Method and device for detecting ultra-low contrast targets with exponential fluctuation of light index

Abstract

The application provides a method and device for detecting an ultra-low contrast target with exponential fluctuation of light, which can be applied to the fields of computational optical imaging, infrared detection, target identification and tracking. The method comprises the following steps: constructing an energy-light index, which is used to quantify the energy-photon distribution difference in the mid-wave infrared and long-wave infrared bands; based on the energy-light index, performing spatial domain screening on a dual-band infrared image to generate a candidate target region; based on the time sequence data of the energy-light index of the candidate target region, calculating the second-order photon coherent fluctuation of the candidate target region, which represents the time fluctuation of the photon number correlation; based on the second-order photon coherent fluctuation, calculating a fluctuation enhancement factor; and based on the fluctuation enhancement factor, performing quantum statistical identification to output a target detection result. This method can accurately detect targets in the case of extremely small temperature difference between the target and the background, subtle emissivity difference and spectral camouflage.

Description

Technical Field

[0001] This application relates to the fields of computational optical imaging, infrared detection, target recognition and tracking, and more specifically to a method and apparatus for detecting ultra-low contrast targets with energy index fluctuations. Background Technology

[0002] With the integration of infrared detection technology with target recognition and tracking technologies, infrared detection systems are developing towards higher resolution, higher real-time performance, and greater intelligence, leading to increasingly higher demands on infrared detection technology. Current infrared detection technology primarily relies on the contrast in radiation intensity between the target and the background, as well as the arrival time and statistical distribution of photons. However, under extremely low contrast conditions where the temperature difference between the target and the background is minimal and the emissivity difference is subtle, existing intensity detection methods and photon counting detection methods face fundamental limitations, making effective detection difficult. Summary of the Invention

[0003] In view of the above problems, embodiments of this application provide a method and apparatus for detecting targets with ultra-low contrast due to energy index fluctuations, which can solve the problem that existing detection technologies have poor detection performance under extreme low contrast conditions such as extremely small temperature difference between the target and the background, subtle difference in emissivity, and spectral camouflage.

[0004] According to the first aspect of this application, a method for detecting ultra-low contrast targets with energy-light index fluctuations is provided, comprising: constructing an energy-light index, which is used to quantify the difference in energy-photon distribution between the mid-wave infrared and long-wave infrared bands; performing spatial domain screening on dual-band infrared images based on the energy-light index to generate candidate target regions, wherein the dual-band infrared images include mid-wave infrared images and long-wave infrared images; calculating second-order photon coherent fluctuations in the candidate target regions based on the time series data of the energy-light index of the candidate target regions, wherein the second-order photon coherent fluctuations are used to characterize the temporal fluctuations of photon number correlation; calculating a fluctuation enhancement factor based on the second-order photon coherent fluctuations, wherein the fluctuation enhancement factor is used to characterize the difference in quantum statistical properties between the target and the background; performing quantum statistical identification based on the fluctuation enhancement factor, and outputting the target detection result.

[0005] According to an embodiment of this application, constructing the energy-light index includes: determining the detection wavelength based on Wien's displacement law, wherein the detection wavelength includes an energy detection wavelength and a photon number detection wavelength; determining the optimal detection wavelength based on Planck's radiation law and the detection wavelength, wherein the optimal detection wavelength includes an energy-optimal detection wavelength and a photon number-optimal detection wavelength; determining the radiative ratio corresponding to the optimal detection wavelength based on the optimal detection wavelength, wherein the radiative ratio includes an energy radiative ratio and a photon number radiative ratio; and constructing the normalized differential energy-light index based on the radiative ratio corresponding to the optimal detection wavelength.

[0006] According to an embodiment of this application, the optimal detection wavelength is determined by the temperature sensitivity maximization criterion and displacement constraints.

[0007] According to embodiments of this application, spatial domain filtering of a dual-band infrared image based on the energy index to generate candidate target regions includes: calculating the energy index of each pixel in the dual-band infrared image to generate an energy index feature map; calculating an adaptive threshold based on the statistical distribution of the energy index in the energy index feature map; segmenting the energy index feature map according to the adaptive threshold and marking candidate pixels; performing connected component analysis on the marked candidate pixels to aggregate spatially adjacent candidate pixels into independent candidate regions; and performing morphological filtering on the candidate regions to generate candidate target regions.

[0008] According to an embodiment of this application, the adaptive threshold is calculated based on the statistical distribution of the energy index values ​​in the energy index feature map, including: calculating the mean and standard deviation of the energy index in the energy index feature map; and generating the adaptive threshold by adding a multiple of the mean and standard deviation based on the mean and standard deviation.

[0009] According to an embodiment of this application, calculating the second-order photonic coherent fluctuations of a candidate target region based on the energy index time series data of the candidate target region includes: collecting energy index time series data at a preset frame rate within the candidate target region; and calculating the second-order photonic coherent fluctuations of the candidate target region based on the energy index time series data using an energy index fluctuation variance model, wherein the energy index fluctuation variance model is used to characterize the correlation between the energy index fluctuation variance and the second-order coherence function.

[0010] According to an embodiment of this application, collecting energy index time series data at a preset frame rate within a candidate target area includes: acquiring the candidate target area and a preset collection time period; and collecting energy index time series data of the candidate target area within the preset collection time period.

[0011] According to an embodiment of this application, quantum statistical identification based on fluctuation enhancement factor and outputting target detection results includes: calculating the fluctuation enhancement factor value of the candidate target region; comparing the fluctuation enhancement factor value with a preset discrimination threshold and outputting the comparison result, wherein the preset discrimination threshold is a critical value used to distinguish between real targets and false alarms; and outputting the target detection result based on the comparison result.

[0012] According to an embodiment of this application, the preset discrimination threshold is determined through receiver operation characteristic curve analysis and optimization.

[0013] The second aspect of this application provides an ultra-low contrast target detection device with energy index fluctuations, comprising: an energy index construction module for constructing an energy index, which quantifies the energy-photon distribution difference between the mid-wave infrared and long-wave infrared bands; a candidate target region determination module for spatial domain screening of dual-band infrared images based on the energy index to generate candidate target regions, wherein the dual-band infrared images include a mid-wave infrared image and a long-wave infrared image; a second-order photon coherent fluctuation calculation module for calculating second-order photon coherent fluctuations of the candidate target regions based on the energy index time series data of the candidate target regions, wherein the second-order photon coherent fluctuations characterize the temporal fluctuations of photon number correlation; a fluctuation enhancement factor calculation module for calculating a fluctuation enhancement factor based on the second-order photon coherent fluctuations, wherein the fluctuation enhancement factor characterizes the difference in quantum statistical properties between the target and the background; and a target detection result output module for performing quantum statistical identification based on the fluctuation enhancement factor and outputting the target detection result.

[0014] A third aspect of this application provides an electronic device comprising: one or more processors; and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the method described above.

[0015] A fourth aspect of this application also provides a computer-readable storage medium having a computer program or instructions stored thereon, which, when executed by a processor, implement the steps of the above-described method.

[0016] The fifth aspect of this application also provides a computer program product, including a computer program or instructions that, when executed by a processor, implement the steps of the above-described method.

