Infrared weak and small target detection method and device based on hypergraph

Abstract

The invention discloses an infrared weak and small target detection method and device based on a hypergraph. Comprising the following steps: 1) constructing a to-be-detected image sequence into a three-dimensional space-time tensor # imgabs0 #; 2) establishing a low-rank sparse tensor decomposition model; 3) designing a tensor nuclear norm based on Laplace; 4) designing a Laplacian regularization item of the hypergraph; 5) establishing a space-time tensor decomposition model based on a hypergraph regularization item; 6) solving the model established in the step 5) by using an ADMM-based algorithm to obtain a target detection result tensor; and (7) converting the obtained target detection result tensor into a target detection result sequence as an infrared weak and small target detection result to realize infrared weak and small target detection. According to the infrared weak and small target detection method based on the hypergraph, the detection performance of the infrared weak and small target can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of image processing, and in particular to a method and device for detecting infrared dim small targets based on a hypergraph. Background Art

[0002] Thermal infrared imaging has the characteristics of all-weather, strong penetration, thermal sensitivity, and strong concealment. It has been widely used in military reconnaissance, maritime search and rescue, photovoltaic power station operation and maintenance and other fields. Due to long-distance imaging, the target imaging area is small and the signal strength is weak, and it exists as a weak target in the infrared sequence image. At the same time, the complex background of infrared images is often mixed with various interferences, such as dense clouds, ocean clutter, etc. In addition, infrared weak target detection is one of the core technologies of infrared search and tracking systems, which puts forward requirements for improving detection accuracy and optimizing computing efficiency to meet the needs of practical applications. Therefore, it is urgent to study the weak target detection method of infrared sequence images, which is of great value and significance to ensure national defense security and public safety.

[0003] At present, infrared dim target detection technology has become a research hotspot in academia and industry. Its method system can be mainly divided into the following categories: detection methods based on background filtering, local contrast measurement, machine learning, deep learning and component analysis. The method based on background filtering suppresses background information through filtering technology to highlight the target signal; however, since the background model is difficult to accurately describe the background characteristics in complex scenes, this method performs poorly in environments with rapid background changes or complex clutter, limiting its practicality. The method based on local contrast measurement mainly relies on the characteristic differences between the target and the background in the local area, such as gradient, texture, etc.; although it has a good detection effect in some static scenes, it is highly sensitive to complex backgrounds and noise clutter, is easily affected by environmental conditions, and is difficult to maintain robustness in changing scenes. The method based on machine learning distinguishes the target from the background by building a classifier, which usually relies on manually designed features; however, the quality of feature design directly determines the detection performance. At the same time, the algorithm has a high dependence on training samples and poor robustness in unseen scenes. The deep learning-based method can autonomously extract multi-level features to improve detection accuracy; however, this method usually requires a large amount of labeled data and has high computational complexity, making it difficult to apply in real time in resource-constrained scenarios. The component analysis-based method decomposes the components in the image to separate the target and the background, and its performance is highly dependent on the accurate modeling of the intrinsic characteristics of each component; this method has certain advantages in dealing with noise and complex backgrounds, but its computational complexity and dependence on the model also pose challenges to practical applications.

[0004] Therefore, developing infrared dim small target detection methods with higher robustness, real-time and generalization capabilities remains an important research direction in this field. Summary of the invention

[0005] In view of the deficiencies in the prior art, the purpose of the present invention is to provide a method and device for infrared weak and small target detection based on a hypergraph, by constructing the infrared image sequence to be detected as a three-dimensional spatiotemporal tensor, making full use of the spatiotemporal information of the infrared image sequence, and carrying out low-rank sparse spatiotemporal tensor decomposition. Through the construction of the tensor nuclear norm based on Laplace calculation, the importance of different features in the background can be distinguished, and different weights can be assigned, so that more accurate low-rank feature characterization can be achieved. Considering the flexibility of the hypergraph, the image sequence to be detected is constructed as a hypergraph structure, which can capture more complex correlations from multiple frames at the same time, better represent cross-frame structural information, and further improve the accuracy of background estimation. Furthermore, a spatiotemporal tensor decomposition model based on a hypergraph is established, which has excellent target detection ability and background suppression ability. Through the comprehensive verification of the embodiments, it is proved that the infrared weak and small target detection method based on the hypergraph has excellent effects in improving detection performance and background suppression, and provides new ideas and methods for research and application in related fields.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The present invention discloses a method for detecting infrared small targets based on a hypergraph, which is characterized by comprising the following steps:

