System and method for detecting optical properties of camouflage materials

Abstract

The application discloses a kind of optical performance detection system and method of camouflage material, it is related to camouflage material detection technical field, including structure identification and lighting control module, for gathering the surface image of camouflage material sample, identify sample surface structure, start full-spectrum light source, simulate from weak light to strong light step-by-step illumination, based on standard illumination RGB imaging;Region division module is used to apply superpixel segmentation algorithm to image, automatically divide sub-region, carry out hyperspectral reflectivity scanning to each region, synchronously collect polarization direction light intensity image, calculate Stokes vector and construct polarization enhancement image.The present application not only can realize the frequency domain quantitative analysis of micro interference fringe, the accurate mask extraction of polarization abnormal area, but also through multi-angle reflection sampling and score function modeling, effectively quantifies the optical exposure risk of camouflage material under complex illumination and viewing angle condition, significantly improves detection precision, regional pertinence and automation level.

Description

Technical Field

[0001] This invention relates to the field of camouflage material detection technology, and in particular to a system and method for detecting the optical properties of camouflage materials. Background Technology

[0002] With the continuous development of modern military reconnaissance and intelligent imaging technologies, the optical camouflage capability of camouflage materials has increasingly become a core indicator in material research and development and application evaluation. Existing camouflage materials typically rely on the manipulation of reflectivity in the visible and near-infrared bands, achieving color fusion and reflection control of the background through surface pattern design, nanostructure construction, or multilayer thin film deposition. Against the backdrop of continuously upgrading imaging technology, traditional evaluation methods mainly use spectral reflectance testing or image comparison techniques for preliminary judgment. However, these methods often fail to accurately identify the risk of camouflage failure caused by factors such as structural color, angle-dependent reflectance, or polarization characteristics, especially under complex conditions such as dynamic changes in natural light, oblique angle detection, or polarized laser illumination, which makes them more susceptible to exposure. On the other hand, advanced optical detection methods such as polarization imaging, hyperspectral imaging, and BRDF (bidirectional reflectance distribution function) modeling have gradually been introduced into the field of camouflage material research to capture more subtle differences in optical response. However, a complete detection system that can integrate multi-dimensional physical indicators and achieve accurate region division and scoring is currently lacking. Summary of the Invention

[0003] In view of the aforementioned existing problems, the present invention is proposed.

[0004] Therefore, this invention provides an optical performance testing system and method for camouflage materials, solving the current problem of lacking a complete testing system that can integrate multi-dimensional physical indicators and achieve accurate region division and scoring.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0006] In a first aspect, the present invention provides an optical performance testing system for camouflage materials, comprising,

[0007] The structure recognition and lighting control module is used to acquire surface images of camouflage material samples, identify the sample surface structure, activate a full-spectrum light source, simulate gradual illumination from weak light to strong light, and perform RGB imaging based on standard illuminance.

[0008] The region segmentation module is used to apply superpixel segmentation algorithms to the image, automatically divide it into sub-regions, perform hyperspectral reflectance scanning on each region, synchronously acquire polarization direction light intensity images, calculate Stokes vectors, and construct polarization-enhanced images.

[0009] The region mask generation module is used to adaptively segment the polarization reflection intensity map using the Otsu algorithm, correct the boundary using morphological erosion and dilation operations, and generate a mask IE(x,y) for the target polarization reflection significant region.

[0010] The interference scoring module is used to select hyperspectral reflectance curves within the IE(x,y) range, and to use fast Fourier transform to detect periodic fringe fluctuations and calculate the interference score Ir(i).

[0011] The multi-angle BRDF measurement module is used to change the observation angle of the sample, perform multi-angle spectral acquisition on the mask area, establish the BRDF response function, and calculate the directional deviation ΔR(i).

[0012] The scoring module is used to comprehensively score the camouflage performance based on the intensity index Ir(i) and the directional deviation ΔR(i). It generates a two-dimensional heat map based on the camouflage performance score, displays the camouflage exposure risk level, and automatically generates a detection report.

[0013] As a preferred embodiment of the optical performance testing system for the camouflage material described in this invention, the step of applying the Otsu algorithm to adaptively segment the polarization reflection intensity map, using morphological erosion and dilation operations to correct the boundaries, and generating a mask IE(x,y) for the significant polarization reflection region of the target includes:

[0014] Traverse the entire polarization reflection intensity map and normalize it to obtain the normalized image pixel values;

[0015] Perform a difference calculation on the entire row of pixels for each image row;

[0016] The standard Sobel operator is applied to the entire image to obtain the horizontal gradient map and the vertical gradient map, and the local edge structure intensity of the image is calculated. A pixel energy function is constructed, and the pixel-level energy term E(x,y) is calculated by fusing brightness change and edge suppression information. The energy of all pixels is accumulated row by row to obtain the row energy integration. The row with the maximum energy y1 is searched as the structural boundary to divide the image into upper and lower sub-images. If the pixel in the y-th row is less than y1, it is the upper half of the image; otherwise, it is the lower half of the image.