[0017] The above one or more embodiments have the following beneficial effects: Compared with the prior art, the energy index fluctuation ultra-low contrast target detection method provided by the embodiments of this application quantifies the macroscopic spectral characteristics by constructing the energy index and further explores its quantum statistical fluctuation characteristics. It establishes a computable physical correlation and processing framework between the spectral domain and the quantum statistical domain, so that the detection dimension is expanded from the traditional single intensity or photon statistics to a dual dimension that integrates spectral energy distribution and quantum fluctuation information. This can solve the fundamental problem of target detection failure under extreme low contrast and spectral camouflage conditions. Attached Figure Description

[0018] The above-mentioned contents, as well as other objects, features, and advantages of this application, will become clearer from the following description of embodiments with reference to the accompanying drawings, in which:

[0019] Figure 1The illustration shows an application scenario of the energy index fluctuation ultra-low contrast target detection method and apparatus according to an embodiment of this application;

[0020] Figure 2 A flowchart illustrating an embodiment of the energy index fluctuation ultra-low contrast target detection method according to this application is shown.

[0021] Figure 3 A flowchart illustrating the generation of candidate target regions according to an embodiment of this application is shown schematically;

[0022] Figure 4 The illustration shows a schematic diagram of the construction of an energy index feature map according to an embodiment of this application;

[0023] Figure 5 A schematic diagram illustrating a spatial domain fast filtering process according to an embodiment of this application is provided.

[0024] Figure 6 A schematic diagram illustrating the principle of quantum statistical identification according to an embodiment of this application is shown.

[0025] Figure 7 A flowchart illustrating a two-stage collaborative processing architecture according to an embodiment of this application is shown.

[0026] Figure 8A The illustrations show detection results in different scenarios in embodiments according to this application;

[0027] Figure 8B The illustration schematically shows another detection result diagram under different scenarios in embodiments according to the present application;

[0028] Figure 9A The illustration shows a comparison of detection performance in different scenarios according to embodiments of this application;

[0029] Figure 9B This illustration schematically shows another comparison chart of detection performance in different scenarios according to embodiments of this application;

[0030] Figure 10 A schematic diagram illustrating the structure of an ultra-low contrast target detection device with energy index fluctuations according to an embodiment of this application is shown; and

[0031] Figure 11 A block diagram schematically illustrates an electronic device suitable for implementing an energy index fluctuation ultra-low contrast target detection method according to an embodiment of this application. Detailed Implementation

[0032] The embodiments of this application will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of this application. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of this application for ease of explanation. However, it will be apparent that one or more embodiments may be practiced without these specific details. Furthermore, descriptions of well-known structures and technologies are omitted in the following description to avoid unnecessarily obscuring the concepts of this application.

[0033] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of this application. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0034] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0035] When using expressions such as "at least one of A, B and C", they should generally be interpreted in accordance with the meaning that is commonly understood by those skilled in the art (e.g., "a system having at least one of A, B and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B and C, etc.).

[0036] Current infrared detection technology mainly relies on the contrast in radiation intensity between the target and the background, as well as the arrival time and statistical distribution of photons. Under extremely low contrast conditions where the temperature difference between the target and the background is very small and the emissivity difference is subtle, existing intensity detection methods and photon counting detection methods face fundamental limitations.

[0037] The main technical bottlenecks of existing solutions include:

[0038] (1) The detection dimension is singular. Existing methods either rely only on radiation intensity information and cannot make full use of the quantum statistical characteristics of photon flow; or rely only on photon number and its timing information and cannot effectively utilize the spectral distribution characteristics of radiation energy. Neither of these methods can uniformly characterize the intrinsic physical relationship between energy and photons. Traditional intensity methods lack quantum statistical dimension; photon counting methods lack spectral resolution dimension.

[0039] (2) The multispectral fusion method is still mainly based on intensity feature comparison and has failed to construct a unified physical observation to characterize the differences in energy and photon distribution. Low temperature difference between the target and the background or small change in emissivity will lead to detection failure.

[0040] (3) When faced with a camouflaged target with carefully designed spectral emissivity characteristics, the traditional intensity comparison method is very likely to fail because the apparent radiation characteristics are imitated; while the photon counting method can obtain statistical fluctuation information, it is also difficult to distinguish the target camouflaged with spectral characteristics because it discards the key spectral context.

[0041] This application provides a method for detecting ultra-low contrast targets using energy-photon index fluctuations. The method includes: constructing an energy-photon index, which is used to quantify the difference in energy-photon distribution between the mid-wave infrared and long-wave infrared bands; performing spatial domain screening on dual-band infrared images based on the energy-photon index to generate candidate target regions, wherein the dual-band infrared images include mid-wave infrared images and long-wave infrared images; calculating second-order photon coherent fluctuations in the candidate target regions based on the time series data of the energy-photon index, wherein the second-order photon coherent fluctuations are used to characterize the temporal fluctuations of photon number correlation; calculating a fluctuation enhancement factor based on the second-order photon coherent fluctuations, wherein the fluctuation enhancement factor is used to characterize the difference in quantum statistical properties between the target and the background; and performing quantum statistical identification based on the fluctuation enhancement factor to output the target detection result. In this way, by constructing an energy index to quantify macroscopic spectral characteristics and further exploring their quantum statistical fluctuation characteristics, a computable physical correlation and processing framework is established between the spectral domain and the quantum statistical domain. This expands the detection dimension from the traditional single intensity or photon statistics to a dual dimension that integrates spectral energy distribution and quantum fluctuation information, thereby solving the fundamental problem of target detection failure under extreme low contrast and spectral camouflage conditions.

[0042] Figure 1 The illustration shows an application scenario of the energy index fluctuation ultra-low contrast target detection method and apparatus according to an embodiment of this application.

[0043] like Figure 1 As shown, application scenario 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 serves as a medium for providing a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0044] Users can use the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 via the network 104 to receive or send messages, etc. Various communication client applications can be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social media platform software, etc. (for example only).

[0045] The first terminal device 101, the second terminal device 102, and the third terminal device 103 can be various electronic devices with displays and support web browsing, including but not limited to smartphones, tablets, laptops, and desktop computers.

[0046] Server 105 can be a server that provides various services, such as a backend management server that supports websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103 (this is just an example). The backend management server can analyze and process data such as received user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal devices.

[0047] It should be noted that the energy index fluctuation ultra-low contrast target detection method provided in the embodiments of this application can generally be executed by server 105. Correspondingly, the energy index fluctuation ultra-low contrast target detection device provided in the embodiments of this application can generally be located in server 105. The energy index fluctuation ultra-low contrast target detection method provided in the embodiments of this application can also be executed by a server or server cluster that is different from server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or server 105. Correspondingly, the energy index fluctuation ultra-low contrast target detection device provided in the embodiments of this application can also be located in a server or server cluster that is different from server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or server 105.

[0048] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0049] The following will be based on Figure 1 The described scene, through Figures 2-9B A method for detecting ultra-low contrast targets with energy index fluctuations according to embodiments of this application will be described in detail.

[0050] Figure 2A flowchart illustrating an embodiment of the energy index fluctuation ultra-low contrast target detection method according to this application is shown.

[0051] like Figure 2 As shown, the ultra-low contrast target detection method 200 with energy index fluctuation in this embodiment includes operations S210 to S250.

[0052] In operation S210, the energy-photon index is constructed. The energy-photon index is used to quantify the difference in energy-photon distribution between the mid-wave infrared and long-wave infrared bands.

[0053] In the embodiments of this application, constructing the energy index includes steps 1 to 4.

[0054] Step 1: Based on Wien's displacement law, determine the detection wavelength, which includes the energy detection wavelength and the photon number detection wavelength.

[0055] Wien's displacement law states that the peak wavelength of blackbody radiation is inversely proportional to its absolute temperature T. The formula for Wien's displacement law can be expressed as:

[0056] ;

[0057] in, Indicates the peak wavelength. denoted by absolute temperature, and b represents Wien's displacement constant.

[0058] In the embodiments of this application, the detection wavelength can be determined based on Wien's displacement law. Detection wavelength Including energy detection wavelength and photon number detection wavelength , and The calculation formula can be expressed as:

[0059] ;

[0060] in, , .

