CN118279397A - Infrared dim target rapid detection method based on first-order directional derivative - Google Patents

Abstract

The invention belongs to the technical field of infrared image processing and target detection, and particularly relates to a method for rapidly detecting an infrared weak and small target based on a first-order directional derivative, which comprises the following steps: s1: calculating first-order directional derivatives of the original infrared image processed by the Gaussian differential filter along 0-degree and 90-degree directions based on Facet models; s2: performing amplitude normalization processing on the first and second first-order direction derivative images; s3: threshold detection is carried out on the first normalized image and the second normalized image; s4: judging whether each candidate point of the first normalized image and the second normalized image has a target position or not; s5: obtaining first and second candidate target images according to the calculation result of the step S4; s6: performing image fusion on the first candidate target image and the second candidate target image to obtain an output image containing an infrared weak and small target; s7: and performing target detection with a pixel value of 1 on the output image to obtain a final detection result. The invention has the advantages of high detection precision and high detection efficiency.

Description

A fast detection method for infrared dim small targets based on first-order directional derivative

Technical Field

The invention belongs to the technical field of infrared image processing and target detection, and in particular relates to a method for rapid detection of infrared dim small targets based on first-order directional derivatives.

Background technique

Infrared small target detection technology is an important part of infrared search and tracking system. Small target detection based on infrared images has important applications in missile warning, missile interception and other fields. Affected by long-distance imaging and random complex background interference, the targets detected by infrared imaging systems have the characteristics of small imaging size, weak radiation energy, difficult geometric contours, susceptibility to complex background interference, and low signal-to-noise ratio, that is, the detection of infrared small targets is very difficult. At present, infrared small target detection can be divided into two categories according to the input source, namely infrared small target detection based on image sequence and infrared small target detection based on single frame image. Since the infrared small target detection algorithm based on image sequence has a large amount of calculation, high requirements on processor and hardware resources, poor real-time performance, and lower efficiency than the infrared small target detection algorithm based on single frame image, the research on infrared small target detection based on single frame image has gradually become the mainstream. Infrared weak target detection methods based on single-frame images include filtering methods based on spatial domain or frequency domain, contrast methods based on visual systems, and methods based on data structures. The first two are easy to implement, but have high false alarm rates and low spatial resolution under complex backgrounds, while methods based on data structures have large computational complexity and are not easy to implement in engineering.

Summary of the invention

In view of this, the present invention aims to provide a rapid detection method for infrared dim small targets based on first-order directional derivatives, so as to solve the problems of false alarm, mutual constraint between spatial resolution and algorithm calculation amount in existing infrared dim small target detection methods, and to improve the detection accuracy and efficiency of infrared dim small targets and reduce the probability of false alarm.

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

A method for rapid detection of infrared dim small targets based on first-order directional derivatives specifically comprises the following steps:

S1: The original infrared image is processed by using a Gaussian difference filter, and the first-order directional derivative of the original infrared image processed by the Gaussian difference filter along the 0 degree direction is calculated based on the Facet model. The first-order directional derivative of the original infrared image processed by the Gaussian difference filter along the 90 degree direction is calculated based on the Facet model, and the first first-order directional derivative image and the second first-order directional derivative image are obtained correspondingly;

S2: performing amplitude normalization processing on the first first-order directional derivative image and the second first-order directional derivative image simultaneously, and obtaining a first normalized image and a second normalized image correspondingly;

S3: Performing threshold detection on the first normalized image and the second normalized image simultaneously to obtain all candidate points of the first normalized image and all candidate points of the second normalized image;

S4: simultaneously judging whether each candidate point of the first normalized image has a target position along the 0° direction, and judging whether each candidate point of the second normalized image has a target position along the 90° direction;

S5: According to the judgment result of step S4, a first candidate target image and a second candidate target image are obtained;

S6: fusing the first candidate target image and the second candidate target image to obtain an output image containing a small infrared target;

S7: Perform target detection with a pixel value of 1 on the output image, determine the center position of the infrared weak small target, and use the center position of the infrared weak small target as the final detection result.

