the upper-left region (starting position (0,0)), the middle region (starting position ((M - d) / 2, (N - d) / 2)), and the lower-right region (aligning its lower-right boundary with the matrix boundary) on the intensity feature matrix as the feature matrices F1, F2, and F3, that is: take matrices with sizes of upper-left m / 3, middle m / 3, and lower-right m / 3 as the feature matrices F1, F2, and F3 respectively.

[0077] In step S5, perform an accumulation summation operation on all elements within each region feature matrix, divide the obtained region sum value by the total number of elements d² in this region, and calculate the average coefficient values f1, f2, and f3 of each region, forming a region eigenvalue sequence. These eigenvalues reflect the degree of frequency domain energy concentration of the image at different spatial positions.

[0078] In step S6, calculate the ratio R1 = f1 / f2 of the first eigenvalue to the second eigenvalue, and the ratio R2 = f1 / f3 of the first eigenvalue to the third eigenvalue, and perform target existence evaluation according to the preset determination rule: directly determine that a target object exists when R1 ≥ T1 (the first threshold); also determine that a target object exists when T2 ≤ R1 < T1 and R2 ≥ T3; otherwise, determine that no target object exists, where the threshold relationship is T1 > T2 > 1, T3 > 1.

[0079] Among them, the recognition of the image target object is carried out by a side-scan sonar. Perform a two-dimensional discrete cosine transform on the image to obtain the image discrete cosine transform coefficient matrix Co; take the coefficient intensity matrix I = log(abs(Co)), and take the smaller value of the number of rows and columns of the matrix I as m; take matrices with sizes of upper-left m / 3, middle m / 3, and lower-right m / 3 as the feature matrices F1, F2, and F3 respectively; take the average values f1, f2, and f3 of the feature matrices F1, F2, and F3 as eigenvalues;

[0080] If , it is determined that there is a target object in the image; if , it is determined that there is a target object in the image; otherwise, it is determined that there is no target object in the image.

[0081] For example Figure 2 The side-scan sonar image of the shipwreck shown is subjected to a two-dimensional discrete cosine transform to obtain a discrete cosine transform coefficient matrix Co with the same size as the image; the coefficient intensity matrix I = log (abs(Co)) is then taken, and its intensity image is shown below. Figure 3 As shown, the size is 205×189, so the smaller value of the number of rows and columns in the matrix, 189, is taken as the segmentation m value; the matrices of size 63×63 in the upper left, middle, and lower right are taken as feature matrices F1, F2, and F3 respectively; the average values ​​of feature matrices F1, F2, and F3, f1=33.15, f2=8.30, and f3=2.04, are taken as eigenvalues; according to the comparison rules, 2 ≤ f1 / f2 = 3.99 ≤ 5, f1 / f3 = 16.5 ≥ 10, it is determined that there is a target object in the image.

[0082] For example Figure 4 The side-scan sonar image without a target shown is subjected to a two-dimensional discrete cosine transform to obtain a discrete cosine transform coefficient matrix Co with the same size as the image.

[0083] Take the coefficient intensity matrix I = log (abs(Co)), and its intensity image is as follows. Figure 5 As shown, the size is 416×456, so the smaller value of 416 among the number of rows and columns of the matrix is ​​taken as the segmentation m value;

[0084] The matrices of size 138×138 in the upper left, middle, and lower right parts are respectively taken as characteristic matrices F1, F2, and F3;

[0085] The average values ​​of the feature matrices F1, F2, and F3, f1=22.40, f2=11.19, and f3=4.6, are taken as the eigenvalues.

[0086] According to the comparison rule, 2 ≤ f1 / f2 = 2.00 ≤ 5, but f1 / f3 = 4.87 ≤ 10, so it is determined that there is no target object in the image.