[0008] Step 1): Stack the thermal infrared image sequence to be detected in sequence according to the time dimension to construct a three-dimensional space-time tensor ;

[0009] Step 2): Using the 3D space-time tensor , establish a low-rank sparse decomposition model based on three-dimensional space-time tensor;

[0010] Step 3): Design a Laplace-based tensor nuclear norm to estimate the low-rank characteristics of the background;

[0011] Step 4): Each frame of the thermal infrared image sequence to be detected is constructed as an image block tensor as a graph node, and a hypergraph is constructed using the node set to design the hypergraph Laplace regularization term;

[0012] Step 5): Combine the Laplace-based tensor nuclear norm and the hypergraph Laplace regularization term to establish a hypergraph-based spatiotemporal tensor decomposition model;

[0013] Step 6): Using the ADMM-based optimization solution algorithm, solve the spatiotemporal tensor decomposition model constructed in step 5) to obtain the target detection result tensor;

[0014] Step 7): Convert the target detection result tensor into a target detection result sequence T as the infrared small target detection result to realize infrared small target detection.

[0015] The present invention also discloses an infrared dim small target detection device based on spatiotemporal background reconstruction for implementing the method, which comprises:

[0016] A three-dimensional space-time tensor construction module is used to construct the infrared image sequence to be detected into a three-dimensional space-time tensor;

[0017] A low-rank sparse decomposition model building module is used to build a low-rank sparse tensor decomposition model corresponding to a three-dimensional space-time tensor;

[0018] Laplace-based tensor nuclear norm design module, used to design Laplace-based tensor nuclear norm;

[0019] A hypergraph construction module, used for constructing a sequence of image frames to be detected into a hypergraph structure;

[0020] A hypergraph Laplace regularization construction module, used to construct a hypergraph Laplace regularization term corresponding to the hypergraph structure;

[0021] A hypergraph-based spatiotemporal tensor decomposition model building module, which is used to build a spatiotemporal tensor decomposition model based on a hypergraph regularization term;

[0022] Model optimization solution module, used to solve the spatiotemporal tensor decomposition model based on hypergraph regularization term using ADMM-based optimization algorithm;

[0023] The target detection result output module is used to output the infrared weak target detection result map of each frame of infrared image in the infrared image sequence.

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] 1) The present invention constructs a three-dimensional space-time tensor and makes full use of the space-time information of the infrared image sequence, which is helpful for the expression of background signals and target signals; and provides a tensor nuclear norm based on Laplace calculation, which can better characterize the low-rank characteristics of the background and is conducive to the effective separation of background and target.

[0026] 2) In order to fully explore and extract the spatiotemporal relationship between image frames in an infrared sequence, the present invention designs a hypergraph structure, constructs infrared image frames as image block tensors as nodes of the hypergraph, and introduces time and space factors into the weights of hyperedges, so as to capture more complex correlations from multiple frames at the same time; by introducing the hypergraph Laplace regularization term, in the spatiotemporal tensor decomposition model based on low-rank sparsity theory, satisfactory target detection capability, background suppression capability and comprehensive detection performance can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 A schematic diagram of constructing a three-dimensional space-time tensor in the present invention;

[0028] Figure 2 It is a structural schematic diagram of an infrared dim small target detection device based on a hypergraph in the present invention;

[0029] Figure 3 Schematic diagram for node construction of hypergraph;