Furthermore, in step S1, the calculation formula for calculating the first-order directional derivative along the 0 degree direction and the first-order directional derivative along the 90 degree direction of the original infrared image processed by the Gaussian difference filter based on the Facet model is:

;

;

in, is the first-order directional derivative image, is the second first-order directional derivative image, is the coordinate of the pixel point, , , , , , are the interpolation coefficients of the Facet model.

Furthermore, the calculation formula of the interpolation coefficient of the Facet model is:

;

;

;

;

;

;

;

in, is the i-th interpolation coefficient of the Facet model, is the kernel coefficient corresponding to the i-th interpolation coefficient, is the output of the Gaussian difference filter, and ^T is the transpose operation of the matrix.

Further, in step S2, the first normalized image and the second normalized image satisfy the following formula:

;

;

in, is the first normalized image, is the second normalized image, is the coordinate of the pixel point.

Further, in step S3, the threshold is set to , based on the threshold, and all candidate points of the first normalized image and all candidate points of the second normalized image are obtained by the following formula:

;

;

in, is the i-th candidate point of the first normalized image, is the i-th candidate point of the second normalized image, For any coordinate Pixels.

Furthermore, in step S4, the specific steps of judging whether each candidate point of the first normalized image has a target position along the 0° direction include:

S411: taking the i-th candidate point in the first normalized image as a candidate point to be processed, and setting a local area R based on the candidate point to be processed, where the local area R is a square centered on the candidate point to be processed, i={1, 2, 3, ..., n}, and n is the total number of candidate points;

S412: According to the pixel values of each pixel point contained in the local area R, select the maximum pixel value point and the minimum pixel value point in the local area R. When the maximum pixel value point and the minimum pixel value point satisfy the following formula, execute step S413. Otherwise, the current candidate point is regarded as a target position that does not exist along the 0° direction, and the i+1th candidate point is used as a candidate point to be processed in the first normalized image, and execute step S411:

;

;

;

in, is the pixel value of the maximum pixel value point, is the pixel value of the minimum pixel value point, is the position coordinate of the maximum pixel value point, is the position coordinate of the point with the minimum pixel value, is the amplitude threshold value, is the spatial distance threshold;

S413: Calculate the target position of the current candidate point by the following formula:

;

;

in,( ) is the coordinate of the target position of the current candidate point along the 0° direction;

S414: Repeat steps S411-S413 to calculate the coordinates of the target positions of all candidate points of the first normalized image along the 0° direction.

Further, in step S4, the specific steps of judging whether each candidate point of the second normalized image has a target position along the 90° direction include:

S421: taking the i-th candidate point in the second normalized image as a candidate point to be processed, and setting a local area R based on the candidate point to be processed, where the local area R is a square centered on the candidate point to be processed, i={1, 2, 3, ..., n}, where n is the total number of candidate points;

S422: According to the pixel values of each pixel point contained in the local area R, select the maximum pixel value point and the minimum pixel value point in the local area R. When the maximum pixel value point and the minimum pixel value point satisfy the following formula, execute step S423. Otherwise, the current candidate point is regarded as a target position that does not exist along the 90° direction for judgment, and the i+1th candidate point is used as the candidate point to be processed in the second normalized image, and execute step S421:

;

;

;

in, is the pixel value of the maximum pixel value point, is the pixel value of the minimum pixel value point, is the position coordinate of the maximum pixel value point, is the position coordinate of the point with the minimum pixel value, is the amplitude threshold value, is the spatial distance threshold;

S423: Calculate the target position of the current candidate point by the following formula:

;

;

in,( ) is the coordinate of the target position of the current candidate point along the 90° direction;

S424: Repeat steps S421-S423 to calculate the target positions of all candidate points of the second normalized image along the 90° direction.

Further, in step S5, in the first normalized image, the pixel values of the candidate points with the target position along the 0° direction are all set to 1, and the pixel values of the candidate points without the target position along the 0° direction are all set to 0, to obtain the first candidate target image:

In the second normalized image, the pixel values of the candidate points having the target position along the 90° direction are all set to 1, and the pixel values of the candidate points not having the target position along the 90° direction are all set to 0, so as to obtain a second candidate target image.

Furthermore, in step S6, the calculation formula for image fusion of the first candidate target image and the second candidate target image is:

;

in, For the output image, is the first candidate target image, is the second candidate target image.