[0087] For example Figure 6 The side-scan sonar image of the shipwreck shown is subjected to a two-dimensional discrete cosine transform to obtain a discrete cosine transform coefficient matrix Co with the same size as the image; the coefficient intensity matrix I = log (abs(Co)) is then taken, and its intensity image is shown below. Figure 7 As shown, the size is 376×330, so the smaller value of the number of rows and columns in the matrix, 330, is taken as the segmentation m value; the matrices of size 110×110 in the upper left, middle and lower right are taken as feature matrices F1, F2 and F3 respectively; the average values ​​of feature matrices F1, F2 and F3, f1=47.14, f2=9.40 and f3=3.56, are taken as eigenvalues; according to the comparison rule, f1 / f2 = 5.01≥ 5, it is determined that there is a target object in the image.

[0088] For example Figure 8 The side-scan sonar image of the shipwreck shown is subjected to a two-dimensional discrete cosine transform to obtain a discrete cosine transform coefficient matrix Co with the same size as the image; the coefficient intensity matrix I = log (abs(Co)) is then taken, and its intensity image is shown below. Figure 9 As shown, the size is 342×338, so the smaller value of the number of rows and columns in the matrix, 338, is taken as the segmentation m value; the matrices of size 332×332 in the upper left, middle and lower right are taken as feature matrices F1, F2 and F3 respectively; the average values ​​of feature matrices F1, F2 and F3, f1=53.85, f2=8.87 and f3=2.66, are taken as eigenvalues; according to the comparison rule, f1 / f2 = 6.07≥ 5, it is determined that there is a target object in the image.

[0089] For example Figure 10 The side-scan sonar image of the shipwreck shown is subjected to a two-dimensional discrete cosine transform to obtain a discrete cosine transform coefficient matrix Co with the same size as the image; the coefficient intensity matrix I = log (abs(Co)) is then taken, and its intensity image is shown below. Figure 11 As shown, the size is 414×377, so the smaller value of the number of rows and columns in the matrix, 377, is taken as the segmentation m value; the matrices of size 125×125 in the upper left, middle and lower right are taken as feature matrices F1, F2 and F3 respectively; the average values ​​of feature matrices F1, F2 and F3, f1=23.36, f2=11.46 and f3=4.50, are taken as eigenvalues; according to the comparison rules, 2 ≤ f1 / f2 = 2.04 ≤ 5, f1 / f3 = 5.19 ≤ 10, it is determined that there is no target object in the image.

[0090] In another embodiment, step S2 includes:

[0091] S21: Receive pixel data of the original image matrix;

[0092] S22: Perform a one-dimensional discrete cosine transform on the pixel data in the row direction to generate an intermediate matrix of the row transform;

[0093] S23: Based on the row transformation intermediate matrix, perform a one-dimensional discrete cosine transformation in the column direction to generate a transformation coefficient matrix.

[0094] The working principle and beneficial effects of the above technical solution are as follows: In step S21, the system receives the pixel intensity values ​​arranged in row and column order in the original image matrix, and each pixel value represents the acoustic reflection intensity at the corresponding spatial location.

[0095] In step S22, a one-dimensional discrete cosine transform is performed independently on each row of pixel data in the image matrix. The spatial domain signal in the row direction is decomposed in the frequency domain using a DCT basis function sequence. The original spatial domain pixel values ​​are converted into coefficient representations in the frequency domain to generate an intermediate matrix for row transformation. This process keeps the number of rows of the matrix unchanged while changing the way the data is represented.

[0096] In step S23, the row transformation intermediate matrix is ​​used as input, and the one-dimensional discrete cosine transform operation is repeatedly performed on each column of data. The calculation process of two-dimensional DCT is completed through the frequency domain transformation in the column direction, and finally a complete transform coefficient matrix is ​​generated. This matrix contains the complete representation information of the original image in the two-dimensional frequency space.

[0097] In another embodiment, step S3 includes:

[0098] S31: Extract the coefficient values ​​at each position in the transformation coefficient matrix;

[0099] S32: Perform absolute value operation on each coefficient value to generate an absolute value matrix;

[0100] S33: Based on the absolute value matrix, perform logarithmic operations on each element to generate an intensity feature matrix.

[0101] The working principle and beneficial effects of the above technical solution are as follows: In step S31, the system reads the complex coefficient values ​​corresponding to each spatial position (i,j) in the transformation coefficient matrix Co one by one. These coefficients contain real and imaginary part information.

