A method for estimating the center position of small infrared targets

Abstract

The present invention belongs to the field of infrared image processing and target detection technology, and in particular to a method for estimating the center position of a small infrared target. The method comprises: S1: processing the original infrared image based on the target extraction algorithm for segmenting the adjacent background area to obtain a target confidence map of the small infrared target. DT ; S2: confidence map of the target DT Perform derivative calculation to obtain a directional derivative image; S3: Based on the maximum point coordinate value and the minimum point coordinate value of the directional derivative image, obtain the center position coordinate value of the infrared small target. The present invention can directly give the center position coordinate value of the infrared small target and can greatly improve the detection accuracy and detection efficiency.

Description

Technical Field

[0001] The invention belongs to the technical field of infrared image processing and target detection, and in particular relates to a method for estimating the central position of a small infrared target. Background Art

[0002] Small target detection technology based on infrared images is an important part of infrared search and tracking systems. It has always been one of the hot areas of development and competitive research in military reconnaissance, space exploration and other applications in various countries around the world. The detection and search capabilities of infrared small target detection systems for various types of targets in different scenarios are key factors in determining, discovering, locking and tracking targets and effectively enhancing environmental perception capabilities. Due to the small pixel size and weak radiation intensity of infrared small targets in infrared images, infrared small targets lack rich structural and texture information, and the signal-to-noise ratio is lower, making it difficult to detect infrared small targets in long-distance infrared detection.

[0003] According to the technical route of infrared small target detection and tracking algorithms, the existing algorithms can be divided into two categories, namely single-frame detection algorithms and continuous multi-frame detection algorithms; the structure of the single-frame detection algorithm is concise and clear, and the detection timeliness is high, but when the signal-to-noise ratio of the sequence image is low, the false alarm rate of the detection result of the single-frame detection algorithm is high, and even multiple frames may be missed; the calculation complexity of the continuous multi-frame detection algorithm is high, and the required computing resources are also large. It is suitable for applications with low signal-to-noise ratio of sequence images, high frame rate, rich computing resources, and low detection timeliness requirements. For systems with limited computing resources and high real-time requirements, the research on infrared weak small target detection based on single-frame images has gradually become the mainstream. According to the different theoretical methods based on the algorithm, the single-frame image infrared small target detection algorithm can be roughly divided into three categories, namely, the detection algorithm based on local feature representation, the detection algorithm based on background estimation filtering, and the detection algorithm based on component analysis. The first two algorithms are relatively simple and easy to implement, but the detection probability is reduced and the false alarm is high under complex backgrounds. The third algorithm is complex in calculation and not easy to implement in engineering; in addition, most of the current detection algorithms extract infrared small targets from the confidence map through adaptive thresholds, and cannot give the center position coordinates of infrared small targets. Summary of the invention

[0004] In view of this, the present invention aims to provide a method for estimating the center position of a small infrared target, so as to solve the problem that most of the existing technologies extract small infrared targets from confidence maps through adaptive thresholds, but cannot give the center position coordinates of the small infrared targets, and the mutual constraint between the detection accuracy and the algorithm calculation amount of the existing algorithms. The present invention can directly give the center position coordinate values ​​of the small infrared targets and can greatly improve the detection accuracy and detection efficiency.

[0005] To achieve the above object, the technical solution created by the present invention is implemented as follows:

[0006] A method for estimating the center position of a small infrared target comprises the following steps:

[0007] S1: Process the original infrared image based on the target extraction algorithm for segmentation of the adjacent background area to obtain the target confidence map of the infrared small target DT ;

[0008] S2: Confidence map of the target DT Perform derivative operation to obtain directional derivative image;

[0009] S3: Based on the maximum point coordinate value and the minimum point coordinate value of the directional derivative image, the center position coordinates of the small infrared target are obtained.