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

(1) The present invention creates a method for rapid detection of infrared dim small targets based on first-order directional derivatives. By utilizing the different distribution characteristics of first-order derivative images of infrared dim small targets in different directions, the candidate target images in the two directions of 0° and 90° are fused to obtain an output image. Target detection with a pixel value of 1 is performed on the output image to determine the center position of the infrared dim small target. The center position of the infrared dim small target is used as the final detection result. The entire detection process greatly improves the detection accuracy and efficiency of infrared dim small targets and reduces the false alarm probability.

(2) The infrared dim small target rapid detection method based on the first-order directional derivative created by the present invention can realize the accurate positioning of the target position. Since the detection process in two directions can be calculated in parallel, the infrared dim small target rapid detection method of the present invention has the advantages of high real-time performance and high detection efficiency.

BRIEF DESCRIPTION OF THE DRAWINGS

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:

FIG1 is a schematic flow chart of a method for rapid detection of infrared dim small targets based on first-order directional derivatives according to an embodiment of the present invention;

FIG2 is a schematic diagram of a structure for determining the coordinates of a target position in a local area according to an embodiment of the present invention;

FIG3 is an original infrared image according to an embodiment of the present invention;

FIG4 is a first-order directional derivative image according to an embodiment of the present invention;

FIG5 is a second first-order directional derivative image according to an embodiment of the present invention;

FIG6 is a three-dimensional simulation schematic diagram of obtaining candidate points of a first normalized image according to an embodiment of the present invention;

FIG7 is a schematic diagram of a three-dimensional simulation of candidate points for obtaining a second normalized image according to an embodiment of the present invention;

FIG8 is a first candidate target image according to an embodiment of the present invention;

FIG9 is a second candidate target image according to an embodiment of the present invention;

FIG. 10 is an image of the center position of a small infrared target according to an embodiment of the present invention.

Detailed ways

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.

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.

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.

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.

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

As shown in FIG1 , an infrared dim small target rapid detection method based on first-order directional derivative provided by an embodiment of the present invention specifically comprises the following steps:

S1: The original infrared image is processed by using a Gaussian difference filter, and the first-order directional derivative of the original infrared image processed by the Gaussian difference filter along the 0 degree direction is calculated based on the Facet model. The first-order directional derivative of the original infrared image processed by the Gaussian difference filter along the 90 degree direction is calculated based on the Facet model, and the first first-order directional derivative image and the second first-order directional derivative image are obtained accordingly.

In step S1, the calculation formula for calculating the first-order directional derivative along the 0 degree direction and the first-order directional derivative along the 90 degree direction of the original infrared image processed by the Gaussian difference filter based on the Facet model is:

;

;

in, is the first-order directional derivative image, is the second first-order directional derivative image, is the coordinate of the pixel point, , , , , , are the interpolation coefficients of the Facet model. The calculation formula for the interpolation coefficients of the Facet model is:

;

;

;

;

;

;

;

in, is the i-th interpolation coefficient of the Facet model, is the kernel coefficient corresponding to the i-th interpolation coefficient, is the output of the Gaussian difference filter, and ^T is the transpose operation of the matrix.

S2: performing amplitude normalization processing on the first first-order directional derivative image and the second first-order directional derivative image simultaneously, and obtaining a first normalized image and a second normalized image correspondingly.

In step S2, the first normalized image and the second normalized image satisfy the following equation:

;

;

in, is the first normalized image, is the second normalized image, is the coordinate of the pixel point.

S3: Perform threshold detection on the first normalized image and the second normalized image simultaneously to obtain all candidate points of the first normalized image and all candidate points of the second normalized image.

According to the actual application, the threshold Take any value from {0.5, 0.6, 0.7, 0.8, 0.9}.

In step S3, the threshold is set to , based on the threshold, and all candidate points of the first normalized image and all candidate points of the second normalized image are obtained by the following formula:

;

;

in, is the i-th candidate point of the first normalized image, is the i-th candidate point of the second normalized image, For any coordinate Pixels.

S4: It is simultaneously determined whether each candidate point of the first normalized image has a target position along the 0° direction, and whether each candidate point of the second normalized image has a target position along the 90° direction.