[0017] The normalized image is subjected to median filtering to obtain the median map M(x,y). The median map is subjected to mean filtering to obtain the local average map G(x,y). Based on M(x,y) and G(x,y), a joint gray-level pair (i,j) for each pixel (x,y) is constructed. The gray-level pairs are assigned to the upper and lower halves of the image according to their respective regions. The frequency of all (i,j) in the two regions is counted to form a joint gray-level histogram.

[0018] Define a joint grayscale threshold combination (s,t) and divide (i,j) into 4 regions according to (s,t) in each histogram;

[0019] Calculate the sum of the relative frequencies of all gray-level pairs (i,j) belonging to region k in the entire image from the joint gray-level histogram. Using the gray-level values ​​of the median-filtered image M(x,y) as the main variable, calculate the joint gray-level mean μ for each region. k and population mean μ T According to the two-dimensional Otsu definition, the comprehensive scoring index of inter-class differences is calculated. For each (s,t), the inter-class variance trace values ​​of the upper and lower halves of the joint gray-level histogram are calculated. The two trace values ​​are multiplied to obtain the joint scoring function Q(s,t).

[0020] Iterate through all rating results and find the highest-scoring threshold combination;

[0021] In the upper and lower sub-images, each pixel (x,y) is traversed separately to construct the upper and lower sub-image masks, and the upper and lower region masks are merged into the full image mask M0(x,y);

[0022] Connected regions are marked on the mask image M0(x,y), regions with an area smaller than a preset threshold are removed, and morphological closing and opening operations are performed on the removed mask image to obtain the final polarization salient region mask image.

[0023] As a preferred embodiment of the optical performance testing system for the camouflage material described in this invention, the following steps are described: acquiring surface images of the camouflage material sample, identifying the sample surface structure, activating a full-spectrum light source, simulating gradual illumination from weak to strong light, performing RGB imaging based on standard illuminance, and applying a superpixel segmentation algorithm to the image to automatically divide it into sub-regions, including:

[0024] High-resolution grayscale images of the sample surface are acquired, and a two-dimensional fast Fourier transform is performed on the images to obtain a spectrum. The spectrum amplitude is calculated and normalized before the main frequency corresponding to the direction of maximum amplitude in the spectrum is extracted.

[0025] If the main frequency is greater than the preset threshold U, the energy proportion of the main frequency is further evaluated. If the energy proportion of the main frequency is greater than the preset threshold u, the region is considered to have the possibility of artificial structure color interference.

[0026] Activate the controllable full-spectrum LED array light source and set the light intensity to five fixed levels, including I1 to I5;

[0027] At each illuminance level, the reflectance spectrum curves of the material sample surface were collected, and the normalized reflectance R(λ) was calculated based on the collected data. j ,I i For all wavelengths under the i-th illuminance, the reflectance R(λ,I) i ), calculate its mean and the standard deviation of reflectance σ(I) under this illuminance iThe five sets of standard deviation results are compared, and the illuminance level number corresponding to the smallest one is selected as the standard illuminance. Based on the determined standard illuminance, the industrial camera system is started to acquire sample images and obtain three-channel RGB images.

[0028] As a preferred embodiment of the optical performance testing system for the camouflage material described in this invention, the steps of applying a superpixel segmentation algorithm to the image, automatically dividing it into sub-regions, performing hyperspectral reflectance scanning on each region, synchronously acquiring polarization direction light intensity images, calculating the Stokes vector, and constructing a polarization-enhanced image include:

[0029] The RGB image matrix after linearization of light intensity is obtained by processing each component of each pixel individually according to the standard sRGB gamma inverse correction.

[0030] For each pixel, the linear R, G, B values ​​are converted into X, Y, Z channel values. The XYZ components of each pixel are normalized. A perceptual mapping function f(p) is defined to establish a color-luminance nonlinear response. The Lab channel value of each pixel is calculated, and the Lab space three-channel matrix I is output. Lab (x,y);

[0031] The clustering window spacing S is calculated based on the preset number of regions K, and a two-dimensional spatial coordinate matrix is ​​constructed for each pixel (x,y) in the image.

[0032] For each pixel (x, y) in the image, construct the five-dimensional joint feature vector V required for SLIC clustering. x,y ;

[0033] V of all pixels x,y The feature combination is a five-dimensional tensor structure T V (x,y);

[0034] Based on S, K cluster center points are initialized in a uniform grid on the image. For each cluster center, the composite distance between the current pixel and the center is calculated. The pixel (x,y) is assigned to the cluster center number with the smallest distance. The mean of the five-dimensional features of each region is recalculated as the new center. The moving distance m between the old center and the new center is calculated. If all m are less than the preset threshold β, the clustering is stopped, and the final pixel assignment mask matrix is ​​obtained. For each region corresponding to number i, 8-neighborhood connectivity analysis is performed.