[0061] Step 2: Based on Planck's radiation law and the detection wavelength, determine the optimal detection wavelength, which includes the optimal detection wavelength for energy and the optimal detection wavelength for photon number.

[0062] Planck's law states the relationship between the emissivity and frequency of electromagnetic radiation emitted from a blackbody at any temperature.

[0063] In the embodiments of this application, the radiation ratio is calculated based on Planck's law, by comparing the radiation amount under current conditions with the radiation amount under fixed reference conditions, thereby obtaining a dimensionless radiation ratio. The radiation ratio includes energy radiation ratio and photon number radiation ratio.

[0064] The formula for calculating the energy radiation ratio is expressed as:

[0065] ;

[0066] ;

[0067] in, Indicates spectral emissivity, Indicates the energy detection wavelength. Denotes Planck's constant. Represents the speed of light. This represents an exponential function with the natural constant e (approximately 2.71828) as its base. Represents Boltzmann's constant. Represents absolute temperature. Indicates the energy-to-radiation ratio. Indicates the reference wavelength. Indicates the reference temperature.

[0068] The formula for calculating the photon number-radiative ratio is expressed as:

[0069] ;

[0070] ;

[0071] in, Expressed as spectral photon flux. Indicates the wavelength for photon number detection. This represents the photon number radiative ratio.

[0072] The optimal detection wavelength is determined by the temperature sensitivity maximization criterion and displacement constraints. The temperature sensitivity maximization criterion under displacement constraints aims to achieve the highest response sensitivity of the optical signal to temperature changes by optimizing the sensor's materials and structure.

[0073] In the embodiments of this application, the optimal detection wavelength can be determined by using the radiation ratio and detection wavelength constructed based on Planck's law. The optimal detection wavelength includes the energy-optimal detection wavelength and the photon-number-optimal detection wavelength, and the corresponding calculation formula can be expressed as follows:

[0074] ;

[0075] in, This indicates the optimal detection wavelength for energy. This represents the energy-optimal detection wavelength under the condition of absolute temperature T. This indicates the optimal detection wavelength for the number of photons. This represents the optimal detection wavelength for the number of photons under absolute temperature T. For the available infrared detection wavelength range, This indicates that the variable value that maximizes the objective function value is returned.

[0076] In the embodiments of this application, medium-wavelength infrared (MWIR) and long-wavelength infrared (LWIR) images can be acquired simultaneously, wherein the band range of the medium-wavelength infrared image is 3. ~5 The spectral range of long-wave infrared images is 8 ~12 The energy radiance can be calculated using the acquired mid-wave infrared images. The photon number-radiative ratio was calculated using the acquired long-wave infrared images. In utilizing calculate hour, The band range is 3 -5 In utilizing calculate hour, The band range is 8 ~12 .

[0077] Step 3: Based on the optimal detection wavelength, calculate the emissivity corresponding to the optimal detection wavelength. (This step involves assigning the optimal energy detection wavelength to the wavelength.) Optimal detection wavelength for photon number Substituting these values ​​into the formulas for calculating the energy radiance ratio and photon number radiance ratio, respectively, we can obtain the radiance ratio based on the optimal detection wavelength:

[0078] ;

[0079] .

[0080] Step 4: Based on the radiance corresponding to the optimal detection wavelength, construct the normalized differential energy index. The normalized differential energy index can be simply referred to as the energy index. The calculation formula can be expressed as:

[0081] ;

[0082] in, Indicates the emissivity in the mid-infrared band. This indicates the emissivity in the long-wave infrared band.

[0083] It should be noted that, and Both parameters can be set to the same value. Once they are set to the same value, the effect is the same as if they were not set at all. and When the values ​​are the same, the light index The calculation formula can be expressed as:

[0084] .

[0085] In operation S220, based on the energy index, spatial domain filtering is performed on the dual-band infrared image to generate candidate target regions, i.e., regions of interest (ROIs). The dual-band infrared image includes a mid-wave infrared image and a long-wave infrared image.

[0086] In the embodiments of this application, the spatial domain fast filtering employs adaptive threshold segmentation and morphological processing, which will be described below in conjunction with the appendix. Figure 3 The operation S220 is described in detail.

[0087] In operation S230, based on the time series data of the energy index of the candidate target region, the second-order photon coherent fluctuations of the candidate target region are calculated. These second-order photon coherent fluctuations characterize the temporal fluctuations of the photon number correlation.

[0088] In physics, fluctuations refer to random deviations of a physical quantity around its average value, typically manifested as statistical fluctuations of the physical quantity in different measurements or different system samples.

[0089] In the embodiments of this application, the calculation of second-order photonic coherent fluctuations is based on Bose-Einstein statistics. Operation S230 specifically includes: collecting energy index time-series data at a preset frame rate within the candidate target region; and calculating the second-order photonic coherent fluctuations of the candidate target region based on the energy index time-series data using an energy index fluctuation variance model, wherein the energy index fluctuation variance model is used to characterize the correlation between the energy index fluctuation variance and the second-order coherence function.

[0090] It should be noted that the preset frame rate can be set to a high frame rate, that is, the data acquisition frame rate is not less than 100Hz and the duration is not less than 1 second. Using a high frame rate to acquire data can obtain stable statistical characteristics of energy index fluctuations.

[0091] According to an embodiment of this application, collecting energy index time series data at a preset frame rate within a candidate target area specifically includes: acquiring the candidate target area and a preset collection time period; and collecting energy index time series data of the candidate target area within the preset collection time period.

[0092] For example, a time period for data acquisition can be preset. When there are multiple candidate target areas, data can be acquired for each candidate target area within that time period. For each candidate target area, N frames of time-series MWIR and LWIR images can be acquired using a high frame rate mode. For the N frames of MWIR and LWIR images, the energy index can be calculated pixel by pixel to obtain the energy index time-series data.

[0093] In the embodiments of this application, the energy index fluctuation variance model is used to characterize the correlation between the energy index fluctuation variance and the second-order coherence function. The calculation formula of the energy index fluctuation variance model can be expressed as:

[0094] ;

[0095] in, This indicates the variance of the energy index fluctuation. This represents the average value calculation. Indicates the number of photons. Represents a zero-delay second-order coherent function. This represents the spectral correlation factor between the mid-wave infrared band and the long-wave infrared band. Furthermore, The calculation formula can be expressed as:

[0096] .

[0097] Second-order coherence function It is a normalized metric describing the correlation of intensity fluctuations in a light field (or a general boson field). It measures the time... Under the condition of detecting a photon, in time The ratio of the conditional probability of detecting another photon to the random background probability. This represents time delay. A zero-delay second-order coherence function refers to the time delay... The function form corresponding to a value of 0.

[0098] The zero-delay second-order coherence function can be expressed by the formula:

[0099] ;

[0100] in, This represents the average value calculation. This indicates the number of photons.

[0101] In the embodiments of this application, based on the energy index Calculate the variance of the energy index fluctuation. Then, the value of the zero-delay second-order coherence function can be estimated based on the energy-light index fluctuation variance model. The estimated value of the zero-delay second-order coherence function can also be called the second-order photon coherence fluctuation, which can be expressed by the formula:

[0102] ;

[0103] in, The light index represents the energy of the time series image at frame t. express The fluctuation variance.

[0104] In operation S240, the fluctuation enhancement factor is calculated based on second-order photonic coherent fluctuations. This fluctuation enhancement factor characterizes the difference in quantum statistical properties between the target and the background.