[0030] Figure 4 Schematic diagram of the constructed hypergraph structure;

[0031] Figure 5 This is a sample frame image of a thermal infrared image sequence used for experimental testing;

[0032] Figure 6 The thermal infrared small target detection result obtained by the method proposed by the present invention is an example frame of the thermal infrared image sequence;

[0033] Figure 7 Figure 3 is an original image of an example frame of a thermal infrared image and its thermal infrared weak target detection result diagram after detection by the method of the present invention, MDWCM, MSLSTIPT, TWTVR, STT-TRNR, FGLR-MCP, AGPCNet and RPCANet. The green box indicates target loss, the red box indicates target area, and the orange dotted box indicates clutter interference. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical solutions and advantages of the present invention clearer, the present invention is described in detail below in conjunction with specific embodiments. Specific embodiments are described below to simplify the present invention. However, it should be recognized that the present invention is not limited to the illustrated embodiments, and various modifications of the present invention are possible without departing from the basic principles, and these equivalent forms also fall within the scope defined by the claims attached to the present application.

[0035] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] The basic steps of the infrared small target detection method based on frequency domain data spherification of the present invention in this embodiment mainly include:

[0037] Step 1: Stack the thermal infrared image sequence to be detected in sequence according to the time dimension to construct a three-dimensional space-time tensor ;

[0038] Specifically, Figure 1 As shown, for the original thermal infrared image sequence, the original thermal infrared image sequence is stacked in the time dimension direction to construct a three-dimensional space-time tensor , The size is , express The width of express Height, express Thickness.

[0039] Specifically in the embodiment, the constructed three-dimensional space-time tensor The size is .

[0040] Step 2: Using the 3D space-time tensor , establish a low-rank sparse decomposition model based on three-dimensional space-time tensor; specifically, the three-dimensional space-time tensor Modeled as a background tensor , target tensor and the noise tensor Considering the low-rank characteristics of the background tensor and the sparse characteristics of the target tensor, the spatial-temporal tensor Modeled as

[0041] (1)

[0042] in, represents the rank calculation, express norm, represents the square of the Frobenius norm, and Represents the weight coefficient.

[0043] Step 3: Design a Laplace-based tensor nuclear norm to estimate the low-rank characteristics of the background;

[0044] Specifically, the background tensor Perform fast Fourier transform to get the tensor ,calculate The numerical sum of the Laplace functions of the singular values ​​of all positive slices is defined as the Laplace-based tensor nuclear norm , the calculation formula is:

[0045] (5)

[0046] in, is the background tensor The i-th frontal slice after fast Fourier transformation The jth singular value of is the control parameter of the Laplace operator, which is a positive constant.

[0047] Specifically in the embodiment, Take it as 0.001.

[0048] Step 4: construct each frame of the thermal infrared image sequence to be detected into an image block tensor as a graph node, and use the node set to construct a hypergraph, and design the hypergraph Laplace regularization term;

[0049] Specifically, Figure 3 As shown, the image frame The image block tensor is constructed as a graph node. The construction process is as follows: using the image frame of the thermal infrared image sequence to be detected , by fixing the size The sliding window is Slide in an S-shaped path to obtain non-overlapping image patches, The numerical value and The values ​​of are the same, , represents the round-up operator and stacks them as front slices to obtain the image frame Tensor of non-overlapping image patches , as an image frame Corresponding graph nodes ; Thus, the node set corresponding to the original thermal infrared image sequence is obtained ; indicates that there is The image frame is constructed as nodes;

[0050] Using Node Sets To construct a hypergraph, first define the hyperedge set ,in, express hyperedges, and each hyperedge has nodes, represented by , , , ; For the hyperedge within fully connected nodes, any two nodes and The weight between The definition is as follows:

[0051] (6)

[0052] in, represents the square of the matrix's bi-norm, Indicates Image frames The corresponding node No. A frontal slice. Indicates Image frames The corresponding node No. A frontal slice. is the time interval factor, , is a normal number to prevent the denominator from being zero;