[0035] The closed boundary point set of the main connected component is extracted using a contour tracing algorithm and saved as a region vector mask. Then, the centroid coordinates of the region are calculated.

[0036] Extract the centroid coordinates of each sub-region and set them as the target sampling point. Perform pixel-by-pixel scanning on each sub-region to obtain a two-dimensional spectral matrix. Start the DoFP polarization camera to output images in four polarization directions. Calculate the Stokes vector components for each pixel (x,y).

[0037] The polarization enhancement image IS(x,y) of the region is calculated by combining the obtained Stokes vector components.

[0038] As a preferred embodiment of the optical performance testing system for the camouflage material described in this invention, the process involves: selecting hyperspectral reflectance curves within the IE(x,y) range; using Fast Fourier Transform to detect periodic fringe fluctuations and calculating the interference score Ir(i); traversing each sub-region numbered i; if the corresponding sample is a structural color material, then filtering the pixels marked by the IE(x,y) mask within that region, retaining only the spectral data in regions with significant polarization enhancement; and averaging the hyperspectral reflectance curves at all mask points within that region to obtain a representative reflectance curve. The amplitude spectrum value F at frequency index k is obtained by applying a one-dimensional fast Fourier transform operation. i (k), calculate the average energy value of the high-frequency band as the interferometric score Ir(i) of the region.

[0039] As a preferred embodiment of the optical performance testing system for the camouflage material described in this invention, the following steps are taken: Changing the sample observation angle, performing multi-angle spectral acquisition on the mask area, establishing a BRDF response function, and calculating the directional deviation ΔR(i) refers to controlling a three-axis servo motor platform to adjust the sample attitude, fixing the incident light source. After each angle setting, using a hyperspectral imaging module, under the condition of maintaining standard illumination, hyperspectral reflectance data of each sub-region within the mask area IE(x,y) are acquired. For each sub-region i, at each angle θ... k At this point, calculate the BRDF function value f. r,i (θ k ,λ);

[0040] Averaging is performed along the wavelength dimension to obtain the band-averaged BRDF response for the angle-region.

[0041] For each sub-region i, the average BRDF response at all angles is taken as the mean.

[0042] based on and Calculate the directional consistency deviation ΔR(i) of sub-region i.

[0043] As a preferred embodiment of the optical performance testing system for the camouflage material described in this invention, the following steps are taken: A comprehensive camouflage performance score is generated based on the intensity index Ir(i) and the directional deviation ΔR(i). A two-dimensional heatmap is generated based on the camouflage performance score to display the camouflage exposure risk level and automatically generate a test report. This involves linearly weighting the interference score Ir(i) and the directional deviation score ΔR(i) for each sub-region i to construct a comprehensive optical camouflage performance score Score(i). The maximum score is mapped to 1, and the minimum score is mapped to 0, forming a normalized score matrix. Each sub-region uses its boundary as a primitive and fills the normalized score matrix value with color to form a transparent layer heatmap. A test report is then generated based on the test data.

[0044] Secondly, the present invention provides a method for detecting the optical properties of camouflage materials, including,

[0045] Surface images of camouflage material samples are acquired, RGB imaging is performed based on standard illumination and sub-regions are automatically divided, hyperspectral reflectance is scanned in each region, polarization direction light intensity images are acquired simultaneously, Stokes vectors are calculated and polarization-enhanced images are constructed.

[0046] Adaptively segment and correct the boundaries of the polarization reflection intensity map to generate a mask IE(x,y) for the significant polarization reflection region of the target.

[0047] Hyperspectral reflectance curves within the IE(x,y) range were selected, periodic fringe fluctuations were detected and interference scores Ir(i) were calculated. The observation angle of the sample was changed, and multi-angle spectral acquisition was performed on the mask area. The BRDF response function was established, and the directional deviation ΔR(i) was calculated.

[0048] The camouflage performance is comprehensively scored based on the intensity index Ir(i) and the directional deviation ΔR(i). A two-dimensional heat map is generated based on the camouflage performance score to show the camouflage exposure risk level and an automatic detection report is generated.

[0049] Thirdly, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, it implements any step of the optical performance detection method for camouflage materials as described in the first aspect of the present invention.

[0050] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the optical performance detection method for camouflage materials as described in the first aspect of the present invention.

[0051] The beneficial effects of this invention are as follows: This invention can not only automatically identify whether there is structural color interference behavior on the sample surface, but also extract the target region by simultaneously acquiring hyperspectral and polarization images, combining Stokes vectors and polarization enhancement maps, and quantitatively scoring the high-frequency interference fluctuations of the reflection spectrum based on Fast Fourier Transform. Simultaneously, the platform integrates a multi-angle attitude adjustment mechanism, models the reflection consistency of materials under different viewing angles by constructing a BRDF response function, and finally fuses interference scores and orientation deviation scores to generate a two-dimensional heatmap and an automatic detection report. This not only enables frequency domain quantitative analysis of microscopic interference fringes and precise mask extraction of polarization anomaly regions, but also effectively quantifies the optical exposure risk of camouflage materials under complex lighting and viewing angle conditions through multi-angle reflection sampling and scoring function modeling, significantly improving detection accuracy, regional targeting, and automation level. Attached Figure Description

[0052] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 This is a flowchart of the optical performance testing method for the camouflage material in Example 1.

