A target positioning method based on optoelectronic pod

Abstract

The present invention discloses a target positioning method based on an optoelectronic pod, which belongs to the field of image processing technology and includes the following steps: S1, collecting image data using an optoelectronic pod installed on a drone; S2, generating color array values ​​according to the standard color matrix of the optoelectronic pod and each structural matrix of the image data; S3, determining the target positioning according to the contour of the image data and the color array values. The present invention generates color array values ​​according to the standard color matrix of the optoelectronic pod and each structural matrix of the image data, and the color array values ​​can reflect the color characteristics of the target object, which helps to accurately locate the target in a complex background; the present invention also uses the color array values ​​to analyze the contour of the image data, which can further accurately locate the target.

Description

Technical Field

[0001] The invention belongs to the technical field of image processing, and in particular relates to a target positioning method based on an optoelectronic pod. Background Art

[0002] In the fields of drone reconnaissance, environmental monitoring and disaster relief, high-precision real-time positioning of targets is crucial. Traditional methods often rely on the navigation system of the drone itself, but this method is limited by the flight attitude and position of the drone, and the positioning accuracy and real-time performance need to be improved. Therefore, carrying an optoelectronic pod for visible light imaging has become a more popular method. The present invention aims to develop a method for achieving accurate target positioning based on an optoelectronic pod. Summary of the invention

[0003] In order to solve the above problems, the present invention proposes a target positioning method based on an optoelectronic pod.

[0004] The technical solution of the present invention is: a target positioning method based on an optoelectronic pod comprises the following steps:

[0005] S1, collect image data using the optoelectronic pod installed on the drone;

[0006] S2. Generate color array values ​​according to the standard color matrix of the optoelectronic pod and various structural matrices of the image data;

[0007] S3. Determine the target location based on the contour and color array value of the image data.

[0008] Furthermore, S2 includes the following sub-steps:

[0009] S21, constructing a structure matrix for each row of image data;

[0010] S22. Generate color array values ​​according to the standard color matrix of the optoelectronic pod and all structural matrices of the image data.

[0011] The beneficial effect of the above further scheme is that in the present invention, by setting each diagonal element of the diagonal matrix to the RGB value of the corresponding pixel, it is possible to ensure that the color information of each pixel is accurately represented, and data compression is achieved, reducing the redundancy of data processing. The color array value can accurately fuse the color information of each pixel, thereby generating a more accurate color representation.

[0012] Furthermore, in S21, the structure matrix Q of the tth row in the image data t The expression is:

[0013] ; In the formula, q 1 Represents the RGB value of the first pixel in the tth row of the image data, q2 Represents the RGB value of the second pixel in the tth row of the image data, q H represents the RGB value of the Hth pixel in the tth row of the image data, H represents the total number of pixels in each row of the image data, and Diag(·) represents the construction of a diagonal matrix.

[0014] Diag(·) is specifically: 1 ,q 1 ,…,q H ) is used as the diagonal elements of the structure matrix in turn and the remaining elements are all 0.

[0015] Further, S22 includes the following sub-steps:

[0016] S221, generating a standard color matrix according to the transmittance of each filter in the Bayer array of the optoelectronic pod;

[0017] S222. Generate a color array value according to the structure matrix of each row of the image data and the standard color matrix.

[0018] The beneficial effect of the above further scheme is: in the present invention, the Bayer array in the optoelectronic pod is a key color filter array technology, which enables the optoelectronic pod to capture color images. The arrangement of the Bayer array is usually a 2x2 matrix. Two green (G) filters occupy the diagonal positions. A red (R) filter and a blue (B) filter occupy the remaining two positions respectively. A standard color matrix is ​​generated according to the transmittance of each filter in the Bayer array of the optoelectronic pod, which can ensure the accuracy of color reproduction.

[0019] Furthermore, in S221, the calculation formula of the standard color matrix Y is:

[0020] Where g 1 represents the transmittance of the first green filter in the Bayer array, g 2 represents the transmittance of the second green filter in the Bayer array, r represents the transmittance of the red filter in the Bayer array, and b represents the transmittance of the blue filter in the Bayer array.