[0105] In the embodiments of this application, the fluctuation enhancement factor can be calculated based on second-order photonic coherent fluctuations. The formula for calculating the fluctuation enhancement factor can be expressed as:

[0106] ;

[0107] in, Indicates fluctuation enhancement factor, This represents the variance of the energy index fluctuation in the candidate target region. This represents the variance of the energy index fluctuations in the background region. This represents the zero-delay second-order coherence function of the candidate target region. The zero-delay second-order coherence function represents the background region.

[0108] When operating the S250, quantum statistical identification is performed based on the fluctuation enhancement factor, and the target detection results are output.

[0109] In the embodiments of this application, quantum statistical identification is achieved through a fluctuation enhancement factor. The fluctuation enhancement factor value of the candidate target region can be calculated; the fluctuation enhancement factor value is compared with a preset discrimination threshold, and the comparison result is output, where the preset discrimination threshold is a critical value used to distinguish between real targets and false alarms; based on the comparison result, the target detection result is output. The comparison process can be expressed by the following formula:

[0110] ;

[0111] in, This represents the discrimination threshold. A false alarm occurs when the system misjudges noise as a signal, thus reporting "target detected," even though the target does not actually exist in the environment. In the embodiments of this application, if the fluctuation enhancement factor D is greater than the discrimination threshold, If it is, then it is considered a real target; otherwise, it is considered a false alarm.

[0112] In the embodiments of this application, the final detection result is output through a two-stage collaborative processing architecture, which includes rapid spatial domain screening based on the energy-light index and precise temporal identification based on its quantum fluctuation characteristics. Specifically, the first stage uses the energy-light index for rapid, coarse screening in the spatial domain; the second stage uses the quantum fluctuation characteristics of the energy-light index for precise, fine identification in the temporal domain. These two stages are executed sequentially, collaboratively completing the entire process from candidate region generation to target confirmation.

[0113] In the embodiments of this application, the final detection result is output through a two-stage collaborative processing architecture, specifically including: fusing the candidate regions selected by spatial domain screening with the confirmation results of quantum statistical identification to establish a target information mapping table, wherein the target mapping table is used to record the mapping relationship between candidate target regions and judgment results, and the judgment results include true targets or false alarms; and outputting the final detection result based on the target information mapping table, including the target spatial location, outline, category identifier and confidence level, to complete the entire detection process.

[0114] In the embodiments of this application, the discrimination threshold is... It is not a fixed value, but rather can be optimized and determined through offline Receiver Operating Characteristic Curve (ROC) analysis to control the false alarm rate while ensuring a high detection probability. The specific method involves collecting calibration data containing a large amount of known real targets and typical background clutter, and calculating its fluctuation enhancement factor. The distribution; with a series of candidate discrimination thresholds Plot an ROC curve with the x-axis value, detection probability as the y-axis, and false alarm rate as the auxiliary y-axis. Select the appropriate value from the ROC curve based on the system's false alarm rate requirements. As an operating point. In the embodiments of this application, a discrimination threshold can be set according to the system's comprehensive requirements for low false alarm rate and high detection probability. .

[0115] Figure 6 A schematic diagram illustrating the principle of quantum statistical identification according to an embodiment of this application is shown.

[0116] like Figure 6 As shown, first, candidate target areas (ROIs) are determined. Then, high frame rate time-series acquisition is performed on each ROI, which can acquire N frames (e.g., 100 frames) of light index time-series images. ,in, Indicates the first Time series image of frame energy index. Indicates the first Time series image of frame energy index. Indicates the first A time-series image of the energy index. Subsequently, quantum feature extraction is performed on the energy index time-series image, which may include steps 1 to 3:

[0117] Step 1: Background parameter extraction. Specifically, obtain the variance of the energy index fluctuation in the background region corresponding to the candidate target region. and zero-delay second-order coherence function The value of .

[0118] Step 2: Calculate the variance of the energy index. Specifically, the variance of energy index fluctuations can be calculated based on the acquired time-series images of the energy index of the candidate target region. This can be expressed as a formula: ,in, denoted as the light index of the time series image at frame t.

[0119] Step 3: Estimate the second-order coherence function. Specifically, this can be based on the variance of energy index fluctuations. The value of the zero-delay second-order coherence function is estimated by the formula:

[0120] .

[0121] After quantum feature extraction, the fluctuation enhancement factor can be calculated. Specifically, it can be calculated based on the energy index fluctuation variance and the zero-delay second-order coherence function values ​​of the candidate target region and its background region. Then, target classification decisions can be made based on the fluctuation enhancement factor value. Specifically, the fluctuation enhancement factor value... With discrimination threshold Comparative analysis, if If the result is true, it is considered a real target; otherwise, it is considered a false alarm. The final output based on the judgment result is either a real target or a false alarm.

[0122] In the embodiments of this application, firstly, by constructing an energy-photon distribution index, the energy-photon distribution difference of Planck's law can be transformed into a measurable detection dimension. Secondly, a two-stage collaborative processing architecture of macroscopic index screening and microscopic fluctuation identification is adopted. This architecture is specifically divided into two stages: the first stage uses the energy-photon distribution index to perform spatial domain screening on the input dual-band infrared image to generate candidate target regions; the second stage utilizes the quantum fluctuation characteristics of the energy-photon distribution index to perform precise and detailed identification in the time domain. The two stages are executed sequentially, collaboratively completing the entire process from candidate region generation to target confirmation. By adopting this architecture, the invariance of quantum fluctuation noise can be utilized to break through detection performance under extremely low contrast conditions or spectral camouflage conditions, accurately detecting targets. It has excellent compatibility detection capabilities for conventional targets, low-contrast targets, and spectral camouflage targets, and is suitable for fields such as aerospace and national defense security that have an urgent need for ultra-low contrast target detection.

[0123] Figure 3 A flowchart illustrating the generation of candidate target regions according to an embodiment of this application is shown.

[0124] like Figure 3 As shown, this embodiment is one embodiment of operation S220, including operations S221 to S225.

[0125] In operation S221, the energy index of each pixel in the dual-band infrared image is calculated, and an energy index feature map is generated.

[0126] In operation S222, an adaptive threshold is calculated based on the statistical distribution of the energy index in the energy index feature map.

[0127] Figure 4 The illustration shows a schematic diagram of the construction of an energy index feature map according to an embodiment of the present application.

[0128] like Figure 4 As shown, firstly, dual-band images of the same scene can be acquired, including a mid-wave infrared (MWIR) image and a long-wave infrared (LWIR) image. These two images can then be geometrically corrected and matched at the pixel level using a dual-band registration method to ensure complete correspondence in the spatial coordinate system, ultimately yielding the registered MWIR and LWIR images. Then, the energy radiance ratio can be calculated for the MWIR image. Its calculation formula is expressed as:

[0129] ;

[0130] The photon radiance can be calculated from LWIR images. Its calculation formula is expressed as:

[0131] ;

[0132] Before calculating the energy radiance ratio and photon radiance ratio, the detection wavelength can be determined based on Wien's displacement law, where the detection wavelength includes the energy detection wavelength. and photon number detection wavelength Based on Planck's radiation law and the detection wavelength, the optimal detection wavelength can be determined. Next, the dimensionless radiance corresponding to the optimal wavelength is calculated, and a normalized difference index is constructed based on this dimensionless radiance to form the energy-light index. Its calculation formula is expressed as:

[0133] .

[0134] Subsequently, the energy index (i.e., the energy per pixel) was calculated for both the MWIR and LWIR images. , ), the calculated Mapping this onto each pixel of the image generates an energy index feature map.

[0135] In operation S223, the energy index feature map is segmented according to the adaptive threshold, and candidate pixels are marked.