[0053] Therefore, the super edge Weight It is expressed as:

[0054] (7)

[0055] Thus, a hypergraph is constructed. ,like Figure 4 As shown; define the hypergraph Laplacian matrix , which contains the node degree matrix , the hyperedge degree matrix , and the incidence matrix as follows:

[0056] (1) Node degree matrix is the diagonal matrix of the degree of each node, with size , the degree of each node is the sum of the weights of all hyperedges connected to that node:

[0057] (8)

[0058] in, , , Respectively represent nodes , and degree;

[0059] (2) Hyperedge degree matrix is a size of The diagonal matrix represents the degree of each hyperedge, that is, the number of nodes contained. If the hyperedge Include nodes, then the hyperedge The degree is :

[0060] (9)

[0061] (3) Correlation Matrix It is a matrix describing the relationship between points and hyperedges, and its size is , if the node Super edge ,but , otherwise 0, for

[0062] (10)

[0063] Therefore, the hypergraph The hypergraph Laplacian matrix of Defined as

[0064] (11)

[0065] The hypergraph Laplacian matrix is ​​used to construct the hypergraph Laplacian regularization term:

[0066] (12)

[0067] in, represents the trace calculation of the matrix, Represents the background tensor The background matrix obtained after expansion; , which means that the background tensor The background matrix obtained after expansion , background matrix The height is , the width is , Represents the spread operator.

[0068] Specifically in the embodiment, Take it as 2, Take it as 5, Take , Take it as 0.001, Take it as 0.8.

[0069] Step 5: Combine the Laplace-based tensor nuclear norm and the hypergraph Laplace regularization term to establish a hypergraph-based spatiotemporal tensor decomposition model;

[0070] Specifically, the sparse reweighted tensor is introduced , Represents a positive constant to prevent the denominator from being zero; use Norm Approximation norm; the space-time tensor decomposition model based on hypergraph established by combining the tensor core norm based on Laplace and the hypergraph Laplace regularization term is

[0071] (13)

[0072] in, express norm, , and represents the weight coefficient, represents the Hadamard product;

[0073] Step 6: Use the ADMM-based optimization solution algorithm to solve the spatiotemporal tensor decomposition model constructed in step 5) to obtain the target detection result tensor;

[0074] Specifically, the alternating direction method of multipliers (ADMM) algorithm is used to solve model (13), introducing auxiliary variables and , and the equivalent model is

[0075] (14)

[0076] The corresponding augmented Lagrangian function for:

[0077] (15)

[0078] in, is the penalty coefficient, is the Lagrange multiplier;

[0079] Decompose the problem of solving equation (15) into several sub-problems, and update the variables alternately according to equations (16) to (25) until the iteration ends;

[0080] Auxiliary variables The iteration formula is:

[0081] (16)

[0082] in, represents the number of iterations, , and are all unitary matrices, represents the singular value matrix, represents the singular value contraction operator, and ,for Each singular value in ,have ;

[0083] Auxiliary variables The iteration formula is:

[0084] (17)

[0085] in, represents the identity matrix, express Hypergraph Laplacian matrix;

[0086] Background tensor The iteration formula is:

[0087] (18)

[0088] in, , , which means that the matrices and matrix Reconstructed to size Tensor and tensors ;

[0089] Target Tensor The iteration formula is:

[0090] (19)

[0091] in, is a soft threshold shrinkage operator, and , is the soft threshold;

[0092] Noise Tensor The iteration formula is:

[0093] (20)

[0094] Sparse reweighted tensor The iteration formula is:

[0095] (twenty one)

[0096] Lagrange multipliers The iteration formula is:

[0097] (twenty two)

[0098] (twenty three)

[0099] (twenty four)

[0100] Penalty coefficient The iteration formula is:

[0101] (25)

[0102] in, represents the update coefficient, express The maximum value of

[0103] During the iteration process, the iteration stop condition is: when the relative error Less than the set error The iteration terminates when ; or when the target tensor The number of non-zero elements in no longer changes, so the iteration stops;

[0104] The target tensor obtained after the iteration stops is used as the target detection result tensor;

[0105] Specifically in the embodiment, , , , , , , , , , represents the three-dimensional zero tensor, , represents a two-dimensional zero matrix.