[0021] Furthermore, in S222, the calculation formula of the color array value h is:

[0022] ; In the formula, μ 1 Represents the maximum singular value of the first row of the structure matrix in the image data, μ 2 Represents the maximum singular value of the second row structure matrix in the image data, μ T represents the maximum singular value of the T-th row structure matrix in the image data, λ *represents the maximum eigenvalue of the standard color matrix, and T represents the total number of rows of image data.

[0023] Furthermore, S3 includes the following sub-steps:

[0024] S31, extracting the contour of the image data, and obtaining the first-order moment corresponding to the contour;

[0025] S32, calculating the contour splicing weight using the color array value and the first-order moment;

[0026] S33, removing pixels whose RGB values ​​are greater than the contour splicing weight in the contour of the image data, and taking the remaining pixels of the contour as target positioning.

[0027] The beneficial effect of the above further solution is that: in the present invention, the first-order moment of the image contour generally refers to the moment of the image about the x-axis and the y-axis, that is, M 01 and M 10 Obtaining the first-order moment corresponding to the contour can provide useful information about the shape and position of the contour. The first-order moment can reflect the overall distribution of the contour, and the color array value can reflect the color information of the object. Accurate target positioning can be achieved through accurate contour extraction.

[0028] Furthermore, in S32, the calculation formula of the contour splicing weight b is:

[0029] ; In the formula, M 10 represents the first component of the first-order moment, M 01 represents the second component of the first-order moment, h represents the color array value, and e represents the exponent.

[0030] The beneficial effects of the present invention are as follows: the present invention generates color array values ​​according to the standard color matrix of the optoelectronic pod and the various structural matrices of the image data, the color array values ​​can reflect the color characteristics of the target object, and help to accurately locate the target in a complex background; the present invention also uses the color array values ​​to analyze the contour of the image data, which can further accurately locate the target. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 The figure is a flow chart of the target positioning method based on the optoelectronic pod. DETAILED DESCRIPTION

[0032] The embodiments of the present invention will be further described below in conjunction with the accompanying drawings.

[0033] like Figure 1 As shown, the present invention provides a target positioning method based on an optoelectronic pod, comprising the following steps:

[0034] S1, collect image data using the optoelectronic pod installed on the drone;

[0035] S2. Generate color array values ​​according to the standard color matrix of the optoelectronic pod and various structural matrices of the image data;

[0036] S3. Determine the target location based on the contour and color array value of the image data.

[0037] In this embodiment of the present invention, S2 includes the following sub-steps:

[0038] S21, constructing a structure matrix for each row of image data;

[0039] S22. Generate color array values ​​according to the standard color matrix of the optoelectronic pod and all structural matrices of the image data.

[0040] In the present invention, by setting each diagonal element of the diagonal matrix to the RGB value of the corresponding pixel, the color information of each pixel can be accurately represented, data compression can be achieved, and the redundancy of data processing can be reduced. The color array value can accurately fuse the color information of each pixel, thereby generating a more accurate color representation.

[0041] In the embodiment of the present invention, in S21, the structure matrix Q of the tth row in the image data t The expression is:

[0042] ; In the formula, q 1 Represents the RGB value of the first pixel in the tth row of the image data, q 2 Represents the RGB value of the second pixel in the tth row of the image data, q H represents the RGB value of the Hth pixel in the tth row of the image data, H represents the total number of pixels in each row of the image data, and Diag(·) represents the construction of a diagonal matrix.

[0043] Diag(·) is specifically: 1 ,q 1 ,…,q H ) is used as the diagonal elements of the structure matrix in turn and the remaining elements are all 0.

[0044] In this embodiment of the present invention, S22 includes the following sub-steps:

[0045] S221, generating a standard color matrix according to the transmittance of each filter in the Bayer array of the optoelectronic pod;

[0046] S222. Generate a color array value according to the structure matrix of each row of the image data and the standard color matrix.

[0047] In the present invention, the Bayer array in the optoelectronic pod is a key color filter array technology that enables the optoelectronic pod to capture color images. The Bayer array is usually arranged in a 2x2 matrix. Two green (G) filters occupy the diagonal positions. A red (R) filter and a blue (B) filter occupy the remaining two positions respectively. A standard color matrix is ​​generated according to the transmittance of each filter in the Bayer array of the optoelectronic pod to ensure the accuracy of color reproduction.