[0136] In the embodiments of this application, an adaptive threshold can be calculated based on the statistical characteristics of pixel values ​​in the energy index feature map. The calculation process specifically includes: calculating the mean and standard deviation of the energy index in the energy index feature map; and generating an adaptive threshold based on the mean and standard deviation by adding a multiple of the mean and standard deviation. The calculation method of the adaptive threshold can be expressed by the formula:

[0137] ;

[0138] in, Represents pixels The light index value, Indicates an adaptive threshold. This represents the global mean of the energy index feature map, i.e., the mean. This represents the standard deviation of the energy index characteristic map. This indicates the sensitivity parameter, and the specific settings for the sensitivity parameter can be determined according to actual needs.

[0139] In embodiments of this application, pixels in the energy index feature map can be segmented and filtered according to a calculated adaptive threshold, and all pixels that meet the threshold can be filtered. The pixels are labeled as candidate pixels, and these pixels form a binary image B.

[0140] In operation S224, connected component analysis is performed on the marked candidate pixels to aggregate spatially adjacent candidate pixels into independent candidate regions.

[0141] In image processing, connected component analysis can be used to group spatially adjacent pixels or regions into the same category, thus achieving spatial clustering. Commonly used adjacency rules include 4-connectivity (considering only top, bottom, left, and right) and 8-connectivity (additionally including the four diagonal directions).

[0142] In the embodiments of this application, connected component analysis can be performed on the binary image B (i.e., candidate pixels) to aggregate spatially adjacent candidate pixels into independent candidate regions.

[0143] For example, the 8-connectivity rule can be used to label connected components in a binary image B. The 8-connectivity rule is a connection method used in digital image processing and computer vision to determine whether two pixels belong to the same region. Its core idea is that a pixel can be connected not only through its four directly adjacent pixels (4-connected), but also through its four diagonally adjacent pixels. That is, if two pixels are adjacent in the horizontal, vertical, or diagonal direction, they are considered connected. By performing connected component analysis on candidate pixels, spatially adjacent candidate pixels can be aggregated into independent candidate regions. Within each candidate region, each pixel is spatially adjacent.

[0144] In operation S225, morphological filtering is performed on the candidate region to generate candidate target regions.

[0145] Morphological operations are a set of image processing techniques based on set theory and geometric shape theory. Its core idea is to use a predefined shape (called a "structuring element") to probe and scan an image. By changing the set relationships of pixels in the image, noise, burrs, or unimportant details can be effectively removed. Opening is an important combinatorial operation in morphological operations. It follows the order of "erosion first, then dilation" and is mainly used to remove burrs and smooth boundaries, making the image clearer and more regular. Specifically, erosion is used to eliminate minute noise, causing the boundaries of objects in the image to shrink inwards; dilation is used to fill holes and connect objects, causing the boundaries of objects to expand outwards.

[0146] In the embodiments of this application, morphological filtering is performed on the candidate regions, specifically through opening operations. This operation not only effectively suppresses background noise and fills in small holes, but also regularizes their edge structures, greatly improving the accuracy and robustness of subsequent image analysis and processing. The calculation formula for the opening operation can be expressed as:

[0147] ;

[0148] Where B represents the candidate region (i.e., a binary image composed of candidate pixels), and S represents the structuring element. For example, it can be adopted... The matrix is ​​used as the structuring element S:

[0149] ;

[0150] It should be noted that the specific structure of the structural element S can be determined according to actual needs, and no restrictions are imposed here. This indicates that an erosion operation will be performed on the candidate region. This indicates that the candidate region is first subjected to erosion and then expansion.

[0151] In the embodiments of this application, for the candidate region, the first erosion operation can effectively remove small noise points in the candidate region, and then the dilation operation can maintain the overall shape and size of the candidate region unchanged.

[0152] Furthermore, for candidate regions filtered using the opening operation, a minimum area (e.g., 10 pixels) can be set to filter out all substandard regions. Using an area thresholding method, false alarms can be effectively filtered out by removing noise or small fragments from the image. The size of the area can be determined according to actual needs and is not limited here.

[0153] Figure 5 A schematic diagram illustrating a spatial domain fast filtering process according to an embodiment of this application is provided.

[0154] like Figure 5 As shown, firstly, the energy index feature map is obtained, which is a two-dimensional image. Then, the energy index feature map can be processed using steps 1 to 4 as follows:

[0155] Step 1: Adaptive threshold segmentation. Specifically, the global mean of the energy index feature map can be calculated. and global standard deviation An adaptive threshold is generated based on these two statistical features to create a light index feature map. Its calculation formula can be expressed as:

[0156] ;

[0157] The energy index feature map can be segmented based on this adaptive threshold. All pixels that meet the threshold are marked as candidate pixels, and a binary image is formed from these candidate pixels. .

[0158] Step 2: Connectivity analysis. Specifically, for binary images... It can perform connected component analysis, aggregate adjacent candidate pixels into independent candidate regions, and generate multiple connected regions that are independent of each other.

[0159] Step 3: Morphological opening operation. Specifically, a morphological opening operation is performed on each generated connected region. The calculation formula can be expressed as:

[0160] ;

[0161] For example, you can use Structural element S removes noise in small areas and smooths the boundaries.

[0162] Step 4: Area Filtering. Specifically, area filtering can be performed on the candidate regions. For example, setting the minimum area of ​​the candidate region... 10 pixels, reserved area The area will have an area Small areas are filtered out, thus generating the final candidate target region.

[0163] Subsequently, candidate target regions (ROIs) can be drawn on the energy index feature map, and each ROI can be labeled with a number, for example, , , ,in, This represents the first ROI. This indicates the second ROI. This represents the m-th ROI.

[0164] Figure 7 A flowchart illustrating a two-stage collaborative processing architecture according to an embodiment of this application is shown.

[0165] like Figure 7 As shown, the input data consists of MWIR and LWIR co-registered image data. Specifically, synchronous acquisition means acquiring infrared image data in both MWIR and LWIR bands simultaneously at the same time point (or within a very short time difference); registration operation means precisely aligning images from different sources (such as images from different bands, different viewpoints, or different times) in spatial location, so that the same physical point corresponds to the same pixel coordinates in different images. The two-stage collaborative processing architecture outputs the detection results by executing stage 1 and stage 2.

[0166] Phase 1: Index Screening (Spatial Domain, Macro, Rapid).

[0167] First, the energy index mapping is generated. Specifically, this can be done by calculating the energy index of each pixel in the acquired MWIR and LWIR images. , calculate Mapping this onto each pixel of the image generates an energy index feature map with macroscopic spectral characteristics, where... It is calculated based on the optimal detection wavelength. Energy index. The calculation formula can be expressed as:

[0168] .

[0169] Then, an adaptive threshold is calculated. Specifically, an adaptive threshold can be dynamically configured based on the global mean and variance of the energy index feature map and the statistical confidence level. This adaptive threshold is then used to segment the energy index feature map, thereby obtaining candidate pixels. This can be expressed by the formula:

[0170] .

[0171] Next, spatial clustering and morphological filtering are performed. Specifically, connected component analysis can be performed on candidate pixels to achieve spatial clustering, aggregating spatially adjacent candidate pixels into independent candidate regions. Further, morphological operations can be used to filter out noise from the candidate regions to generate the final list of candidate target regions. Specifically, the morphological operation is the morphological opening operation, the calculation formula of which can be expressed as:

[0172] .

[0173] Phase 2: Fluctuation Identification (Time Series, Microscopic, Precise).

[0174] First, temporal energy index fluctuations are extracted. Specifically, high-frame-rate time-series images are acquired for candidate target regions; then, based on these high-frame-rate time-series images, temporal energy index fluctuations can be extracted. The formula for calculating the energy index can be expressed as:

[0175] ;

[0176] in, The light index represents the energy of the time series image at frame t. Represents the photon number-radiance ratio of the time-series image in frame t. This represents the energy radiance of the time series image at frame t.