[0010] Furthermore, step S1 specifically includes the following steps:

[0011] S11: Using the gradient operator KG to suppress the background of the original infrared image, and obtaining a background suppressed image;

[0012] S12: performing image segmentation on the background suppression image based on a sliding window of an adjacent background region segmentation target extraction algorithm to obtain a target region subwindow, adjacent background subwindows of different background regions, and distant background subwindows of different background regions;

[0013] S13: Based on the average grayscale values ​​of the pixels in the target area subwindow, the adjacent background subwindows of different background areas, and the distant background subwindows of different background areas, a target confidence map of the infrared small target is calculated.

[0014] Furthermore, in step S11, the gradient operator KG is: .

[0015] Furthermore, in step S11, the formula used to suppress the background of the infrared original image using the gradient operator KG is:

[0016] ;

[0017] in, is the background suppression image, is the original image I ( ) in which the coordinates are The pixel value of The gradient operator KG is in coordinates The value at .

[0018] Further, in step S13, the step of calculating the average grayscale value of each pixel of the target area subwindow, the adjacent background subwindow of the different background areas, and the distant background subwindow of the different background areas specifically includes:

[0019] The average grayscale value of each sub-window is calculated in turn by the following formula:

[0020] ;

[0021] in, is the average gray value of the i-th sub-window, N is the total number of pixels in the current sub-window, For the The first The gray value of pixels;

[0022] Calculate the average gray value of the sub-window of the target area:

[0023] ;

[0024] in, is the average gray value of the sub-window of the target area, A is the total number of sub-windows in the target area, is the average gray value of the i-th sub-window of the target area;

[0025] Calculate the average grayscale value of each far background subwindow in the cth background area:

[0026] ;

[0027] in, is the average grayscale value of each far background sub-window of the c-th background area, C is the total number of far background sub-windows of the c-th background area, is the average gray value of the i-th far background subwindow of the c-th background area;

[0028] Calculate the average grayscale value of each adjacent background subwindow of the cth background area:

[0029] ;

[0030] in, is the average grayscale value of each adjacent background sub-window of the c-th background area, E is the total number of adjacent background sub-windows of the c-th background area, is the average gray value of the ith adjacent background subwindow of the cth background area.

[0031] Furthermore, the step S13 specifically includes the following steps:

[0032] S131: Calculate the first equivalent average grayscale value of different background areas by the following formula:

[0033] ;

[0034] in, is the first equivalent average gray value of the cth background area, is the maximum value function;

[0035] S132: Based on the first equivalent average grayscale values ​​of different background areas, a first confidence map is obtained by the following formula:

[0036] ;

[0037] in, is the first confidence map;

[0038] S133: Calculate the second equivalent average grayscale value of different background areas by the following formula:

[0039] ;

[0040] in, is the second equivalent average gray value of the cth background area, is the averaging function;

[0041] S134: Based on the second equivalent average grayscale values ​​of different background areas, a second confidence map is obtained by the following formula:

[0042] ;

[0043] in, is the second confidence map;

[0044] S135: Obtain the target confidence map of the infrared small target by the following formula :

[0045] .

[0046] Further, in step S2, the directional derivative image includes a horizontal directional derivative image and a vertical directional derivative image.

[0047] Furthermore, in step S2, the target confidence map DT Perform derivative operations along the horizontal direction to obtain the horizontal derivative image :

[0048] ;

[0049] ;

[0050] ;

[0051] ;

[0052] in, , and is the derivative operator, is the horizontal and vertical coordinates of the derivative operator value, Target confidence map DT In coordinates The pixel value of .

[0053] Target confidence map DT Perform derivative operations along the vertical direction to obtain the vertical derivative image :

[0054] ;

[0055] Where T is the transpose operation of the matrix.

[0056] Furthermore, step S3 specifically includes the following steps:

[0057] S31: Obtain the horizontal derivative image by the following formula And the vertical derivative image The coordinates of their respective maximum and minimum points are:

[0058] ;

[0059] ;

[0060] ;

[0061] ;

[0062] in, is the horizontal derivative image The coordinates of the maximum point, is the horizontal derivative image The coordinates of the minimum point of is the vertical derivative image The coordinates of the maximum point, is the vertical derivative image The coordinates of the minimum point of ;

[0063] S32: The center position coordinates of the infrared small target are obtained by the following formula:

[0064] ;

[0065] in, is the center coordinate of the small infrared target.