[0054] Figure 2 This is a structural diagram of the optical performance testing system for the camouflage material in Example 1.

[0055] Figure 3 This is a schematic diagram of the image processing flow in Example 1. Detailed Implementation

[0056] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0057] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0058] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0059] Example 1, referring to Figures 1-3 This is the first embodiment of the present invention, which provides an optical performance testing system for camouflage materials, comprising the following steps:

[0060] S1, Structure Recognition and Illumination Control Module, is used to acquire surface images of camouflage material samples, identify the surface structure of the samples, activate the full-spectrum light source, simulate the gradual illumination from weak light to strong light, and perform RGB imaging based on standard illuminance;

[0061] Specifically, surface images of camouflage material samples are acquired, the sample surface structure is identified, a full-spectrum light source is activated, and a gradual illumination from weak to strong light is simulated. RGB imaging is performed based on standard illuminance, and a superpixel segmentation algorithm is applied to the images to automatically divide them into sub-regions, including:

[0062] The camouflage material sample was placed on a motorized triaxial sample stage. The platform attitude was set as the initial angle. The scanning area was selected and area array imaging was performed on the scanning area. A high-resolution grayscale image of the sample surface was acquired. The image was subjected to a two-dimensional fast Fourier transform to obtain a spectrum. The spectrum amplitude was calculated and normalized before the main frequency corresponding to the direction with the largest amplitude in the spectrum was extracted.

[0063] If the dominant frequency is greater than the preset threshold U, the energy ratio of the dominant frequency is further evaluated. If the energy ratio of the dominant frequency is greater than the preset threshold u, the region is considered to have the possibility of artificial structure color interference, and the sample is marked as 1; otherwise, it is marked as 0.

[0064] Thresholds U and u are set empirically;

[0065] The controllable full-spectrum LED array light source is activated; in this embodiment, the spectral range covers 400-1000nm.

[0066] The light intensity is set to five fixed levels, including I1 to I5, where I1 is (weak light / dawn), I2 is (cloudy / forest), I3 is (daytime / indoor light), I4 is (sunny daylight), and I5 is (strong sunlight or reflective surface). In this embodiment, the light intensity of I1 is 500 lux, the light intensity of I2 is 2000 lux, the light intensity of I3 is 6000 lux, the light intensity of I4 is 12000 lux, and the light intensity of I5 is 20000 lux.

[0067] At each illuminance level, while maintaining the platform's orientation, the reflectance spectrum curves of the material sample surface were collected, including the sample's reflectance spectral intensity and the reflectance spectral intensity of a reference standard white board. Based on the collected data, the normalized reflectance R(λ) was calculated. j ,I i ):

[0068]

[0069] In the formula, L s (λ j ,I i ) is the illuminance I of the sample at the j-th wavelength point. i The reflected radiation intensity under L ref R(λ j ,I i ) is the standard whiteboard radiance, ρ ref The reflectivity is known for a standard whiteboard.

[0070] To select the optimal illuminance, it is necessary to evaluate the variation of reflectance with wavelength under different illuminance conditions. Assuming the reflectance curve has N sampling points (ideally 50–100 points at equal intervals within the wavelength range), then:

[0071] Reflectance R(λ,I) for all wavelengths under the i-th illuminance i ), calculate its mean

[0072]

[0073] Calculate the standard deviation of reflectance σ(I) under this illuminance. i ):

[0074]

[0075] The five sets of standard deviation results are compared, and the illuminance level number corresponding to the smallest one is selected as the standard illuminance.

[0076] Based on the determined standard illumination, the industrial camera system is started. In this embodiment, a Basler acA1920-155uc camera is selected to acquire sample images and obtain three-channel RGB images.

[0077] Structural frequency analysis enables rapid identification of potential structural color materials, improving the structural adaptability of the detection process and avoiding redundant calculations. The illuminance response discrimination mechanism ensures that the system operates under optimal optical performance conditions, enhancing the consistency and accuracy of subsequent reflectivity, polarization, and interference data. Placing superpixel segmentation of the image before standard illuminance image acquisition is beneficial for constructing a realistic spatial partition map under natural light response conditions, which helps in subsequent regional optical characteristic extraction and local scoring.

[0078] S2, Region Division Module, is used to apply superpixel segmentation algorithm to the image, automatically divide it into sub-regions, perform hyperspectral reflectance scanning on each region, synchronously acquire polarization direction light intensity images, calculate Stokes vectors and construct polarization-enhanced images;

[0079] Specifically, the range of RGB image pixel values ​​is normalized from [0,255] to [0,1], and each component of each pixel is processed separately according to the standard sRGB gamma inverse correction to obtain the RGB image matrix after light intensity linearization.