[0106] Step 7: Convert the target detection result tensor into a target detection result sequence T as the infrared small target detection result to realize infrared small target detection.

[0107] Corresponding to the aforementioned embodiment of an infrared dim small target detection method based on a hypergraph, the present invention further provides an embodiment of an infrared dim small target detection device based on spatiotemporal background reconstruction.

[0108] Figure 2 FIG. 1 is a block diagram of an infrared small target detection device based on a hypergraph according to an exemplary embodiment. Figure 2 As shown, the device comprises:

[0109] A three-dimensional space-time tensor construction module is used to construct the infrared image sequence to be detected into a three-dimensional space-time tensor;

[0110] A low-rank sparse decomposition model building module is used to build a low-rank sparse tensor decomposition model corresponding to a three-dimensional space-time tensor;

[0111] Laplace-based tensor nuclear norm design module, used to design Laplace-based tensor nuclear norm;

[0112] A hypergraph construction module, used for constructing a sequence of image frames to be detected into a hypergraph structure;

[0113] A hypergraph Laplace regularization construction module, used to construct a hypergraph Laplace regularization term corresponding to the hypergraph structure;

[0114] A hypergraph-based spatiotemporal tensor decomposition model building module, which is used to build a spatiotemporal tensor decomposition model based on a hypergraph regularization term;

[0115] Model optimization solution module, used to solve the spatiotemporal tensor decomposition model based on hypergraph regularization term using ADMM-based optimization algorithm;

[0116] The target detection result output module is used to output the infrared weak target detection result map of each frame of infrared image in the infrared image sequence.

[0117] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0118] For the device embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The device embodiment described above is only schematic. The various modules in the device are a kind of logical function division. There may be other division methods in actual implementation. For example, multiple modules can be combined or integrated into another unit. Another point is that the connection between the modules shown or discussed can be a communication connection through some interfaces, which can be electrical or other forms. Some or all of the modules can be selected according to actual needs to achieve the purpose of the present application. Ordinary technicians in this field can understand and implement it without paying creative labor. The following takes the disclosed real thermal infrared image sequence as an example to illustrate the specific implementation method to reflect the technical effect of the present invention. The specific steps in the embodiment will not be repeated.

[0119] Example

[0120] The accompanying drawings shown in the embodiments of the present invention can make the purpose, technical solutions and advantages of the present invention more clearly described. It should be noted that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. All equivalent replacements, improvements, etc. made within the method ideas and principles provided by the present invention should be included in the protection scope of the present invention.

[0121] In this embodiment, the effectiveness of the infrared weak target detection method based on hypergraph will be verified through a public infrared image sequence. The public infrared image sequence contains 150 consecutive infrared image frames, each frame size is 256×256, the sequence contains a slowly flying drone, which forms a high contrast with the surrounding background. The data set is based on the sky and forest as the background, and contains linear highlighted roadside structures, etc. In addition, there is significant clutter and noise in the image. Figure 5 Shown are example frames of infrared image sequence. Figure 6 The target detection result image is obtained by the infrared small target detection method based on the hypergraph of the present invention for the infrared image sequence example frame. It can be intuitively observed from the detection result graph that the method can achieve the improvement of target significance and the effective suppression of background and noise components. In order to more accurately and objectively evaluate the comprehensive performance of the infrared small target detection method based on the hypergraph, the detection performance of the algorithm is evaluated from a quantitative perspective through the 3D-ROC evaluation index system, as follows:

[0122] (1) Detector effectiveness

[0123]

[0124] (2) Target detection probability

[0125]