[0066] Compared with the prior art, the invention can achieve the following beneficial effects:

[0067] (1) The infrared small target center position estimation method described in the present invention solves the problem that most of the existing technologies extract infrared small targets from confidence maps through adaptive thresholds and cannot provide the center position coordinates of the infrared small targets, as well as the problem that the detection accuracy of the existing algorithms and the amount of algorithm calculation are mutually restricted. The present invention can directly provide the center position coordinate values ​​of the infrared small targets and can greatly improve the detection accuracy and detection efficiency.

[0068] (2) The infrared small target center position estimation method described in the present invention uses the gradient operator KG to suppress the background of the infrared original image, and the background suppression effect is good. In addition, the infrared original image is processed based on the target extraction algorithm based on the adjacent background area segmentation, and the adaptability to targets of different scales is high.

[0069] (3) The infrared small target center position estimation method described in the present invention has a small amount of calculation and can greatly improve the detection efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] The drawings constituting part of the present invention are used to provide a further understanding of the present invention. The exemplary embodiments and descriptions of the present invention are used to explain the present invention and do not constitute an improper limitation on the present invention. In the drawings:

[0071] Figure 1 A schematic diagram of a flow chart of a method for estimating the center position of an infrared small target according to an embodiment of the present invention;

[0072] Figure 2 The original infrared image described in the embodiment of the present invention;

[0073] Figure 3 To create a background suppression image as described in the embodiment of the present invention;

[0074] Figure 4 A schematic diagram of the structure of a sliding window of an algorithm for extracting a target by segmenting an adjacent background region according to an embodiment of the present invention;

[0075] Figure 5 A schematic diagram of a structure for further dividing a sliding window according to an embodiment of the present invention;

[0076] Figure 6 A comparison chart obtained by processing the original infrared image based on the adjacent background area segmentation target extraction algorithm described in the embodiment of the present invention;

[0077] Figure 7 A three-dimensional schematic diagram of a horizontal derivative image according to an embodiment of the present invention;

[0078] Figure 8A three-dimensional schematic diagram of a vertical derivative image according to an embodiment of the present invention. DETAILED DESCRIPTION

[0079] In order to make the purpose, technical solution and advantages of the invention more clear, the invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described here are only used to explain the invention and do not constitute a limitation of the invention.

[0080] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0081] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In addition, the terms "first", "second", etc. are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, features defined as "first", "second", etc. may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, "multiple" means two or more.

[0082] In the description of the invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installation", "connection" and "connection" 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 it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the invention can be understood according to specific circumstances.

[0083] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments.

[0084] like Figure 1 As shown, the present invention provides a method for estimating the center position of a small infrared target, which specifically includes the following steps:

[0085] S1: Process the original infrared image based on the target extraction algorithm for segmentation of the adjacent background area to obtain the target confidence map of the infrared small target DT ;

[0086] S11: Using the gradient operator KG to suppress the background of the original infrared image, and obtaining a background suppressed image;

[0087] The gradient operator KG is: .

[0088] The formula used to suppress the background of the original infrared image using the gradient operator KG is:

[0089] ;

[0090] in, is the background suppression image, is the original image I ( ) in which the coordinates are The pixel value of The gradient operator KG is in coordinates The value at .

[0091] S12: performing image segmentation on the background suppression image based on a sliding window of an adjacent background region segmentation target extraction algorithm to obtain a target region subwindow, adjacent background subwindows of different background regions, and distant background subwindows of different background regions;

[0092] S13: Based on the average grayscale values ​​of the pixels in the target area subwindow, the adjacent background subwindows of different background areas, and the distant background subwindows of different background areas, a target confidence map of the infrared small target is calculated.