[0177] Then, quantum statistical feature extraction is performed. Specifically, based on the energy-light index... It can calculate the variance of energy index fluctuations. The value of the zero-delay second-order coherence function is estimated based on the energy-light index fluctuation variance model, and can be expressed by the formula:

[0178] ;

[0179] Then, decisions are made based on decision-making rules. Specifically, this can be based on... and Calculate the fluctuation enhancement factor Furthermore, based on fluctuation enhancement factors and discrimination threshold Decision making, fluctuation enhancement factor The calculation formula can be expressed as:

[0180] .

[0181] Finally, based on the decision results, the ultra-low contrast target detection results are output. These results have high sensitivity, high accuracy, low false alarm rate, and can achieve reverse spectral camouflage.

[0182] For example, in an embodiment of this application, a dual-band infrared imaging system can be used, which includes a mid-wave infrared sensor (band range 3). ~5 ) and long-wave infrared sensor (band range 8) ~12 The two sensors achieve synchronized image acquisition through hardware synchronization to acquire and register mid-wave infrared and long-wave infrared images. Image processing can be performed using an image processing platform equipped with an embedded computing unit accelerated by a GPU (Graphics Processing Unit). System parameters can be set as follows: Reference temperature: Reference wavelength: Frame rate: Time series length: 1 second (100 frames); Sensitivity parameters: Minimum candidate region area: Pixels. Target detection can be broken down into the following five steps.

[0183] The first step is for the system to simultaneously acquire registered MWIR and LWIR images.

[0184] The second step is to generate an energy index feature map.

[0185] (1) Optimal wavelength calculation: based on the scene temperature estimate Calculate the optimal detection wavelength for energy Optimal detection wavelength for photon number .

[0186] Optimal energy wavelength: ;

[0187] Optimal wavelength for photon number: .

[0188] (2) Calculation of the energy index: For each pixel of the MWIR and LWIR images, calculate the dimensionless radiance ratio. , :

[0189] ;

[0190] ;

[0191] Based on radiation ratio , Calculate the energy index based on the optimal detection wavelength. For each pixel of the MWIR and LWIR images calculate Generate an energy index feature map:

[0192] .

[0193] The third step is the spatial domain fast filtering process, which employs adaptive threshold segmentation and morphological processing.

[0194] (1) Feature map statistics based on energy index feature map:

[0195] Calculate the energy index graph global mean ;

[0196] Calculate the energy index graph Standard deviation .

[0197] (2) Adaptive thresholding: Based on the global mean and standard deviation, an adaptive threshold is generated for image segmentation. The threshold is set. Mark all that satisfy the condition. The pixels are selected as candidate pixels to generate a binary image B.

[0198] (3) Spatial clustering: The binary image B is labeled with connected components using the 8-connectivity rule, and spatially adjacent candidate pixels are aggregated into independent candidate regions.

[0199] (4) Morphological filtering: definition The structuring element S performs an opening operation on the labeled image: ,in, This represents the image after the opening operation. The opening operation can eliminate noise and smooth region boundaries. Furthermore, it can remove connected components with an area less than 10 pixels, filtering out small false alarms caused by noise. Finally, it can output 5 candidate target regions. to .

[0200] The fourth step is the quantum statistical identification process.

[0201] (1) Time series acquisition: For each candidate target region 100 frames of time-series image data of energy index were collected.

[0202] (2) Quantum feature extraction: Calculate the variance of the energy index of each region:

[0203] ;

[0204] in, This represents the variance of the energy index of the first candidate target region. This represents the variance of the energy index of the second candidate target region. This represents the variance of the energy index of the 6th candidate target region.

[0205] Based on the variance of the energy index in each region, estimate the value of the zero-delay second-order coherence function:

[0206] ;

[0207] in, This represents the value of the zero-delay second-order coherence function for the first candidate target region. The value of the zero-delay second-order coherence function for the second candidate target region. The value of the zero-delay second-order coherence function for the sixth candidate target region.

[0208] Calculate the variance of the background light index and the value of the zero-delay second-order coherence function:

[0209] .

[0210] (3) Discrimination threshold calibration: The discrimination threshold can be set by analyzing the receiver operation characteristic curves based on offline data. .

[0211] (4) Target identification: Calculate the fluctuation enhancement factor for each region and make target classification decisions based on the enhancement factor.

[0212] Fluctuation enhancement factor in the first candidate target region The calculation formula is:

[0213] .

[0214] Fluctuation enhancement factor in the second candidate target region The calculation formula is:

[0215] .

[0216] Fluctuation enhancement factor in the third candidate target region The calculation formula is:

[0217] .

[0218] Fluctuation enhancement factor in the fourth candidate target region The calculation formula is:

[0219] .

[0220] Fluctuation enhancement factor in the 5th candidate target region The calculation formula is:

[0221] .

[0222] Fluctuation enhancement factor in the 6th candidate target region The calculation formula is:

[0223] .

[0224] Target classification decision:

[0225] because Therefore Determined as a true target; due to Therefore It was determined to be a false alarm; because Therefore Determined as a true target; due to Therefore It was determined to be a false alarm; because Therefore Determined as a true target; due to Therefore It was determined to be a genuine target.

[0226] Step 5: Performance verification.

[0227] Figure 8A The diagram illustrates the detection results in different scenarios according to embodiments of this application. Figure 8B The diagram illustrates another detection result in a different scenario according to an embodiment of the present application.

[0228] like Figure 8A as well as Figure 8B As shown, in the embodiments of this application, three typical scenarios were used as experimental scenarios: baseline scenario (hereinafter referred to as baseline), low contrast scenario (hereinafter referred to as low contrast), and spectral camouflage scenario (hereinafter referred to as camouflage). In Figure 8, the vertical axis of each image represents the number of pixel rows, and the horizontal axis represents the number of pixel columns. The range of the number of pixel rows and the number of pixel columns is [0, 200]. Figure 8A Images (a) through (c) show mid-wave and long-wave infrared images in baseline, low-contrast, and camouflaged scenes, respectively. Figure 8A (d) to (f) in the figure show the energy index diagrams in the baseline, low contrast, and camouflage scenes, respectively; Figure 8B (g) to (i) in the figure respectively show the detection performance of the contrast method (i.e. intensity detection method) used in baseline, low contrast and camouflaged scenes; Figure 8B (j) to (l) in the figure respectively demonstrate the detection effect of the energy index fluctuation ultra-low contrast target detection method (hereinafter referred to as energy index method) of this application in baseline, low contrast and camouflage scenes.

[0229] Figure 9A The diagram illustrates a comparison of detection performance in different scenarios according to embodiments of this application. Figure 9B This illustration schematically shows another comparison chart of detection performance in different scenarios according to embodiments of this application.

[0230] like Figure 9A , Figure 9B As shown, in order to compare the intensity detection method and the energy index detection method, the embodiments of this application employ... Figure 9A (a) to (c) and Figure 9B The performance comparison metrics shown in (d) to (f) are, in order: fluctuation enhancement factor, physical contrast, sensitivity, accuracy, false alarm rate, and F1 score. Figure 9A , Figure 9B The horizontal axis represents the experimental scenarios, in order: baseline, low contrast, and camouflage; the vertical axis represents the values ​​of the corresponding performance comparison indicators.

[0231] like Figure 8A , Figure 8B , Figure 9A as well as Figure 9B As shown in the verification results under three typical scenarios, we can see that:

[0232] (1) Baseline scene (temperature difference between target and background) Both the energy index method and the intensity method performed well, with detection probabilities greater than 0.98, but the energy index method reduced the false alarm rate by 85%.