[0048] In the embodiment of the present invention, in S221, the calculation formula of the standard color matrix Y is:

[0049] Where g 1 represents the transmittance of the first green filter in the Bayer array, g 2 represents the transmittance of the second green filter in the Bayer array, r represents the transmittance of the red filter in the Bayer array, and b represents the transmittance of the blue filter in the Bayer array.

[0050] In the embodiment of the present invention, in S222, the calculation formula of the color array value h is:

[0051] ; In the formula, μ 1 Represents the maximum singular value of the first row of the structure matrix in the image data, μ 2 Represents the maximum singular value of the second row structure matrix in the image data, μ T represents the maximum singular value of the T-th row structure matrix in the image data, λ * represents the maximum eigenvalue of the standard color matrix, and T represents the total number of rows of image data.

[0052] In this embodiment of the present invention, S3 includes the following sub-steps:

[0053] S31, extracting the contour of the image data, and obtaining the first-order moment corresponding to the contour;

[0054] S32, calculating the contour splicing weight using the color array value and the first-order moment;

[0055] S33, removing pixels whose RGB values ​​are greater than the contour splicing weight in the contour of the image data, and taking the remaining pixels of the contour as target positioning.

[0056] In the present invention, the first-order moment of the image contour generally refers to the moment of the image about the x-axis and the y-axis, that is, M 01 and M 10 Obtaining the first-order moment corresponding to the contour can provide useful information about the shape and position of the contour. The first-order moment can reflect the overall distribution of the contour, and the color array value can reflect the color information of the object. Accurate target positioning can be achieved through accurate contour extraction.

[0057] In the embodiment of the present invention, in S32, the calculation formula of the contour splicing weight b is:

[0058] ; In the formula, M 10 represents the first component of the first-order moment, M 01 represents the second component of the first-order moment, h represents the color array value, and e represents the exponent.

[0059] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A target positioning method based on an optoelectronic pod, characterized in that: The following steps are involved: S1, collect image data using the optoelectronic pod installed on the drone; S2. Generate color array values ​​according to the standard color matrix of the optoelectronic pod and each structural matrix of the image data; S3, determining the target location according to the contour and color array value of the image data; The S2 comprises the following sub-steps: S21, constructing a structure matrix for each row of image data; S22, generating a color array value according to a standard color matrix of the optoelectronic pod and all structural matrices of the image data; In S21, the structure matrix Q of the tth row in the image data t The expression is: ; In the formula, q1 represents the RGB value of the first pixel in the t-th row of the image data, q2 represents the RGB value of the second pixel in the t-th row of the image data, and q H represents the RGB value of the Hth pixel in the tth row of the image data, H represents the total number of pixels in each row of the image data, and Diag(·) represents the construction of a diagonal matrix; The S22 comprises the following sub-steps: S221, generating a standard color matrix according to the transmittance of each filter in the Bayer array of the optoelectronic pod; S222, generating a color array value according to the structure matrix of each row of the image data and the standard color matrix; In S221, the calculation formula of the standard color matrix Y is: ; Wherein, g1 represents the transmittance of the first green filter in the Bayer array, g2 represents the transmittance of the second green filter in the Bayer array, r represents the transmittance of the red filter in the Bayer array, and b represents the transmittance of the blue filter in the Bayer array; In S222, the calculation formula of the color array value h is: ; In the formula, μ1 represents the maximum singular value of the structure matrix of the first row in the image data, μ2 represents the maximum singular value of the structure matrix of the second row in the image data, and μ T represents the maximum singular value of the T-th row structure matrix in the image data, λ * represents the maximum eigenvalue of the standard color matrix, and T represents the total number of rows of image data; The S3 comprises the following sub-steps: S31, extracting the contour of the image data, and obtaining the first-order moment corresponding to the contour; S32, calculating the contour splicing weight using the color array value and the first-order moment; S33, removing pixels whose RGB values ​​are greater than the contour splicing weight in the contour of the image data, and taking the remaining pixels of the contour as target positioning; In S32, the calculation formula of the contour splicing weight b is: ; In the formula, M 10 represents the first component of the first-order moment, M 01 represents the second component of the first-order moment, h represents the color array value, and e represents the exponent.

Images