[0093] S131: Calculate the first equivalent average grayscale value of different background areas by the following formula:

[0094] ;

[0095] in, is the first equivalent average gray value of the cth background area, is the maximum value function;

[0096] S132: Based on the first equivalent average grayscale values ​​of different background areas, a first confidence map is obtained by the following formula:

[0097] ;

[0098] in, is the first confidence map;

[0099] S133: Calculate the second equivalent average grayscale value of different background areas by the following formula:

[0100] ;

[0101] in, is the second equivalent average gray value of the cth background area, is the averaging function;

[0102] S134: Based on the second equivalent average grayscale values ​​of different background areas, a second confidence map is obtained by the following formula:

[0103] ;

[0104] in, is the second confidence map;

[0105] S135: Obtain the target confidence map of the infrared small target by the following formula :

[0106] .

[0107] In step S13, the steps of calculating the average grayscale values ​​of the pixels of the target area subwindow, the adjacent background subwindows of different background areas, and the distant background subwindows of different background areas specifically include:

[0108] The average grayscale value of each sub-window is calculated in turn by the following formula:

[0109] ;

[0110] in, is the average gray value of the i-th sub-window, N is the total number of pixels in the current sub-window, For the The first The gray value of pixels;

[0111] Calculate the average gray value of the sub-window of the target area:

[0112] ;

[0113] in, is the average gray value of the sub-window of the target area, A is the total number of sub-windows in the target area, is the average gray value of the i-th sub-window of the target area;

[0114] Calculate the average grayscale value of each far background subwindow in the cth background area:

[0115] ;

[0116] in, is the average grayscale value of each far background sub-window of the c-th background area, C is the total number of far background sub-windows of the c-th background area, is the average gray value of the i-th far background subwindow of the c-th background area;

[0117] Calculate the average grayscale value of each adjacent background subwindow of the cth background area:

[0118] ;

[0119] in, is the average grayscale value of each adjacent background sub-window of the c-th background area, E is the total number of adjacent background sub-windows of the c-th background area, is the average gray value of the ith adjacent background subwindow of the cth background area.

[0120] S2: Confidence map of the target DT A derivative operation is performed to obtain a directional derivative image, which includes a horizontal directional derivative image and a vertical directional derivative image.

[0121] In step S2, the target confidence map DT Perform derivative operations along the horizontal direction to obtain the horizontal derivative image :

[0122] ;

[0123] ;

[0124] ;

[0125] ;

[0126] in, , and is the derivative operator, is the horizontal and vertical coordinates of the derivative operator value, Target confidence map DT In coordinates The pixel value of .

[0127] Target confidence map DT Perform derivative operations along the vertical direction to obtain the vertical derivative image :

[0128] ;

[0129] Where T is the transpose operation of the matrix.

[0130] S3: Based on the maximum point coordinate value and the minimum point coordinate value of the directional derivative image, the center position coordinates of the small infrared target are obtained.

[0131] S31: Obtain the horizontal derivative image by the following formula And the vertical derivative image The coordinates of their respective maximum and minimum points are:

[0132] ;

[0133] ;

[0134] ;

[0135] ;

[0136] in, is the horizontal derivative image The coordinates of the maximum point, is the horizontal derivative image The coordinates of the minimum point of is the vertical derivative image The coordinates of the maximum point, is the vertical derivative image The coordinates of the minimum point of ;

[0137] S32: The center position coordinates of the infrared small target are obtained by the following formula:

[0138] ;

[0139] in, is the center coordinate of the small infrared target.

[0140] Example 1

[0141] For ease of understanding, the solution of the present invention is described in detail below with reference to actual illustrations.

[0142] Infrared raw image like Figure 2 As shown, the infrared small target center position estimation method provided by the embodiment of the present invention specifically includes the following steps:

[0143] S1: Process the original infrared image based on the target extraction algorithm for segmentation of the adjacent background area to obtain the target confidence map of the infrared small target DT ;

[0144] S11: Use the gradient operator with coefficient KG to filter the original infrared image after mean filtering Perform background suppression to obtain Figure 3 Background suppressed image shown.

[0145] The formula used to suppress the background of the original infrared image using the gradient operator KG is as follows:

[0146] ;

[0147] in, is the background suppression image, is the original image I ( ) in which the coordinates are The pixel value of .