[0080] For each pixel, the linear R, G, B values ​​are converted into X, Y, Z channel values ​​through standard matrix multiplication (the matrix is ​​based on the sRGB and D65 standard light source definitions) to obtain an XYZ channel image;

[0081] Using the CIE D65 white point as a reference, the XYZ components of each pixel are normalized, and a perceptual mapping function f(p) is defined to establish a color-luminance nonlinear response:

[0082]

[0083] In the formula, δ is the boundary value, used to define the piecewise nonlinear mapping of the function, and p is the input value;

[0084] According to the CIE specification, the Lab channel value is calculated for each pixel:

[0085] L(x,y)=116·f(y r )-16

[0086] a(x,y)=500·(f(x r )-f(y r ))

[0087] b(x,y)=200·(f(y r )-f(z r ))

[0088] In the formula, L(x,y) is the brightness channel, a(x,y) is the red-green difference channel, b(x,y) is the yellow-blue difference channel, and x... r It is the normalized X value, y r It is the normalized Y value, z r It is the normalized Z-value;

[0089] Output Lab space three-channel matrix I Lab (x,y):

[0090] I Lab (x,y)=[L(x,y),a(x,y),b(x,y)]

[0091] Calculate the clustering window spacing S based on the preset number of regions K:

[0092]

[0093] In the formula, H and W are the height and width of the image, respectively;

[0094] Construct a two-dimensional coordinate matrix for each pixel (x, y) in the image:

[0095] X grid (x,y)=x

[0096] Y grid (x,y)=y

[0097] In the formula, X grid (x,y) and Y grid (x, y) are the original coordinates of each pixel in the image in the horizontal (column) and vertical (row) directions, respectively;

[0098] X grid (x,y) and Y grid (x,y) is normalized to a metric consistent with the color channels, i.e., divided by S:

[0099]

[0100] In the formula, x n (x,y) and y n (x,y) is the normalized result of the original spatial coordinates of the pixel;

[0101] For each pixel (x, y) in the image, construct the five-dimensional joint feature vector V required for SLIC clustering. x,y :

[0102]

[0103] V of all pixels x,y The feature combination is a five-dimensional tensor structure T V (x,y):

[0104]

[0105] Based on S, K cluster center points are initialized on the image by uniformly gridding. The initial position of each center point is the average of the five-dimensional vector of the pixels in the corresponding grid block.

[0106] For each cluster center, matching is performed only within its 2S×2S local window. The composite distance between the current pixel and the center is calculated (by calculating the Euclidean distance of the pixel in the CIELab perceptual color space and the two-dimensional Euclidean distance of the pixel in the image plane). The pixel (x,y) is assigned to the cluster center number with the smallest distance. The mean of the five-dimensional features of each region is recalculated as the new center. The moving distance m between the old center and the new center is calculated. If all m are less than the preset threshold β (set through experimental optimization), clustering is stopped, and the final pixel assignment mask matrix is ​​obtained, representing the final sub-region number to which each pixel is assigned. For each region corresponding to number i, 8-neighborhood connectivity analysis is performed to extract its main connected components and record their areas. For secondary connected components or outlier clusters with an area less than 20 pixels, the following merging is performed:

[0107] If adjacent area numbers are unique, merge them into the neighboring area number;

[0108] If adjacent areas have different numbers, they are merged into the neighboring area with the largest area.

[0109] The closed boundary point set of the main connected component is extracted using a contour tracing algorithm (OpenCV's findContours function), and then saved as a region vector mask. The centroid coordinates of the region are then calculated.

[0110]

[0111] In the formula, R i It is the total number of pixels in the region, x j and y j These are the two-dimensional positions of the j-th pixel within the region;

[0112] Hyperspectral reflectance scanning is performed on each region, polarization direction light intensity images are acquired simultaneously, Stokes vectors are calculated and polarization enhancement image is constructed. The centroid coordinates of each sub-region are extracted and set as the target sampling point. The three-axis electric displacement platform is controlled to align the hyperspectral imaging window with the coordinate center and the polarization camera imaging area is locked simultaneously to make it consistent with the hyperspectral sampling field of view, ensuring that two types of data are acquired simultaneously in the same region.

[0113] Start the linear scanning hyperspectral camera to perform pixel-by-pixel scanning of each sub-region to obtain a two-dimensional spectral matrix. Start the DoFP polarization camera to simultaneously acquire data of the current region and output images in four polarization directions. Calculate the Stokes vector components for each pixel (x, y).

[0114] S0(x,y)=I0(x,y)+I 90 (x,y)

[0115] S1(x,y)=I0(x,y)-I90 (x,y)

[0116] S2(x,y)=I 45 (x,y)-I 135 (x,y)

[0117] In the formula, I θ (x,y) represents the pixel intensity value acquired by the polarization camera, S0(x,y) represents the total light intensity (brightness) of the unpolarized light, S1(x,y) is the horizontal polarization component, and S2(x,y) is the diagonal polarization component.