[0233] (2) Low contrast scene (temperature difference between target and background) The intensity method reduces the detection probability to below 0.005, while the energy index method maintains a detection probability above 0.98.

[0234] (3) Spectral camouflage scenario: In this scenario, the temperature difference between the target and the background is minimal (the temperature difference between the target and the background is very small). Furthermore, the target surface undergoes special treatment, resulting in higher emissivity in the mid-wave infrared (MWIR) and long-wave infrared (LWIR) bands. Through careful design, the overall radiation intensity is made highly similar to the background, thus deceiving traditional intensity detection methods. Experimental results show that intensity methods are completely ineffective under this targeted camouflage, with a detection probability of 0; the energy-photon index method of this application, because it can keenly capture the inherent differences in energy-photon distribution characteristics between the target and the background, can still maintain a detection probability of over 0.97.

[0235] Based on the above-mentioned method for detecting ultra-low contrast targets due to energy index fluctuations, this application also provides a device for detecting ultra-low contrast targets due to energy index fluctuations. The following will be combined with... Figure 10 The device is described in detail.

[0236] Figure 10 A schematic block diagram of an ultra-low contrast target detection device with energy index fluctuation according to an embodiment of this application is shown.

[0237] like Figure 10 As shown, the energy index fluctuation ultra-low contrast target detection device 1000 of this embodiment includes an energy index construction module 1010, a candidate target region determination module 1020, a second-order photonic coherence fluctuation calculation module 1030, a fluctuation enhancement factor calculation module 1040, and a target detection result output module 1050.

[0238] The energy-photon index construction module 1010 is used to construct the energy-photon index, which is used to quantify the difference in energy-photon distribution between the mid-wave infrared and long-wave infrared bands. In one embodiment, the energy-photon index construction module 1010 can be used to perform the operation S210 described above, which will not be repeated here.

[0239] The candidate target region determination module 1020 is used to perform spatial domain filtering on a dual-band infrared image based on the energy index to generate candidate target regions. The dual-band infrared image includes a mid-wave infrared image and a long-wave infrared image. In one embodiment, the candidate target region determination module 1020 can be used to perform the operation S220 described above, which will not be repeated here.

[0240] The second-order photonic coherent fluctuation calculation module 1030 is used to calculate the second-order photonic coherent fluctuations of the candidate target region based on the energy index time series data of the candidate target region. The second-order photonic coherent fluctuations characterize the temporal fluctuations associated with the photon number. In one embodiment, the second-order photonic coherent fluctuation calculation module 1030 can be used to perform the operation S230 described above, which will not be repeated here.

[0241] The fluctuation enhancement factor calculation module 1040 is used to calculate the fluctuation enhancement factor based on second-order photon coherent fluctuations, wherein the fluctuation enhancement factor is used to characterize the difference in quantum statistical properties between the target and the background. In one embodiment, the fluctuation enhancement factor calculation module 1040 can be used to perform the operation S240 described above, which will not be repeated here.

[0242] The target detection result output module 1050 is used for quantum statistical identification based on the fluctuation enhancement factor and outputs the target detection result. In one embodiment, the target detection result output module 1050 can be used to perform the operation S250 described above, which will not be repeated here.

[0243] In the embodiments of this application, the energy-light index construction module 1010 is specifically used for: determining the detection wavelength based on Wien's displacement law, wherein the detection wavelength includes the energy detection wavelength and the photon number detection wavelength; determining the optimal detection wavelength based on Planck's radiation law and the detection wavelength, wherein the optimal detection wavelength includes the energy-optimal detection wavelength and the photon number-optimal detection wavelength; determining the radiance corresponding to the optimal detection wavelength based on the optimal detection wavelength, wherein the radiance includes the energy radiance and the photon number radiance; and constructing the normalized differential energy-light index based on the radiance corresponding to the optimal detection wavelength.

[0244] In the embodiments of this application, the candidate target region determination module 1020 is specifically used for: calculating the energy index of each pixel in the dual-band infrared image and generating an energy index feature map; calculating an adaptive threshold based on the statistical distribution of the energy index in the energy index feature map; segmenting the energy index feature map according to the adaptive threshold and marking candidate pixels; performing connected component analysis on the marked candidate pixels and aggregating spatially adjacent candidate pixels into independent candidate regions; and performing morphological filtering on the candidate regions to generate candidate target regions.

[0245] In the embodiments of this application, the second-order photonic coherent fluctuation calculation module 1030 is specifically used to: collect energy-light index time series data at a preset frame rate within the candidate target region; and calculate the second-order photonic coherent fluctuation of the candidate target region based on the energy-light index time series data using an energy-light index fluctuation variance model, wherein the energy-light index fluctuation variance model is used to characterize the correlation between the energy-light index fluctuation variance and the second-order coherent function.

[0246] In the embodiments of this application, the target detection result output module 1050 is specifically used to: calculate the fluctuation enhancement factor value of the candidate target region; compare the fluctuation enhancement factor value with a preset discrimination threshold and output the comparison result, wherein the preset discrimination threshold is a critical value used to distinguish between real targets and false alarms; and output the target detection result based on the comparison result.

[0247] According to embodiments of this application, by constructing an energy-photon distribution index, the energy-photon distribution difference of Planck's law is transformed into a measurable detection dimension; by adopting a two-stage collaborative processing architecture of macroscopic index screening and microscopic fluctuation identification, the physical property of quantum fluctuation noise invariance can be utilized to accurately detect target objects under extreme low contrast conditions or spectral camouflage conditions where the temperature difference between the target and the background is extremely small and the emissivity difference is subtle.

[0248] According to embodiments of this application, any multiple modules among the energy index construction module 1010, candidate target region determination module 1020, second-order photonic coherence fluctuation calculation module 1030, fluctuation enhancement factor calculation module 1040, and target detection result output module 1050 can be combined into one module, or any one of these modules can be split into multiple modules. Alternatively, at least some of the functions of one or more of these modules can be combined with at least some of the functions of other modules and implemented in one module. According to embodiments of this application, at least one of the energy index construction module 1010, candidate target region determination module 1020, second-order photonic coherent fluctuation calculation module 1030, fluctuation enhancement factor calculation module 1040, and target detection result output module 1050 can be at least partially implemented as a hardware circuit, such as a field-programmable gate array (FPGA), programmable logic array (PLA), system-on-a-chip, system-on-a-substrate, system-on-package, application-specific integrated circuit (ASIC), or any other reasonable method of integrating or packaging the circuit, or implemented in software, hardware, or firmware, or in any appropriate combination of any of these three implementation methods. Alternatively, at least one of the energy index construction module 1010, candidate target region determination module 1020, second-order photonic coherent fluctuation calculation module 1030, fluctuation enhancement factor calculation module 1040, and target detection result output module 1050 can be at least partially implemented as a computer program module, which can perform corresponding functions when the computer program module is run.

[0249] Figure 11 A block diagram schematically illustrates an electronic device suitable for implementing an energy index fluctuation ultra-low contrast target detection method according to an embodiment of this application.

[0250] like Figure 11As shown, an electronic device 1100 according to an embodiment of this application includes a processor 1101, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1102 or a program loaded from a storage portion 1108 into a random access memory (RAM) 1103. The processor 1101 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 1101 may also include onboard memory for caching purposes. The processor 1101 may include a single processing unit or multiple processing units for performing different actions of the method flow according to embodiments of this application.