[0148] The gradient operator KG has the following form:

[0149] ;

[0150] S12: performing image segmentation on the background suppression image based on a sliding window of an adjacent background region segmentation target extraction algorithm to obtain a target region subwindow, adjacent background subwindows of different background regions, and distant background subwindows of different background regions;

[0151] The sliding window structure of the target extraction algorithm for segmentation of the adjacent background area is as follows: Figure 4 As shown in the figure, the sliding window contains 9 areas. The area T in the center represents the target area, and the surrounding areas B1 to B8 represent the background area. The light pink area represents the far background area, represented by BF, and the orange area represents the adjacent background area, represented by BN. Each area is divided into fine parts, such as Figure 5 As shown, each area contains 4 sub-windows, totaling 36 sub-windows; the target area T includes sub-window T15, sub-window T16, sub-window T21, and sub-window T22; the background area B1 includes sub-window BF1, sub-window BF2, and sub-window BF7 representing far background sub-windows, and sub-window BN8 representing adjacent background sub-windows; the background area B2 includes sub-windows BF13 and BF19 representing far background sub-windows, and sub-windows BN14 and BN20 representing adjacent background sub-windows; B3~B8 are divided into far background sub-windows and adjacent background sub-windows by analogy.

[0152] S13: Based on the average grayscale values ​​of the pixels in the target area subwindow, the adjacent background subwindows of different background areas, and the distant background subwindows of different background areas, a target confidence map of the infrared small target is calculated.

[0153] The steps of calculating the average grayscale value of each pixel of the target area subwindow, the adjacent background subwindow of different background areas, and the distant background subwindow of different background areas specifically include:

[0154] The average grayscale value of each sub-window is calculated in turn by the following formula:

[0155] ;

[0156] in, is the average gray value of the i-th sub-window, N is the total number of pixels in the current sub-window, For the The first The gray value of a pixel, ;

[0157] Calculate the average gray value of the sub-window of the target area:

[0158] ;

[0159] in, is the average gray value of the sub-window of the target area, A is the total number of sub-windows in the target area, is the average gray value of the i-th sub-window of the target area;

[0160] That is, the average gray value of the sub-window pixels in the target area T can be expressed as:

[0161] ;

[0162] Calculate the average grayscale value of each far background subwindow in the cth background area:

[0163] ;

[0164] in, is the average grayscale value of each far background sub-window of the c-th background area, C is the total number of far background sub-windows of the c-th background area, is the average gray value of the i-th far background subwindow of the c-th background area;

[0165] That is, the average gray value of the far background sub-window pixels in the background area B1 can be expressed as:

[0166] ;

[0167] The average gray value of the far background sub-window pixels in the background area B2 can be expressed as:

[0168] ;

[0169] The average gray value of the far background sub-window pixels in the background area B3 can be expressed as:

[0170] ;

[0171] The average gray value of the far background sub-window pixels in the background area B4 can be expressed as:

[0172] ;

[0173] The average gray value of the far background sub-window pixels in the background area B5 can be expressed as:

[0174] ;

[0175] The average gray value of the far background sub-window pixels in the background area B6 can be expressed as:

[0176] ;

[0177] The average gray value of the far background sub-window pixels in the background area B7 can be expressed as:

[0178] ;

[0179] The average grayscale value of the far background sub-window pixels in the background area B8 can be expressed as:

[0180] ;

[0181] Calculate the average grayscale value of each adjacent background subwindow of the cth background area:

[0182] ;

[0183] in, is the average grayscale value of each adjacent background sub-window of the c-th background area, E is the total number of adjacent background sub-windows of the c-th background area, is the average gray value of the ith adjacent background subwindow of the cth background area.

