High-performance short-wave infrared image laser spot recognition algorithm

Abstract

The invention relates to the technical field of image laser spot recognition, and discloses a high-performance short-wave infrared image laser spot recognition algorithm, which comprises the following steps of S1, firstly performing region recognition on an image, and positioning the image to a specified recognition region; s2, light spot identification only needs to identify a specified image area, and the data of the area is over-stretched; s3, performing threshold judgment to obtain a binary image; s4, a diameter line segment cluster and a coordinate value of the light spot are obtained after pixel scanning, line segments obtained through actual scanning are given according to the continuity that the pixel point value is 1, and a line segment cluster is obtained; s5, finding out the diameter from the diameter cluster; and S6, calculating a coordinate value according to the diameter length, and giving a circle center coordinate of the light spot. The method is suitable for hardware transplantation processing, real-time performance is achieved, occupied resources are small, complex operation of a traditional light spot recognition algorithm is broken through, and the method can be achieved only through pixel scanning and line segment comparison.

Description

Technical Field

[0001] The invention relates to the technical field of image laser spot recognition, and in particular to a high-performance short-wave infrared image laser spot recognition algorithm. Background Art

[0002] As a common combat equipment, infrared vision equipment has the characteristics of high real-time performance, complex environmental adaptability requirements, and easy integration. Indium gallium arsenide short-wave infrared equipment has a high response rate to the 0.9-1.7um band due to its own infrared spectrum characteristics. Therefore, it is often used as a common equipment for laser guidance or laser interception, mainly to identify the spot effect produced by the laser beam.

[0003] Laser spot recognition is an important technology in the field of infrared image processing and an important means in the current conventional defense field. There are three common laser spot recognition algorithms: centroid method, Hough change method, and circle fitting method. Traditional spot recognition methods must first undergo filtering, pixel caching, binarization, spot edge extraction and other operations. They have poor real-time performance and require a large number of division and square root operations, which are not easy to implement through hardware device transplantation.

[0004] From the above description, it can be seen that the present invention combines the characteristics of hardware devices and realizes the needs of laser spot tracking by comparing thresholds, pixel scanning, obtaining line segment clusters, finding suspected spot diameter segments, and calculating centers. It has high real-time performance and is easy for hardware transplantation.

[0005] However, for infrared laser spot recognition, due to the strong laser energy, there will be a large difference with other pixel grayscales. For this application scenario, the pixel value is stretched and binarized, and then line scanning is performed and the longest line segment is found by comparison. The center value of the line segment is calculated to obtain the spot coordinates. This invention is applicable to scenes with severe speckle and extremely uneven distribution of spot signals. Summary of the invention

[0006] 1. Technical issues to be solved

[0007] In view of the deficiencies in the prior art, the present invention provides a high-performance short-wave infrared image laser spot recognition algorithm.

[0008] (II) Technical solution

[0009] To achieve the above object, the present invention provides the following technical solution: a high-performance short-wave infrared image laser spot recognition algorithm, comprising the following steps:

[0010] S1: First, the image is region-recognized and located in the designated recognition area;

[0011] S2: Spot recognition only requires recognition of a specified image area, where the data is stretched;

[0012] S3: After thresholding, a binary image is obtained;

[0013] S4: After pixel scanning, the diameter line segment cluster and coordinate value of the light spot are obtained. This step will give the line segment actually scanned according to the continuity of the pixel value of 1, and obtain the line segment cluster;

[0014] S5: Find the diameter from the diameter cluster;

[0015] S6: The coordinates of the center of the light spot are calculated based on the diameter length.

[0016] Preferably, the specific algorithm of the circle center coordinates includes:

[0017] S1: First, the light spot recognition area is obtained according to the frame synchronization signal and the line synchronization signal;

[0018] S2: The obtained value of the recognition area is subjected to a stretching process and then a binarization process;

[0019] S3: Perform row scanning according to the binary value. When the binary value is 1, it is used as the starting point of the line segment and the coordinates are latched. When the row scanning determines that the binary value is 0 and the number of points with the accumulated value of 0 is 3 or more, the line segment is determined to be ended and the coordinates of the end point are latched. There may be multiple line segments in the row. Each line segment needs to be compared to obtain the longest one. When there are multiple line segments of equal length, the first one is selected as the longest line segment.

[0020] S4: Compare the values ​​obtained from RAM. The longest one is the diameter of the light spot, and the coordinates of the center of the circle can be obtained.

[0021] Preferably, the specific algorithm of the circle center coordinates is based on a TV repair algorithm.