[0118] The polarization enhancement image IS(x,y) of this region is calculated by combining the obtained Stokes vector components, which expresses the degree of polarization anomaly:

[0119]

[0120] In the formula, ∈ is a tiny positive number to avoid the denominator being zero;

[0121] Linearly normalize the image so that its pixel value range is mapped to [0,1].

[0122] Employing sRGB gamma inverse linearization and CIE Lab perceptual space transformation ensures that region division is based on human visual response, improving the consistency of cluster perception. Introducing five-dimensional feature vector joint modeling achieves color-space coupled division, superior to traditional segmentation schemes based on grayscale or RGB histograms. A hyperspectral and polarization synchronous acquisition mechanism enhances the measurement accuracy of regional physical properties, providing a data foundation for subsequent interferometric feature analysis and directional response modeling. The system has a clear structure and a highly automated acquisition process, facilitating practical deployment in camouflage material experimental verification platforms or quality inspection lines.

[0123] S3, Region Mask Generation Module, is used to apply the Otsu algorithm to adaptively segment the polarization reflection intensity map, and use morphological erosion and dilation operations to correct the boundary, generating a mask IE(x,y) for the target polarization reflection significant region.

[0124] Specifically, the Otsu algorithm is applied to adaptively segment the polarization reflection intensity map, and morphological erosion and dilation operations are used to correct the boundaries to generate a mask IE(x,y) for the significant polarization reflection region of the target, including:

[0125] Traverse the entire polarization reflection intensity map, record the minimum and maximum values, and perform a pixel-by-pixel linear transformation on the image to map it to the [0,255] grayscale space to obtain the normalized image pixel values;

[0126] Perform a difference calculation on the entire row of pixels for each image row:

[0127] Ediff (x,y)=(IS norm (x,y)-IS norm (x,y-1)) 2

[0128] In the formula, E diff (x,y) represents the intensity of the vertical brightness variation, IS norm (x,y) is the normalized brightness value of the image at coordinate point (x,y), IS norm (x, y-1) is the brightness of the pixel in the row above in the same column;

[0129] Apply the standard Sobel operator to the entire image to obtain the lateral gradient map. and vertical gradient plot And calculate the local edge structure intensity E of the image. edge (x,y):

[0130]

[0131] Construct a pixel energy function and calculate the pixel-level energy term E(x,y) by fusing brightness variation and edge suppression information:

[0132] E(x,y)=w1×E diff (x,y)+w2×E edge (x,y)

[0133] In the formula, w1 is the weight of the brightness difference term, and w2 is the weight of the Sobel edge term, which is set through experimental optimization.

[0134] The energy of all pixels is accumulated row by row to obtain the row energy integration. The row with the maximum energy y1 is searched as the structural boundary to divide the image into upper and lower sub-images. If the pixel in the y-th row is less than y1, it is the upper half of the image; otherwise, it is the lower half of the image.

[0135] The normalized image is subjected to median filtering to obtain the median map M(x,y). The median map is subjected to mean filtering to obtain the local average map G(x,y). Based on M(x,y) and G(x,y), a joint gray-level pair (i,j) for each pixel (x,y) is constructed. The gray-level pairs are assigned to the upper and lower halves of the image according to their respective regions. The frequency of all (i,j) in the two regions is counted to form a joint gray-level histogram.

[0136] Define a joint gray-level threshold combination (s,t), where s is a gray-level threshold candidate for the joint median map M(x,y), and t is a gray-level threshold candidate for the joint mean map G(x,y). The actual range is dynamically adjusted according to the image gray-level distribution. Each pair (s,t) represents the boundary that divides the value ranges of M(x,y) and G(x,y) into two sub-regions. In each histogram, (i,j) is divided into 4 regions according to (s,t), as follows:

[0137] Region A is defined as i≤s and j≤t, region B is defined as i>s and j≤t, region C is defined as i≤s and j>t, and region D is defined as i>s and j>t.

[0138] Calculate the sum of the relative frequencies of all gray-level pairs (i,j) belonging to region k in the joint gray-level histogram across the entire image:

[0139] ω k =∑p ij

[0140] In the formula, p ij ω is the probability of a gray-level pair (i,j). k It is the regional weight (joint probability sum);

[0141] Using the gray values ​​of the median filtered image M(x,y) as the main variables, the joint gray mean μ of each region is calculated. k and population mean μ T :

[0142]

[0143] μ T =∑i·p ij

[0144] In the formula, i and j represent the gray-level indices of the median and mean images in the joint gray-level histogram, respectively;

[0145] Based on the two-dimensional Otsu definition, a comprehensive scoring index for inter-class differences is calculated:

[0146]

[0147] In the formula, tr(σ) B ) is the weighted mean squared variance of the inter-class variances;

[0148] For each (s,t), calculate the inter-class variance trace values ​​of the upper and lower halves of the joint gray-level histogram, and multiply the two trace values ​​to obtain the joint scoring function Q(s,t):

[0149]

[0150] In the formula, and is These are the inter-class covariance matrices obtained by using the current threshold pair in the "upper subgraph" and "lower subgraph," respectively.