[0184] The average grayscale value of the adjacent background sub-window pixels in the background area B1 can be expressed as:

[0185] ;

[0186] The average grayscale value of the adjacent background sub-window pixels in the background area B2 can be expressed as:

[0187] ;

[0188] The average grayscale value of the adjacent background sub-window pixels in the background area B3 can be expressed as:

[0189] ;

[0190] The average grayscale value of the adjacent background sub-window pixels in the background area B4 can be expressed as:

[0191] ;

[0192] The average grayscale value of the adjacent background sub-window pixels in the background area B5 can be expressed as:

[0193] ;

[0194] The average grayscale value of the adjacent background sub-window pixels in the background area B6 can be expressed as:

[0195] ;

[0196] The average grayscale value of the adjacent background sub-window pixels of the background area B7 can be expressed as:

[0197] ;

[0198] The average grayscale value of the adjacent background sub-window pixels in the background area B8 can be expressed as:

[0199] ;

[0200] S131: Calculate the first equivalent average grayscale value of different background areas by the following formula (the equivalent average grayscale value of the background area is the larger value of the average grayscale value of the distant background and the adjacent background sub-window pixels):

[0201] ;

[0202] in, is the background area when obtaining the confidence map DT1 The equivalent average gray value is .

[0203] S132: Based on the first equivalent average grayscale value () of different background areas, a first confidence map is obtained by the following formula:

[0204] ;

[0205] in, is the first confidence map;

[0206] S133: Calculate the second equivalent average grayscale value of different background areas by the following formula:

[0207] ;

[0208] in, is the background area when obtaining the confidence map DT2 The equivalent average gray value is .

[0209] S134: Based on the second equivalent average gray value () of different background areas, a second confidence map is obtained by the following formula:

[0210] ;

[0211] in, is the second confidence map;

[0212] S135: Target confidence map of small infrared targets obtained based on the target extraction algorithm for segmentation of adjacent background areas DT As shown below:

[0213] .

[0214] The infrared original image (upper left) is processed based on the target extraction algorithm for segmentation of the adjacent background area, and the first confidence map (upper right), the second confidence map (lower left) and the target confidence map (lower right) are obtained as shown in the figure. Figure 6 shown.

[0215] S2: Confidence map of the target DT A derivative operation is performed to obtain a directional derivative image, which includes a horizontal directional derivative image and a vertical directional derivative image.

[0216] like Figure 7-Figure 8 As shown, the target confidence map DT Perform derivative operations along the horizontal direction to obtain the horizontal derivative image :

[0217] ;

[0218] ;

[0219] ;

[0220] ;

[0221] in, , and is the derivative operator, are the horizontal and vertical coordinates of the derivative operator value.

[0222] Target confidence map DT Perform derivative operations along the vertical direction to obtain the vertical derivative image :

[0223] ;

[0224] Where T is the transpose operation of the matrix.

[0225] S3: Based on the maximum point coordinate value and the minimum point coordinate value of the directional derivative image, the center position coordinates of the small infrared target are obtained.

[0226] S31: Obtain the horizontal derivative image by the following formula And the vertical derivative image The coordinates of their respective maximum and minimum points are:

[0227] ;

[0228] ;

[0229] ;

[0230] ;

[0231] in, is the horizontal derivative image The coordinates of the maximum point, is the horizontal derivative image The coordinates of the minimum point of is the vertical derivative image The coordinates of the maximum point, is the vertical derivative image The coordinates of the minimum point of ;

[0232] S32: The center position coordinates of the infrared small target are obtained by the following formula:

[0233] ;

[0234] in, is the center coordinate of the small infrared target.

[0235] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the disclosure of the present invention can be performed in parallel, sequentially or in different orders, as long as the desired results of the technical solution disclosed in the present invention can be achieved, and this document does not limit this.

[0236] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for estimating the center position of a small infrared target, characterized by: The specific steps include: S1: Process the original infrared image based on the target extraction algorithm for segmentation of the adjacent background area to obtain the target confidence map of the infrared small target DT ; S2: Confidence map of the target DT Perform derivative operation to obtain directional derivative image; Target confidence map DT Perform derivative operations along the horizontal direction to obtain the horizontal derivative image : ； ； ； ； in, , and is the derivative operator, is the horizontal and vertical coordinates of the derivative operator value, Target confidence map DT In coordinates The pixel value of Target confidence map DT Perform derivative operations along the vertical direction to obtain the vertical derivative image : ； Where T is the transpose operation of the matrix; S3: Based on the maximum point coordinate value and the minimum point coordinate value of the directional derivative image, the center position coordinates of the small infrared target are obtained.