As shown in FIG. 2 , in step S4, the specific steps of judging whether each candidate point of the first normalized image has a target position along the 0° direction include:

S411: taking the i-th candidate point in the first normalized image as a candidate point to be processed, and setting a local area R based on the candidate point to be processed, where the local area R is a square centered on the candidate point to be processed, i={1, 2, 3, ..., n}, and n is the total number of candidate points;

S412: According to the pixel values of each pixel point contained in the local area R, select the maximum pixel value point and the minimum pixel value point in the local area R. When the maximum pixel value point and the minimum pixel value point satisfy the following formula, execute step S413. Otherwise, the current candidate point is regarded as a target position that does not exist along the 0° direction, and the i+1th candidate point is used as a candidate point to be processed in the first normalized image, and execute step S411:

;

;

;

in, is the pixel value of the maximum pixel value point, is the pixel value of the minimum pixel value point, is the position coordinate of the maximum pixel value point, is the position coordinate of the point with the minimum pixel value, is the amplitude threshold value, is the spatial distance threshold;

S413: Calculate the target position of the current candidate point by the following formula:

;

;

in,( ) is the coordinate of the target position of the current candidate point along the 0° direction;

S414: Repeat steps S411-S413 to calculate the coordinates of the target positions of all candidate points of the first normalized image along the 0° direction.

In step S4, the specific steps of judging whether each candidate point of the second normalized image has a target position along the 90° direction include:

S421: taking the i-th candidate point in the second normalized image as a candidate point to be processed, and setting a local area R based on the candidate point to be processed, where the local area R is a square with a side length of m and the candidate point to be processed as the center, i={1, 2, 3, ..., n}, n is the total number of candidate points, and m is in the range of 15 to 30, which can be adjusted according to actual conditions;

S422: According to the pixel values of each pixel point contained in the local area R, select the maximum pixel value point and the minimum pixel value point in the local area R. When the maximum pixel value point and the minimum pixel value point satisfy the following formula, execute step S423. Otherwise, the current candidate point is regarded as a target position that does not exist along the 90° direction for judgment, and the i+1th candidate point is used as the candidate point to be processed in the second normalized image, and execute step S421:

;

;

;

in, is the pixel value of the maximum pixel value point, is the pixel value of the minimum pixel value point, is the position coordinate of the maximum pixel value point, is the position coordinate of the point with the minimum pixel value, is the amplitude threshold value, is the spatial distance threshold;

S423: Calculate the target position of the current candidate point by the following formula:

;

;

in,( ) is the coordinate of the target position of the current candidate point along the 90° direction;

S424: Repeat steps S421-S423 to calculate the target positions of all candidate points of the second normalized image along the 90° direction.

S5: According to the judgment result of step S4, a first candidate target image and a second candidate target image are obtained.

In step S5, in the first normalized image, the pixel values of the candidate points with the target position along the 0° direction are all set to 1, and the pixel values of the candidate points without the target position along the 0° direction are all set to 0, so as to obtain the first candidate target image:

In the second normalized image, the pixel values of the candidate points having the target position along the 90° direction are all set to 1, and the pixel values of the candidate points not having the target position along the 90° direction are all set to 0, so as to obtain a second candidate target image.

S6: Fusing the first candidate target image and the second candidate target image to obtain an output image containing a small infrared target.

In step S6, the calculation formula for image fusion of the first candidate target image and the second candidate target image is:

;

in, For the output image, is the first candidate target image, is the second candidate target image.

S7: Perform target detection with a pixel value of 1 on the output image (the pixel value is either 0 or 1), determine the center position of the infrared weak small target, and use the center position of the infrared weak small target as the final detection result.

Example 1

An infrared dim small target rapid detection method based on first-order directional derivative provided by an embodiment of the present invention specifically comprises the following steps:

S1: Use a Gaussian difference filter to process the original infrared image shown in Figure 3, and calculate the first-order directional derivative along the 0 degree direction and the first-order directional derivative along the 90 degree direction of the original infrared image processed by the Gaussian difference filter based on the Facet model, and correspondingly obtain the first first-order directional derivative image shown in Figure 4 and the second first-order directional derivative image shown in Figure 5.