[0022] Preferably, the TV inpainting algorithm defines an ideal image u on a complete image region x = (x1, x2) ∈ Ω 0 —u 0 (x), its subset represents the missing image information on D, let E = D = Ω / D;

[0023] And the repair function is defined as

[0024]

[0025] And satisfy the noise constraint:

[0026]

[0027] Preferably, the outer neighborhood area is A(E), and the Gaussian white noise exists in the image within the shadow; u is the image; σ 2 is the standard deviation of white noise; r is a non-negative real function.

[0028] Preferably, a suitable function r can be found to obtain a good effect of processing the edge of the image. On the edge, ▽u is an impact function δ, so the function r is required to be finite, that is:

[0029]

[0030] Preferably, the function r(s) is expanded to obtain the form: r(s)=s a + low-order expansion term (S is the total number of iterations), formula It is required that r(s) is finite when s→∞, so a≤1. Select a=1, and the model of TV repair technology is the formula Applying the La-Grange multiplier to the formula The new function is

[0031]

[0032] Preferably, the λ is a Lagrangian undetermined coefficient.

[0033] (III) Beneficial effects

[0034] Compared with the prior art, the present invention provides a high-performance short-wave infrared image laser spot recognition algorithm, which has the following beneficial effects:

[0035] 1. This is a high-performance short-wave infrared image laser spot recognition algorithm, suitable for hardware transplantation and processing, with real-time performance and small resource occupation.

[0036] 2. This high-performance short-wave infrared image laser spot recognition algorithm breaks the complex calculation of traditional spot recognition algorithms and can be achieved only through pixel scanning and line segment comparison.

[0037] 3. This is a high-performance short-wave infrared image laser spot recognition algorithm. It only takes a few clock cycles to obtain the coordinate value of the spot through real-time scanning of pixels, and its real-time performance is very high. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0039] Figure 1 This is a calculation flow chart of a high-performance short-wave infrared image laser spot recognition algorithm of the present invention;

[0040] Figure 2 It is an original spot image in an embodiment of a high-performance short-wave infrared image laser spot recognition algorithm of the present invention;

[0041] Figure 3 The diameter of the spot and the coordinate diagram of the center of the spot in an embodiment of a high-performance short-wave infrared image laser spot recognition algorithm of the present invention;

[0042] In the figure: Figure 2 The left side is the actual laser scanning image and the right side is the captured spot image; Figure 3 The left side is the identified coordinate spot image, and the right side is the binarized image. DETAILED DESCRIPTION

[0043] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0044] Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0045] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0046] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0047] like Figure 1-3As shown, the present invention provides a high-performance short-wave infrared image laser spot recognition algorithm, comprising the following steps:

[0048] S1: First, the image is region-recognized and located in the designated recognition area;

[0049] S2: Spot recognition only requires recognition of a specified image area, where the data is stretched;

[0050] S3: After thresholding, a binary image is obtained;

[0051] S4: After pixel scanning, the diameter line segment cluster and coordinate value of the light spot are obtained. This step will give the line segment actually scanned according to the continuity of the pixel value of 1, and obtain the line segment cluster;

[0052] S5: Find the diameter from the diameter cluster;

[0053] S6: The coordinates of the center of the light spot are calculated based on the diameter length.

[0054] The specific algorithm for the center coordinates includes:

[0055] S1: First, the light spot recognition area is obtained according to the frame synchronization signal and the line synchronization signal;

[0056] S2: The obtained value of the recognition area is subjected to a stretching process and then a binarization process;

[0057] S3: Perform row scanning according to the binary value. When the binary value is 1, it is used as the starting point of the line segment and the coordinates are latched. When the row scanning determines that the binary value is 0 and the number of points with the accumulated value of 0 is 3 or more, the line segment is determined to be ended and the coordinates of the end point are latched. There may be multiple line segments in the row. Each line segment needs to be compared to obtain the longest one. When there are multiple line segments of equal length, the first one is selected as the longest line segment.

[0058] S4: Compare the values ​​obtained from RAM. The longest one is the diameter of the light spot, and the coordinates of the center of the circle can be obtained.

[0059] The specific algorithm of the circle center coordinates is based on the TV repair algorithm.

[0060] The TV inpainting algorithm defines an ideal image u on a complete image region x = (x1, x2) ∈ Ω. 0 —u 0 (x), its subset represents the missing image information on D, let E = D = Ω / D;

[0061] And the repair function is defined as

[0062]

[0063] And satisfy the noise constraint:

[0064]

[0065] The outer neighborhood area is A(E), and Gaussian white noise exists in the image within the shadow; u is the image; σ 2 is the standard deviation of white noise; r is a non-negative real function.