[0126] (3) Background suppression probability

[0127]

[0128] (4) Joint target detection capability

[0129]

[0130] (5) Joint background suppression capability

[0131]

[0132] (6) Joint target detection and background suppression capabilities

[0133]

[0134] (7) Target-background signal ratio

[0135]

[0136] (8) Comprehensive target detection capability

[0137]

[0138] In order to more objectively verify the effectiveness of the method of the present invention, seven thermal infrared dim target detection methods are selected for comparison with the method of the present invention. The comparison methods include MDWCM, MSLSTIPT, TWTVR, STT-TRNR, FGLR-MCP, AGPCNet and RPCANet. Table 1 shows the indicators of the 3D-ROC evaluation system for infrared dim target detection results using the comparison methods and the method of the present invention. The bold values ​​and underlined values ​​represent the corresponding optimal performance and suboptimal performance, respectively.

[0139] MDWCM comes from Lu R, Yang X, Li W, et al. Robust infrared small target detection via multidirectional derivative-based weighted contrast measure[J]. IEEE Geoscience and Remote Sensing Letters, 2020, 19: 1-5.

[0140] MSLSTIPT comes from Sun Y, Yang J, An W. Infrared dim and small target detection via multiple subspace learning and spatial-temporal patch-tensormodel[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 59(5):3737-3752.

[0141] TWTVR comes from Zhao E, Dong L, Li C, et al. Infrared maritime target detection based on temporal weight and total variation regularization under strong wave interferences[J]. IEEE Transactions on Geoscience and RemoteSensing, 2024.

[0142] STT-TRNR comes from Yi, Haiyang, et al. "Spatial-Temporal Tensor Ring NormRegularization for Infrared Small Target Detection." IEEE Geoscience andRemote Sensing Letters 20 (2023): 1-5.

[0143] FGLR-MCP comes from Liu T, Liu Y, Yang J, et al. Graph Laplacianregularization for fast infrared small target detection[J]. PatternRecognition, 2025, 158: 111077.

[0144] AGPCNet comes from Zhang T, Cao S, Pu T, et al. AGPCNet: Attention-guidedpyramid context networks for infrared small target detection. arXiv 2021[J].arXiv preprint arXiv:2111.03580.

[0145] RPCANet comes from Wu F, Zhang T, Li L, et al. RPCANet: Deep Unfolding RPCABased Infrared Small Target Detection[C] / / Proceedings of the IEEE / CVF WinterConference on Applications of Computer Vision. 2024: 4809-4818.

[0146] Figure 7 The original image of the infrared image example frame and the thermal infrared weak target detection result diagram after the method of the present invention, MDWCM, MSLSTIPT, TWTVR, STT-TRNR, FGLR-MCP, AGPCNet and RPCANet detection. Figure 7It can be seen from the target detection result images that the background suppression capabilities of STT-TRNR, TWTVR and MSLSTIPT are extremely weak, MDWCM, FGLR-MCP and AGPCNet have problems of target shrinkage or even target loss, and there are more clutter residues in the detection result images of RPCANet; in contrast, the infrared weak target detection method based on hypergraph proposed in the present invention can well suppress background, noise and other components, and enhance the significance of weak targets. In addition, according to the indicator results based on the 3D-ROC evaluation system shown in Table 1, the target detection ability, background suppression ability and comprehensive ability of the method proposed in the present invention can basically achieve optimal performance and suboptimal performance; in contrast, other methods have insufficient background suppression ability or insufficient target detection ability, resulting in an overall performance far worse than the method proposed in the present invention. Combining the above qualitative and quantitative analysis, the infrared weak target detection method based on hypergraph proposed in the present invention has superior target detection ability, background suppression ability and comprehensive effectiveness.