[0251] RAM 1103 stores various programs and data required for the operation of electronic device 1100. Processor 1101, ROM 1102, and RAM 1103 are interconnected via bus 1104. Processor 1101 executes various operations of the method flow according to embodiments of this application by executing programs in ROM 1102 and / or RAM 1103. It should be noted that the programs may also be stored in one or more memories other than ROM 1102 and RAM 1103. Processor 1101 may also execute various operations of the method flow according to embodiments of this application by executing programs stored in said one or more memories.

[0252] According to embodiments of this application, the electronic device 1100 may further include an input / output (I / O) interface 1105, which is also connected to a bus 1104. The electronic device 1100 may also include one or more of the following components connected to the input / output (I / O) interface 1105: an input section 1106 including a keyboard, mouse, etc.; an output section 1107 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 1108 including a hard disk, etc.; and a communication section 1109 including a network interface card such as a LAN card, modem, etc. The communication section 1109 performs communication processing via a network such as the Internet. A drive 1110 is also connected to the input / output (I / O) interface 1105 as needed. A removable medium 1111, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 1110 as needed so that computer programs read from it can be installed into the storage section 1108 as needed.

[0253] This application also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs, which, when executed, implement the method according to the embodiments of this application.

[0254] According to embodiments of this application, the computer-readable storage medium can be a non-volatile computer-readable storage medium, such as including but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this application, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to embodiments of this application, the computer-readable storage medium may include ROM1102 and / or RAM1103 and / or one or more memories other than ROM1102 and RAM1103 described above.

[0255] Embodiments of this application also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowchart. When the computer program product is run on a computer system, the program code enables the computer system to implement the energy index fluctuation ultra-low contrast target detection method provided by embodiments of this application.

[0256] When the computer program is executed by processor 1101, it performs the functions defined in the system / apparatus of the embodiments of this application. According to the embodiments of this application, the systems, apparatuses, modules, units, etc. described above can be implemented by computer program modules.

[0257] In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and may be downloaded and installed via the communication section 1109, and / or installed from the removable medium 1111. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.

[0258] In such an embodiment, the computer program can be downloaded and installed from a network via communication section 1109, and / or installed from removable medium 1111. When the computer program is executed by processor 1101, it performs the functions defined in the system of the embodiments of this application. According to the embodiments of this application, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0259] According to embodiments of this application, program code for executing the computer programs provided in embodiments of this application can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Julia, Java, C++, Python, C, or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0260] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0261] Those skilled in the art will understand that the features described in the various embodiments of this application can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in this application. In particular, the features described in the various embodiments of this application can be combined and / or combined in various ways without departing from the spirit and teachings of this application. All such combinations and / or combinations fall within the scope of this application.

Claims

1. A method for detecting an ultra-low contrast target capable of optical exponential fluctuation, characterized in that, The method includes: Construct an energy-photon index, which is used to quantify the difference in energy-photon distribution between the mid-wave infrared and long-wave infrared bands; Based on the energy index, spatial domain filtering is performed on the dual-band infrared image to generate candidate target regions, wherein the dual-band infrared image includes a mid-wave infrared image and a long-wave infrared image. Based on the energy index time series data of the candidate target region, the second-order photon coherent fluctuations of the candidate target region are calculated, wherein the second-order photon coherent fluctuations are used to characterize the temporal fluctuations of photon number correlation. Based on the second-order photonic coherent fluctuations, a fluctuation enhancement factor is calculated, wherein the fluctuation enhancement factor is used to characterize the difference in quantum statistical properties between the target and the background; Quantum statistical identification is performed based on the fluctuation enhancement factor, and the target detection result is output.

2. The method of claim 1, wherein, The constructed energy index includes: Based on Wien's displacement law, the detection wavelength is determined, wherein the detection wavelength includes the energy detection wavelength and the photon number detection wavelength; Based on Planck's radiation law and the detection wavelength, the optimal detection wavelength is determined, wherein the optimal detection wavelength includes the energy-optimal detection wavelength and the photon-optimal detection wavelength. Based on the optimal detection wavelength, the radiation ratio corresponding to the optimal detection wavelength is determined, wherein the radiation ratio includes energy radiation ratio and photon number radiation ratio; Based on the radiance corresponding to the optimal detection wavelength, a normalized differential energy index is constructed.

3. The method of claim 2, wherein, The optimal detection wavelength is determined by the temperature sensitivity maximization criterion and displacement constraints.

4. The method according to claim 1, characterized in that, The step of spatial domain filtering of the dual-band infrared image based on the energy index to generate candidate target regions includes: Calculate the energy index of each pixel in the dual-band infrared image and generate an energy index feature map; Based on the statistical distribution of the energy index in the energy index feature map, an adaptive threshold is calculated; The energy index feature map is segmented according to the adaptive threshold, and candidate pixels are marked. Connectivity analysis is performed on the marked candidate pixels to aggregate spatially adjacent candidate pixels into independent candidate regions; Morphological filtering is performed on the candidate regions to generate candidate target regions.

5. The method according to claim 4, characterized in that, The step of calculating the adaptive threshold based on the statistical distribution of energy index values ​​in the energy index feature map includes: Calculate the mean and standard deviation of the energy index in the energy index feature graph; An adaptive threshold is generated based on the mean and the standard deviation by adding a multiple of the mean and the standard deviation.

6. The method according to claim 1, characterized in that, The calculation of the second-order photonic coherent fluctuations of the candidate target region based on the energy index time series data of the candidate target region includes: Within the candidate target area, collect time-series data of the energy-light index at a preset frame rate; Based on the energy index time series data, the second-order photon coherence fluctuations of the candidate target region are calculated using the energy index fluctuation variance model, wherein the energy index fluctuation variance model is used to characterize the correlation between the energy index fluctuation variance and the second-order coherence function.

7. The method according to claim 6, characterized in that, The step of collecting energy index time series data at a preset frame rate within the candidate target area includes: Obtain the candidate target region and the preset collection time period; During the preset acquisition time period, time series data of the energy index of the candidate target region are acquired.

8. The method according to claim 1, characterized in that, The quantum statistical identification based on the fluctuation enhancement factor outputs target detection results including: Calculate the fluctuation enhancement factor value of the candidate target region; The fluctuation enhancement factor value is compared with a preset discrimination threshold, and the comparison result is output. The preset discrimination threshold is a critical value used to distinguish between real targets and false alarms. Based on the comparison results, the target detection results are output.

9. The method according to claim 8, characterized in that, The preset discrimination threshold is determined through receiver operation characteristic curve analysis and optimization.

10. A target detection device for ultra-low contrast due to energy index fluctuations, characterized in that, The device includes: The energy-photon index construction module is used to construct the energy-photon index, which is used to quantify the difference in energy-photon distribution between the mid-wave infrared and long-wave infrared bands. The candidate target region determination module is used to perform spatial domain filtering on the dual-band infrared image based on the energy index to generate candidate target regions, wherein the dual-band infrared image includes a mid-wave infrared image and a long-wave infrared image. The second-order photonic coherent fluctuation calculation module is used to calculate the second-order photonic coherent fluctuation of the candidate target region based on the energy index time series data of the candidate target region, wherein the second-order photonic coherent fluctuation is used to characterize the temporal fluctuation of the photon number correlation. The fluctuation enhancement factor calculation module is used to calculate the fluctuation enhancement factor based on the second-order photon coherent fluctuations, wherein the fluctuation enhancement factor is used to characterize the difference in quantum statistical properties between the target and the background; and The target detection result output module is used to perform quantum statistical identification based on the fluctuation enhancement factor and output the target detection result.

11. An electronic device, comprising: One or more processors; Memory, used to store one or more computer programs. The characteristic feature is that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 9.

12. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, When the computer program or instructions are executed by a processor, they implement the steps of the method according to any one of claims 1 to 9.

13. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed by a processor, they implement the steps of the method according to any one of claims 1 to 9.

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