[0102] In step S32, the modulus of each complex coefficient is calculated to obtain the absolute value. The absolute values ​​of all positions are arranged in the original space to form an absolute value matrix to eliminate the influence of phase information.

[0103] In step S33, the natural logarithm operation is performed on each element in the absolute value matrix to generate the intensity feature matrix. The logarithmic transformation can compress the dynamic range of the data, amplify the differences of small values, and facilitate subsequent feature analysis processing.

[0104] In another embodiment, step S4 includes:

[0105] S41: Obtain the number of rows and columns of the intensity feature matrix;

[0106] S42: Select the smaller value between the number of rows and the number of columns as the splitting reference value;

[0107] S43: Determine the region size parameters according to a preset ratio based on the segmentation reference value;

[0108] S44: Based on the region size parameter, extract region feature matrices of the same size from the upper left, middle and lower right corners of the intensity feature matrix.

[0109] The working principle and beneficial effects of the above technical solution are as follows: In step S41, the number of rows M and the number of columns N of the intensity feature matrix I are obtained through the matrix dimension query function. These two parameters determine the spatial range that can be used for feature extraction.

[0110] In step S42, the number of rows M and the number of columns N are compared, and the smaller value is selected as the segmentation reference value m=min(M,N) to ensure that the extracted region matrix is ​​within the image boundary range.

[0111] In step S43, the segmentation reference value m is divided by the preset segmentation coefficient K=3 to calculate the region side length d=m / 3. This parameter determines the spatial size of each feature region.

[0112] In step S44, spatial localization and extraction of three regions are performed on the intensity feature matrix: the upper left region extracts a d×d submatrix F1 starting from position (0,0); the middle region extracts a d×d submatrix F2 starting from position ((Md) / 2,(Nd) / 2); and the lower right region extracts a d×d submatrix F3 starting from position (Md,Nd).

[0113] In another embodiment, step S5 includes:

[0114] S51: Receives multiple region feature matrices;

[0115] S52: Perform a summation operation on the elements within the feature matrix of each region to generate the region sum value;

[0116] S53: Divide the sum of each region by the number of elements in the corresponding region to generate a sequence of region feature values.

[0117] The working principle and beneficial effects of the above technical solution are as follows: In step S51, the system simultaneously receives three regional feature matrices F1, F2, and F3, each containing d×d elements.

[0118] In step S52, all elements in the feature matrix of each region are summed to obtain the sum value of each region.

[0119] In step S53, the sum of each region is divided by the total number of elements in that region, d², to calculate the average characteristic value of each region and generate a sequence of region characteristic values ​​containing f1, f2, and f3. These values ​​reflect the average concentration of frequency domain energy in different regions.

[0120] In another embodiment, step S6 includes:

[0121] S61: Extract the first feature value f1, the second feature value f2, and the third feature value f3 from the region feature value sequence;

[0122] S62: Calculate the ratio R1 of the first eigenvalue to the second eigenvalue, where R1 = f1 / f2;

[0123] S63: Calculate the ratio R2 of the first eigenvalue to the third eigenvalue, where R2 = f1 / f3;

[0124] S64: Perform a decision operation based on the ratios R1 and R2 to generate a result determining the existence of the target.

[0125] The working principle and beneficial effects of the above technical solution are as follows: In step S61, three numerical parameters are extracted sequentially from the region feature value sequence: the upper left region feature value f1, the middle region feature value f2, and the lower right region feature value f3.

[0126] In step S62, the first ratio calculation is performed, and the ratio relationship between the feature values ​​of the upper left corner region and the middle region is obtained by using the formula R1=f1 / f2. This ratio reflects the frequency domain energy difference between the image edge and the center region.

[0127] In step S63, the second ratio calculation is performed, and the ratio of the feature values ​​of the upper left corner region and the lower right corner region is obtained by using the formula R2=f1 / f3. This ratio reflects the difference in energy distribution between the diagonal regions of the image.

[0128] In step S64, based on the calculated ratios R1 and R2, the existence of the target is analyzed according to the preset judgment logic rules. By comparing the relationship between these ratios and the preset threshold, a binary judgment result of "the target exists" or "the target does not exist" is finally output.