2. The method for estimating the center position of a small infrared target according to claim 1, characterized in that: Step S1 specifically includes the following steps: S11: Using the gradient operator KG to suppress the background of the original infrared image, and obtaining a background suppressed image; S12: performing image segmentation on the background suppression image based on a sliding window of an adjacent background region segmentation target extraction algorithm to obtain a target region subwindow, adjacent background subwindows of different background regions, and distant background subwindows of different background regions; S13: Based on the average grayscale values ​​of the pixels in the target area subwindow, the adjacent background subwindows of different background areas, and the distant background subwindows of different background areas, a target confidence map of the infrared small target is calculated.

3. The method for estimating the center position of a small infrared target according to claim 2, characterized in that: In step S11, the gradient operator KG is: 。 4. The method for estimating the center position of a small infrared target according to claim 2, characterized in that: In step S11, the formula used to suppress the background of the infrared original image using the gradient operator KG is: ； in, is the background suppression image, is the original image I ( ) in which the coordinates are The pixel value of The gradient operator KG is in coordinates The value at .

5. The method for estimating the center position of a small infrared target according to claim 2, characterized in that: In step S13, the steps of calculating the average grayscale values ​​of the pixels of the target area subwindow, the adjacent background subwindows of different background areas, and the distant background subwindows of different background areas specifically include: The average grayscale value of each sub-window is calculated in turn by the following formula: ； in, is the average gray value of the i-th sub-window, N is the total number of pixels in the current sub-window, For the The first The gray value of pixels; Calculate the average gray value of the sub-window of the target area: ； in, is the average gray value of the sub-window of the target area, A is the total number of sub-windows in the target area, is the average gray value of the i-th sub-window of the target area; Calculate the average grayscale value of each far background subwindow in the cth background area: ； in, is the average grayscale value of each far background sub-window of the c-th background area, C is the total number of far background sub-windows of the c-th background area, is the average gray value of the i-th far background subwindow of the c-th background area; Calculate the average grayscale value of each adjacent background subwindow of the cth background area: ； in, is the average grayscale value of each adjacent background sub-window of the c-th background area, E is the total number of adjacent background sub-windows of the c-th background area, is the average gray value of the ith adjacent background subwindow of the cth background area.

6. The method for estimating the center position of a small infrared target according to claim 5, characterized in that: The step S13 specifically includes the following steps: S131: Calculate the first equivalent average grayscale value of different background areas by the following formula: ； in, is the first equivalent average gray value of the cth background area, is the maximum value function; S132: Based on the first equivalent average grayscale values ​​of different background areas, a first confidence map is obtained by the following formula: ； in, is the first confidence map; S133: Calculate the second equivalent average grayscale value of different background areas by the following formula: ； in, is the second equivalent average gray value of the cth background area, is the averaging function; S134: Based on the second equivalent average grayscale values ​​of different background areas, a second confidence map is obtained by the following formula: ； in, is the second confidence map; S135: Obtain the target confidence map of the infrared small target by the following formula : 。 7. The method for estimating the center position of a small infrared target according to claim 1, characterized in that: In step S2, the directional derivative image includes a horizontal directional derivative image and a vertical directional derivative image.

8. The method for estimating the center position of a small infrared target according to claim 1, characterized in that: Step S3 specifically includes the following steps: S31: Obtain the horizontal derivative image by the following formula And the vertical derivative image The coordinates of their respective maximum and minimum points are: ； ； ； ； in, is the horizontal derivative image The coordinates of the maximum point, is the horizontal derivative image The coordinates of the minimum point, is the vertical derivative image The coordinates of the maximum point, is the vertical derivative image The coordinates of the minimum point of ; S32: The center position coordinates of the infrared small target are obtained by the following formula: ； in, is the center coordinate of the small infrared target.

Images