[0147] Table 1 Quantitative indicators of detection results of thermal infrared image example sequences using MDWCM, MSLSTIPT, TWTVR, STT-TRNR, FGLR-MCP, AGPCNet, RPCANet and the proposed method

[0148]

[0149] The accompanying drawings shown in the embodiments of the present invention can make the purpose, technical solutions and advantages of the present invention more clearly described. It should be noted that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. All equivalent replacements, improvements, etc. made within the method ideas and principles provided by the present invention should be included in the protection scope of the present invention.

Claims

1. A method for detecting infrared small targets based on hypergraph, characterized in that: The steps include: Step 1): Stack the thermal infrared image sequence to be detected in sequence according to the time dimension to construct a three-dimensional space-time tensor ; Step 2): Using the 3D space-time tensor , establish a low-rank sparse decomposition model based on three-dimensional space-time tensor; Step 3): Design a Laplace-based tensor nuclear norm to estimate the low-rank characteristics of the background; Step 4): Each frame of the thermal infrared image sequence to be detected is constructed as an image block tensor as a graph node, and a hypergraph is constructed using the node set to design the hypergraph Laplace regularization term; Step 5): Combine the Laplace-based tensor nuclear norm and the hypergraph Laplace regularization term to establish a hypergraph-based spatiotemporal tensor decomposition model; Step 6): Using the ADMM-based optimization solution algorithm, solve the spatiotemporal tensor decomposition model constructed in step 5) to obtain the target detection result tensor; Step 7): Convert the target detection result tensor into a target detection result sequence T as the infrared small target detection result to realize infrared small target detection.

2. The method for detecting infrared small targets based on a hypergraph according to claim 1, characterized in that: The step 1) comprises: stacking the original thermal infrared image sequence in the time dimension direction to construct a three-dimensional space-time tensor , The size is , express The width of express Height, express Thickness.

3. The infrared small target detection method based on hypergraph according to claim 1 is characterized in that: The step 2) is specifically as follows: The three-dimensional space-time tensor Modeled as a background tensor , target tensor and the noise tensor Considering the low-rank characteristics of the background tensor and the sparse characteristics of the target tensor, the spatiotemporal tensor Modeled as: （1） in, represents the rank calculation, express norm, represents the square of the Frobenius norm, and Represents the weight coefficient.

4. The infrared small target detection method based on hypergraph according to claim 1 is characterized in that: The step 3) is specifically as follows: The background tensor Perform fast Fourier transform to get the tensor ,calculate The numerical sum of the Laplace functions of the singular values ​​of all positive slices is defined as the Laplace-based tensor nuclear norm , the calculation formula is: （5） in, is the background tensor The i-th frontal slice after fast Fourier transformation The jth singular value of is the control parameter of the Laplace operator.

5. The infrared small target detection method based on hypergraph according to claim 1 is characterized in that: The step 4) is specifically as follows: The image frames of the thermal infrared image sequence to be detected The image block tensor is constructed as a graph node. The construction process is as follows: using the image frame of the thermal infrared image sequence to be detected , by fixing the size The sliding window is Slide in an S-shaped path to obtain non-overlapping image patches, The numerical value and The values ​​of are the same, , represents the round-up operator and stacks them as front slices to obtain the image frame Tensor of non-overlapping image patches , as the image frame Corresponding graph nodes ; Thus, the node set corresponding to the original thermal infrared image sequence is obtained ; Using Node Sets To construct a hypergraph, first define the hyperedge set ,in, express hyperedges, and each hyperedge has nodes, represented by , , , For the hyperedge within fully connected nodes, any two nodes and The weight between The definition is as follows: （6） in, represents the square of the matrix's bi-norm, Indicates Image frames The corresponding node No. A frontal slice. Indicates Image frames The corresponding node No. A frontal slice. is the time interval factor, , is a normal number to prevent the denominator from being zero; Therefore, the super edge Weight It is expressed as: （7） Thus, a hypergraph is constructed. , define the hypergraph Laplacian matrix , which contains the node degree matrix , hyperedge degree matrix , and the correlation matrix ,as follows: Node degree matrix is the diagonal matrix of the degree of each node, with size , the degree of each node is the sum of the weights of all hyperedges connected to that node: （8） in, , , Respectively represent nodes , and degree; Hyperedge degree matrix is a size of The diagonal matrix of （9） Incidence Matrix It is a matrix describing the relationship between points and hyperedges, and its size is , for （10） Hypergraph The hypergraph Laplacian matrix of Defined as （11） The hypergraph Laplacian matrix is ​​used to construct the hypergraph Laplacian regularization term: （12） in, represents the trace calculation of the matrix, , which means that the background tensor The background matrix obtained after expansion , background matrix The height is , the width is , Represents the spread operator.