[0151] This score reflects the probability of achieving optimal segmentation in both regions simultaneously.

[0152] Traverse all scoring results and find the threshold combination with the highest score. This threshold pair is the optimal joint segmentation boundary of the upper and lower graphs.

[0153] In the upper and lower sub-images, traverse each pixel (x, y) to construct the upper and lower sub-image masks respectively:

[0154]

[0155] In the formula, and These are the pixel values ​​of the binary mask;

[0156] Merge the upper and lower region masks into a full-image mask M0(x,y):

[0157]

[0158] The mask image M0(x,y) is labeled with connected components using common connected component labeling algorithms (such as OpenCV's connectedComponents). Regions with an area smaller than a preset threshold (statistical analysis setting) are removed. Morphological closing and opening operations are then performed on the removed mask image to obtain the final polarization salient region mask image.

[0159] First, by normalizing the polarization reflection intensity map and calculating the brightness difference term, rapid detection of abrupt polarization brightness boundaries can be achieved. Combining the pixel energy term constructed using the Sobel gradient response avoids, to some extent, the over-segmentation or under-segmentation problems caused by relying solely on brightness. Furthermore, by calculating the peak energy row of the entire image using the row energy accumulation curve, the image can be automatically divided into upper and lower parts, and a joint gray-level histogram can be built on each sub-image. This not only achieves region-level optimized segmentation but also significantly reduces the redundant effects caused by texture inhomogeneity in global optimization. By modeling the inter-class variance of the four gray-level pairs (regions A, B, C, and D) in the joint histogram and introducing the trace product of the covariance matrix as a joint scoring index, this method achieves a global search for the optimal threshold combination in the median-mean joint two-dimensional space, solving the threshold offset problem of the traditional Otsu algorithm when processing low-contrast, blurred-boundary images. Finally, through the construction and merging of binary masks of the upper and lower regions, the system obtains a complete region mask map. This mask accurately marks the location and shape of highly polarized reflective regions, providing a precise spatial reference for subsequent interference fringe analysis and BRDF angular response modeling. Particularly noteworthy is that subsequent connected component analysis and area filtering operations (such as using the `connectedComponents` function in OpenCV) effectively remove noisy regions and isolated pixel clusters. Combined with morphological opening and closing operations, this further enhances the geometric consistency and usability of the mask image.

[0160] S4, Interference Scoring Module, is used to select hyperspectral reflectance curves within the IE(x,y) range, and to use Fast Fourier Transform to detect periodic fringe fluctuations and calculate the interference score Ir(i);

[0161] Specifically, hyperspectral reflectance curves within the IE(x,y) range are selected. Fast Fourier Transform is used to detect periodic fringe fluctuations and calculate the interference score Ir(i). This involves traversing each sub-region numbered i. If the corresponding sample is a structural color material, the pixels marked by the IE(x,y) mask within that region are filtered, retaining only the spectral data from the "significantly polarized enhanced region." The hyperspectral reflectance curves at all mask points within that region are then averaged to obtain a representative reflectance curve. Curve for the i-th region Perform normalization processing, and apply a one-dimensional fast Fourier transform operation to the normalized curve to obtain the amplitude spectrum value F at the frequency index k. i (k), to quantify the intensity of the interference fringes, the average energy of the high-frequency band (excluding DC and low-frequency components) is calculated as the interference score Ir(i) for this region:

[0162]

[0163] In the formula, H is the total number of frequency points in the integration interval, k is the frequency index in the Fourier transform, k1 is the starting point of the frequency integration, and k2 is the ending point of the frequency integration.

[0164] The higher the score, the stronger the high-frequency interference component in the reflection curve and the more significant the potential unnatural structure.

[0165] The Ir(i) of all structural color regions are combined into an interference scoring matrix.

[0166] This solution introduces FFT spectral analysis to directly quantify high-frequency energy terms, effectively distinguishing camouflage materials that "possess interference characteristics but have an indistinct spectral modulus." Existing methods for determining microscopic interference structures typically rely on contact-based equipment such as scanning electron microscopes or white-light interferometers, which are unsuitable for rapid screening. This system design, however, only requires acquiring reflectance curves on the material surface to achieve interference behavior assessment based on spectral data, demonstrating strong practicality and engineering deployment value. Ir(i), as a quantitative output index of high-frequency fringe intensity, can directly participate in the calculation of the comprehensive camouflage performance scoring function. A higher Ir(i) value indicates stronger interference and artificial structure indications in the reflectance curve of that region, serving as an important quantitative basis for identifying "optical exposure hotspots."