[0129] In another embodiment, the decision operation in step S64 includes:

[0130] Operation condition 1: When R1 is greater than or equal to the first threshold T1, the result is that the target object exists;

[0131] Operation condition 2: When R1 is greater than or equal to the second threshold T2 and less than the first threshold T1, and R2 is greater than or equal to the third threshold T3, the result is that the target object exists.

[0132] When neither operation condition one nor operation condition two is satisfied, the result is that the target object does not exist.

[0133] Among them, T1>T2>1, T3>1.

[0134] The working principle and beneficial effects of the above technical solution are as follows: The judgment operation adopts a hierarchical threshold comparison strategy:

[0135] In the first-level determination, when the ratio R1 is greater than or equal to the first threshold T1, it indicates that the frequency-domain energy in the upper-left corner region is significantly higher than that in the middle region, and the determination result of "target object exists" is directly output.

[0136] In the second-level determination, when R1 is between the second threshold T2 and the first threshold T1 (T2 ≤ R1 < T1), it is necessary to further check whether R2 is greater than or equal to the third threshold T3. If both of these conditions are satisfied, it is determined that "target object exists".

[0137] In the fallback determination, when none of the above condition combinations are satisfied, the determination result of "target object does not exist" is output. The threshold settings follow the constraint relationship of T1 > T2 > 1 and T3 > 1 to ensure the rationality of the determination logic.

[0138] In another embodiment, step S44 includes:

[0139] S441: Take the upper-left corner position of the intensity feature matrix as the starting position of the first region;

[0140] S442: Based on the number of rows M and the number of columns N of the intensity feature matrix and the region size parameter d, calculate the starting position of the middle region as ((M - d) / 2, (N - d) / 2);

[0141] S443: Calculate the starting position of the lower-right corner region so that the lower-right boundary of this region is aligned with the lower-right boundary of the intensity feature matrix.

[0142] The working principle and beneficial effects of the above technical solution are as follows: In step S441, the coordinate origin (0, 0) of the intensity feature matrix I is set as the starting extraction position of the first region F1, and a sub-matrix of size d×d is extracted from this position in the lower-right direction.

[0143] In step S442, the starting position coordinates of the middle region F2 are determined as ((M - d) / 2, (N - d) / 2) through the calculation formula, where M and N are the number of rows and columns of the intensity feature matrix respectively, and d is the side length of the region. This position makes the extracted region near the geometric center of the entire matrix.

[0144] In step S443, the starting position of the lower-right corner region F3 is calculated as (M - d, N - d), ensuring that the right boundary and the lower boundary of the extracted d×d region are exactly aligned with the right boundary and the lower boundary of the intensity feature matrix respectively, realizing the precise extraction of the lower-right corner region of the matrix.

[0145] In another embodiment, step S43 includes:

[0146] S431: Receive the segmentation reference value m;

[0147] S432: Divide the segmentation reference value by the preset segmentation coefficient K to generate the region side length value d, where d=m / K, and the preset segmentation coefficient K is 3;

[0148] S433: Use the region side length value d as the region size parameter for subsequent region extraction.

[0149] The working principle and beneficial effects of the above technical solution are as follows: In step S431, the system receives the segmentation reference value m determined in the previous step, which is equal to the smaller value in the number of rows and columns of the intensity feature matrix.

[0150] In step S432, a division operation is performed to divide the segmentation reference value m by the preset segmentation coefficient K=3, and the region side length value d is calculated using the formula d=m / K. The setting of segmentation coefficient K=3 ensures that the three feature regions can be reasonably distributed in the image space without overlapping.

[0151] In step S433, the calculated region side length value d is used as the size parameter for subsequent region extraction operations. This parameter uniformly controls the spatial size of the three feature regions, ensuring the fairness and effectiveness of feature comparison.

[0152] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from the spirit and scope of this invention.