6. The method for detecting infrared small targets based on a hypergraph according to claim 5, characterized in that: The step 5) is specifically as follows: Introducing sparse reweighted tensors , Represents a positive constant to prevent the denominator from being zero; use Norm Approximation norm; the hypergraph-based spatiotemporal tensor decomposition model established by combining the Laplace-based tensor core norm and the hypergraph Laplace regularization term is: （13） in, express norm, , and represents the weight coefficient, represents the Hadamard product.

7. The method for detecting infrared small targets based on a hypergraph according to claim 6, characterized in that: The step 6) is specifically as follows: The ADMM algorithm is used to solve the model (13), introducing auxiliary variables and , the equivalent model is: （14） The augmented Lagrangian function corresponding to the equivalent model for: （15） in, is the penalty coefficient, is the Lagrange multiplier; Decompose the problem of solving equation (15) into several sub-problems, and update the variables alternately according to equations (16) to (25) until the iteration ends; Auxiliary variables The iteration formula is: （16） in, represents the number of iterations, , and are all unitary matrices, represents the singular value matrix, represents the singular value contraction operator, and ,for Each singular value in ,have ; Auxiliary variables The iteration formula is: （17） in, represents the identity matrix, express Hypergraph Laplacian matrix; Background tensor The iteration formula is: （18） in, , , which means that the matrices and matrix Reconstructed to size Tensor and tensors ; Target Tensor The iteration formula is: （19） in, is a soft threshold shrinkage operator, and , is the soft threshold; Noise Tensor The iteration formula is: （20） Sparse reweighted tensor The iteration formula is: （21） Lagrange multipliers The iteration formula is: （22） （23） （24） Penalty coefficient The iteration formula is: （25） in, represents the update coefficient, express The maximum value of The iteration stop condition is: when the relative error Less than the set error When, or when the target tensor When the number of non-zero elements in no longer changes, stop iterating; After the iteration stops, the target tensor is obtained .

8. The infrared small target detection method based on hypergraph according to claim 1, characterized in that: The step 7) is specifically as follows: The target tensor that will be obtained in the end Reconstructed into infrared target detection result map, the small target detection result map sequence T is obtained to realize thermal infrared small target detection.

9. A hypergraph-based infrared small target detection device for implementing the method according to any one of claims 1 to 8, characterized in that: include: A three-dimensional space-time tensor construction module is used to construct the infrared image sequence to be detected into a three-dimensional space-time tensor; A low-rank sparse decomposition model building module is used to build a low-rank sparse tensor decomposition model corresponding to a three-dimensional space-time tensor; Laplace-based tensor nuclear norm design module, used to design Laplace-based tensor nuclear norm; A hypergraph construction module, used for constructing a sequence of image frames to be detected into a hypergraph structure; A hypergraph Laplace regularization construction module, used to construct a hypergraph Laplace regularization term corresponding to the hypergraph structure; A hypergraph-based spatiotemporal tensor decomposition model building module, which is used to build a spatiotemporal tensor decomposition model based on a hypergraph regularization term; Model optimization solution module, used to solve the spatiotemporal tensor decomposition model based on hypergraph regularization term using ADMM-based optimization algorithm; The target detection result output module is used to output the infrared weak target detection result map of each frame of infrared image in the infrared image sequence.

Images