S2: performing amplitude normalization processing on the first first-order directional derivative image and the second first-order directional derivative image simultaneously, and obtaining a first normalized image and a second normalized image correspondingly.

S3: Perform threshold detection on the first normalized image and the second normalized image simultaneously to obtain all candidate points of the first normalized image and all candidate points of the second normalized image.

As shown in Figures 6 and 7, (The diamond area is ths), the candidate points of the first normalized image and the candidate points of the second normalized image are obtained by the following formula, and the points greater than ths are taken as candidate points and .

;

;

in, is the i-th candidate point of the first normalized image, is the i-th candidate point of the second normalized image, For any coordinate Pixels.

S4: It is simultaneously determined whether each candidate point of the first normalized image has a target position along the 0° direction, and whether each candidate point of the second normalized image has a target position along the 90° direction.

S5: According to the judgment result of step S4, a first candidate target image and a second candidate target image are obtained.

Take parameters , , the first candidate target image and the second candidate target image are shown in FIG8 and FIG9 respectively.

S6: Fusing the first candidate target image and the second candidate target image to obtain an output image containing a small infrared target.

In step S6, the calculation formula for image fusion of the first candidate target image and the second candidate target image is:

;

in, For the output image, is the first candidate target image, is the second candidate target image.

S7: Detect the target with a pixel value of 1 on the output image (the pixel value is either 0 or 1), determine the center position of the infrared weak target, and use the center position of the infrared weak target as the final detection result. The detection result is shown in Figure 10.

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.

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 (9)

1. A method for rapidly detecting infrared dim targets based on first-order directional derivatives is characterized by comprising the following steps of: the method specifically comprises the following steps:

S1: processing an original infrared image by using a Gaussian differential filter, calculating first-order directional derivative of the original infrared image processed by the Gaussian differential filter along the 0-degree direction based on a Facet model, and calculating first-order directional derivative of the original infrared image processed by the Gaussian differential filter along the 90-degree direction based on a Facet model, so as to correspondingly obtain a first-order directional derivative image and a second first-order directional derivative image;

S2: simultaneously carrying out amplitude normalization processing on the first-order direction derivative image and the second first-order direction derivative image, and correspondingly obtaining a first normalized image and a second normalized image;

S3: simultaneously carrying out threshold detection on the first normalized image and the second normalized image to obtain all candidate points of the first normalized image and all candidate points of the second normalized image;

S4: meanwhile, judging whether each candidate point of the first normalized image has a target position along the 0-degree direction and whether each candidate point of the second normalized image has a target position along the 90-degree direction;

s5: obtaining a first candidate target image and a second candidate target image according to the judging result of the step S4;

S6: performing image fusion on the first candidate target image and the second candidate target image to obtain an output image containing an infrared weak target;

S7: and detecting the target with the pixel value of 1 on the output image, determining the central position of the infrared weak target, and taking the central position of the infrared weak target as a final detection result.

2. The method for rapidly detecting the infrared small target based on the first-order directional derivative according to claim 1, wherein the method comprises the following steps of: in the step S1, a calculation formula for calculating the first-order directional derivative of the original infrared image processed by the gaussian differential filter along the 0 degree direction and the first-order directional derivative of the original infrared image along the 90 degree direction based on the Facet model is as follows:

；

；

Wherein, For the first-order directional derivative image,For the second first order directional derivative image,Is the coordinates of the pixel point and,、、、、、Are interpolation coefficients of Facet models.

3. The method for rapidly detecting the infrared small target based on the first-order directional derivative according to claim 2, wherein the method comprises the following steps of: the calculation formula of the interpolation coefficient of the Facet model is as follows:

；

；

；

；

；

；

；

Wherein, For the ith interpolation coefficient of Facet models,For the kernel coefficient corresponding to the i-th interpolation coefficient,T is the transpose of the matrix, which is the output of the Gaussian differential filter.

4. The method for rapidly detecting the infrared small target based on the first-order directional derivative according to claim 1, wherein the method comprises the following steps of: in the step S2, the first normalized image and the second normalized image satisfy the following formula:

；

；

Wherein, For the first normalized image, the first image is normalized,For the second normalized image, the second image is obtained,Is the coordinates of the pixel point.