Claims

1. A rapid method for identifying suspicious targets in the field of view of side-scan sonar images, characterized in that, include: S1: Acquire side-scan sonar image data and generate the original image matrix; S2: Based on the original image matrix, perform a two-dimensional discrete cosine transform operation to generate a transform coefficient matrix; S3: Based on the transformation coefficient matrix, calculate the logarithmic transformation of the absolute values ​​of the coefficients to generate the intensity feature matrix; S4: Based on the dimensional information of the intensity feature matrix, extract multiple region feature matrices according to preset segmentation rules; Step S4 includes: S41: Obtain the number of rows and columns of the intensity feature matrix; S42: Select the smaller value between the number of rows and the number of columns as the splitting reference value; S43: Determine the region size parameters according to a preset ratio based on the segmentation reference value; S44: Based on the region size parameter, extract region feature matrices of the same size from the top left, middle and bottom right corners of the intensity feature matrix respectively; S5: Based on multiple regional feature matrices, calculate the feature statistics of each region to generate a sequence of regional feature values; Step S5 includes: S51: Receives multiple region feature matrices; S52: Perform a summation operation on the elements within the feature matrix of each region to generate the region sum value; S53: Divide the sum of each region by the number of elements in the corresponding region to generate a sequence of region feature values; S6: Based on the regional feature value sequence, calculate the ratio relationship between feature values, and generate the target existence determination result according to the ratio determination rule.

2. The rapid discrimination method for suspicious targets in the field of view of side-scan sonar images according to claim 1, characterized in that, Step S2 includes: S21: Receive pixel data of the original image matrix; S22: Perform a one-dimensional discrete cosine transform on the pixel data in the row direction to generate an intermediate matrix of the row transform; S23: Based on the row transformation intermediate matrix, perform a one-dimensional discrete cosine transformation in the column direction to generate a transformation coefficient matrix.

3. The rapid discrimination method for suspicious targets in the field of view of side-scan sonar images according to claim 1, characterized in that, Step S3 includes: S31: Extract the coefficient values ​​at each position in the transformation coefficient matrix; S32: Perform absolute value operation on each coefficient value to generate an absolute value matrix; S33: Based on the absolute value matrix, perform logarithmic operations on each element to generate an intensity feature matrix.

4. The rapid discrimination method for suspicious targets in the field of view of side-scan sonar images according to claim 1, characterized in that, Step S6 includes: S61: Extract the top left corner region feature value f1, the middle region feature value f2, and the bottom right corner region feature value f3 from the region feature value sequence; S62: Calculate the ratio R1 of the feature value f1 of the upper left corner region to the feature value f2 of the middle region, where R1 = f1 / f2; S63: Calculate the ratio R2 of the feature value f1 of the upper left corner region to the feature value f3 of the lower right corner region, where R2 = f1 / f3; S64: Perform a decision operation based on the ratios R1 and R2 to generate a result determining the existence of the target.

5. The rapid discrimination method for suspicious targets in the field of view of side-scan sonar images according to claim 4, characterized in that, The decision operation in step S64 includes: Operation condition 1: When R1 is greater than or equal to the first threshold T1, the result is that the target object exists; Operation condition 2: When R1 is greater than or equal to the second threshold T2 and less than the first threshold T1, and R2 is greater than or equal to the third threshold T3, the result is that the target object exists. When neither operation condition one nor operation condition two is satisfied, the result is that the target object does not exist. Among them, T1>T2>1, T3>1.

6. The rapid discrimination method for suspicious targets in the field of view of side-scan sonar images according to claim 1, characterized in that, Step S44 includes: S441: Take the top left corner of the intensity feature matrix as the starting position of the first region; S442: Based on the number of rows M and columns N of the intensity feature matrix and the region size parameter d, the starting position of the central region is calculated as ((Md) / 2, (Nd) / 2); S443: Calculate the starting position of the lower right corner region so that the lower right boundary of the region is aligned with the lower right boundary of the intensity feature matrix.

7. The rapid discrimination method for suspicious targets in the field of view of side-scan sonar images according to claim 1, characterized in that, Step S43 includes: S431: Receive segmentation reference value m; S432: Divide the segmentation reference value by the preset segmentation coefficient K to generate the region side length value d, where d=m / K, and the preset segmentation coefficient K is 3; S433: Use the region side length value d as the region size parameter for subsequent region extraction.

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