[0066] We can find a suitable function r to obtain good image edge processing effects. is an impulse function δ, which requires the function r to be finite, that is:

[0067]

[0068] The function r(s) is expanded to obtain its form: r(s) = s a + low-order expansion term (S is the total number of iterations),

[0069] Mode It is required that r(s) is finite when s→∞, so a≤1. Select a=1, and the model of TV repair technology is the formula Applying the La-Grange multiplier to the formula The new function is

[0070]

[0071] λ is the Lagrange coefficient to be determined,

[0072] in Figure 2 This is the original spot image. It can be seen from the image that the spot is located in the specified area. Then the pixels in the area are scanned to obtain the longest line segment in each line. The diameter of the spot and the coordinates of the center of the spot can be obtained by comparing the line segments.

[0073] The present invention is described in detail above in conjunction with the accompanying drawings. The above-mentioned light spot recognition algorithm principle can obtain the coordinate value of the light spot by a simple scanning method. The coordinate value obtained by the algorithm is verified by the embodiment and is completely consistent with the actual coordinate value. Therefore, the present invention can effectively solve the problems existing in the existing implementation method, and has also been strongly verified in practical applications. Obviously, the specific implementation of the present invention is not limited to the above-mentioned method. As long as various non-substantial improvements are made using the method concept and technical solution of the present invention, or the concept and technical solution of the present invention are directly applied to other occasions without improvement, they are all within the protection scope of the present invention.

[0074] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "comprising a reference structure" do not exclude the existence of other identical elements in the process, method, article or device including the elements.

Claims

1. A high-performance short-wave infrared image laser spot recognition algorithm, characterized by: The following steps are involved: S1: First, the image is region-recognized and located in the designated recognition area; S2: Spot recognition only requires recognition of a specified image area, where the data is stretched; S3: After thresholding, a binary image is obtained; S4: After pixel scanning, the diameter line segment cluster and coordinate value of the light spot are obtained. This step will give the line segment actually scanned according to the continuity of the pixel value of 1, and obtain the line segment cluster; S5: Find the diameter from the diameter cluster; S6: The coordinates of the center of the light spot are calculated based on the diameter length.

2. A high-performance short-wave infrared image laser spot recognition algorithm according to claim 1, characterized in that: The specific algorithm of the circle center coordinates includes: S1: First, the light spot recognition area is obtained according to the frame synchronization signal and the line synchronization signal; S2: The obtained value of the recognition area is subjected to a stretching process and then a binarization process; S3: Perform row scanning according to the binary value. When the binary value is 1, it is used as the starting point of the line segment and the coordinates are latched. When the row scanning determines that the binary value is 0 and the number of points with the accumulated value of 0 is 3 or more, the line segment is determined to be ended and the coordinates of the end point are latched. There may be multiple line segments in the row. Each line segment needs to be compared to obtain the longest one. When there are multiple line segments of equal length, the first one is selected as the longest line segment. S4: Compare the values ​​obtained from RAM. The longest one is the diameter of the light spot, and the coordinates of the center of the circle can be obtained.

3. The high-performance short-wave infrared image laser spot recognition algorithm according to claim 1, characterized in that: The specific algorithm of the circle center coordinates is based on the TV repair algorithm.

4. The high-performance short-wave infrared image laser spot recognition algorithm according to claim 1, characterized in that: The TV inpainting algorithm defines an ideal image u on a complete image region x = (x1, x2) ∈ Ω. 0 —u 0 (x), its subset represents the missing image information on D, let E = D = Ω / D; And the repair function is defined as And satisfy the noise constraint:

5. The high-performance short-wave infrared image laser spot recognition algorithm according to claim 1, characterized in that: The outer neighborhood area is A(E), and Gaussian white noise exists in the image within the shadow; u is the image; σ 2 is the standard deviation of white noise; r is a non-negative real function.

6. The high-performance short-wave infrared image laser spot recognition algorithm according to claim 1, characterized in that: We can find a suitable function r to obtain good processing effect of image edge. On the edge, ▽u is an impact function δ, so the function r is required to be finite, that is:

7. A high-performance short-wave infrared image laser spot recognition algorithm according to claim 6, characterized in that: The function r(s) is expanded to obtain its form: r(s) = s a + low-order expansion term (S is the total number of iterations), formula It is required that r(s) is finite when s→∞, so a≤1. Select a=1, and the model of TV repair technology is the formula Applying the La-Grange multiplier to the formula The new function is 8. The high-performance short-wave infrared image laser spot recognition algorithm according to claim 1, characterized in that: The λ is the Lagrange coefficient to be determined.

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