5. The method for rapidly detecting the infrared small target based on the first-order directional derivative according to claim 4, wherein the method comprises the following steps of: in the step S3, the threshold is set to beBased on the threshold, all candidate points of the first normalized image and all candidate points of the second normalized image are obtained by:

；

；

Wherein, For the i-th candidate point of the first normalized image,For the i-th candidate point of the second normalized image,For any coordinatesIs a pixel of (a) a pixel of (b).

6. The method for rapidly detecting the infrared small target based on the first-order directional derivative according to claim 1, wherein the method comprises the following steps of: in the step S4, the specific step of determining whether the target position along the 0 ° direction exists at each candidate point of the first normalized image includes:

s411: taking the ith candidate point as a candidate point to be processed in the first normalized image, and setting a local area R based on the candidate point to be processed, wherein the local area R is a square taking the candidate point to be processed as a center, i= {1,2,3, …, n }, and n is the total number of the candidate points;

S412: selecting a maximum pixel value point and a minimum pixel value point in the local region R according to the pixel value of each pixel point included in the local region R, executing step S413 when the maximum pixel value point and the minimum pixel value point satisfy the following formula, otherwise, regarding the current candidate point as a target position along the 0 ° direction, and regarding the i+1th candidate point as a candidate point to be processed in the first normalized image, and executing step S411:

；

；

；

Wherein, The pixel value that is the maximum pixel value point,A pixel value that is a very small pixel value point,Is the position coordinates of the maximum pixel value point,Is the position coordinates of the very small pixel value point,For the amplitude threshold value,Is a spatial distance threshold;

s413: calculating the target position of the current candidate point by the following formula:

；

；

wherein, the method comprises the following steps of ) Coordinates of a target position of the current candidate point along the 0-degree direction;

S414: steps S411 to S413 are repeated, and coordinates of target positions of all the candidate points of the first normalized image in the 0 ° direction are calculated.

7. The method for rapidly detecting the infrared small target based on the first-order directional derivative according to claim 1, wherein the method comprises the following steps of: in the step S4, the specific step of determining whether the target position along the 90 ° direction exists at each candidate point of the second normalized image includes:

S421: taking the ith candidate point as a candidate point to be processed in the second normalized image, and setting a local area R based on the candidate point to be processed, wherein the local area R is a square taking the candidate point to be processed as a center, i= {1,2,3, …, n }, and n is the total number of the candidate points;

S422: selecting a maximum pixel value point and a minimum pixel value point in the local region R according to the pixel value of each pixel point included in the local region R, executing step S423 when the maximum pixel value point and the minimum pixel value point satisfy the following formula, otherwise, considering the current candidate point as not having a target position along the 90 ° direction, judging, and executing step S421 with the i+1st candidate point as the candidate point to be processed in the second normalized image:

；

；

；

Wherein, The pixel value that is the maximum pixel value point,A pixel value that is a very small pixel value point,Is the position coordinates of the maximum pixel value point,Is the position coordinates of the very small pixel value point,For the amplitude threshold value,Is a spatial distance threshold;

s423: calculating the target position of the current candidate point by the following formula:

；

；

wherein, the method comprises the following steps of ) Coordinates of a target position of the current candidate point along the 90-degree direction;

s424: repeating steps S421-S423, and calculating target positions of all candidate points of the second normalized image along the 90-degree direction.

8. The method for rapidly detecting the infrared small target based on the first-order directional derivative according to claim 1, wherein the method comprises the following steps of: in the step S5, in the first normalized image, the pixel values of the candidate points having the target position in the 0 ° direction are set to 1, and the pixel values of the candidate points not having the target position in the 0 ° direction are set to 0, to obtain a first candidate target image:

In the second normalized image, the pixel values of the candidate points having the target positions in the 90 ° direction are set to 1, and the pixel values of the candidate points having no target positions in the 90 ° direction are set to 0, to obtain a second candidate target image.

9. The method for rapidly detecting the infrared small target based on the first-order directional derivative according to claim 1, wherein the method comprises the following steps of: in the step S6, a calculation formula for performing image fusion on the first candidate target image and the second candidate target image is as follows:

；

Wherein, In order to output an image of the subject,For the first candidate object-image,Is the second candidate target image.