[0167] S5, Multi-angle BRDF measurement module, is used to change the observation angle of the sample, perform multi-angle spectral acquisition on the mask area, establish the BRDF response function, and calculate the directional deviation ΔR(i);

[0168] Specifically, by changing the sample observation angle, multi-angle spectral acquisition is performed on the mask area, a BRDF response function is established, and the directional deviation ΔR(i) is calculated. The three-axis servo motor platform is controlled to adjust the sample attitude to fix the incident light source. In this embodiment, the detector receiving angles are set to 0°, 30°, 60°, and 80°. After each angle setting, the hyperspectral imaging module is used to acquire hyperspectral reflectance data of each sub-region within the mask area IE(x,y) while maintaining standard illumination. For each sub-region i, at each angle θ... k At this point, calculate its BRDF function value f. r,i (θ k ,λ):

[0169]

[0170] In the formula, R(θ) k ,λ) is the material at angle θ k Reflectance at θ, cos(θ) k ) is the angular projection coefficient;

[0171] Then, averaging is performed along the wavelength dimension to obtain the band-averaged BRDF response for the angle-region.

[0172]

[0173] In the formula, M is the number of sampling bands, and λ j It is the j-th band;

[0174] For each sub-region i, the average BRDF response at all angles is taken as the mean.

[0175]

[0176] Calculate the directional consistency deviation ΔR(i) of sub-region i:

[0177]

[0178] In the formula, It is the i-th subregion at angle θ k The average BRDF response under the following conditions ΔR(i) is the average BRDF value across all angles, and ΔR(i) is the maximum deviation of the region at different angles. It is the inverse index of directional camouflage consistency. The larger the value, the worse the reflectivity consistency and the weaker the camouflage effect.

[0179] Traditional reflection detection typically uses normal or near-normal angles, failing to capture the high-contrast reflection issues of materials at oblique viewing angles. This invention introduces a multi-angle difference analysis mechanism through ΔR(i), for the first time integrating "directional exposure risk" as a quantitative indicator into a camouflage performance evaluation system. Many novel camouflage materials exhibit non-ideal microstructures on their surfaces (such as semi-regular particles and micro-textured coatings). These materials may reflect well at small angles, but are prone to specular reflection at large angles. ΔR(i) can effectively capture this type of directional reflection risk. The ΔR(i) index can not only be used for material quality assessment but also assist in battlefield deployment strategies, such as suggesting that a certain material should only be used in specific angle camouflage areas under specific backgrounds to improve camouflage efficiency.

[0180] S6, Scoring Module, is used to comprehensively score camouflage performance based on intensity index Ir(i) and directional deviation ΔR(i), generate a two-dimensional heat map based on the camouflage performance score, display the camouflage exposure risk level and automatically generate a detection report;

[0181] Specifically, a comprehensive camouflage performance score is calculated based on the intensity index Ir(i) and the directional deviation ΔR(i). A two-dimensional heatmap is generated based on the camouflage performance score to display the camouflage exposure risk level and automatically generate a test report. For each sub-region i, the interference score value Ir(i) and the directional deviation score ΔR(i) are linearly weighted to construct a comprehensive optical camouflage performance score Score(i). The score results of all sub-regions are standardized and normalized, mapping the maximum score to 1 and the minimum score to 0 to form a normalized score matrix. Each sub-region uses its region boundary as a primitive, and its fill color (0: green, 1: red) is controlled by the normalized score matrix value. The final graphic is automatically drawn using OpenCV, and the original material image is superimposed to form a transparent layer heatmap. A test report is generated based on the test data, including basic sample information, material surface structure image and structural color judgment results, standard illuminance parameters and reflectivity response curve, polarization diagram (IS diagram) and extracted region mask image, interference score, directional deviation and comprehensive score corresponding to each region.

[0182] The combination of interference scoring and directional bias allows the scoring system to span both microscopic structural optical effects and macroscopic reflectivity consistency, exhibiting high complementarity. Structural color interference reflects manufacturing defects or design weaknesses at the microscopic level of the material, while multi-angle consistency reflects the stability of the macroscopic morphology and coating under the viewing angle. Together, they determine the material's identifiability under complex natural conditions. The scoring mechanism of this invention is based on explicit physical quantity calculations, and the linear weighted structure avoids model overfitting or fuzzy judgments, adapting to the differences in emphasis on structural interference or directional consistency in different scenarios.

[0183] This embodiment also provides a method for detecting the optical properties of camouflage materials, including:

[0184] Surface images of camouflage material samples are acquired, RGB imaging is performed based on standard illumination and sub-regions are automatically divided, hyperspectral reflectance is scanned in each region, polarization direction light intensity images are acquired simultaneously, Stokes vectors are calculated and polarization-enhanced images are constructed.

[0185] Adaptively segment and correct the boundaries of the polarization reflection intensity map to generate a mask IE(x,y) for the significant polarization reflection region of the target.

[0186] Hyperspectral reflectance curves within the IE(x,y) range were selected, periodic fringe fluctuations were detected and interference scores Ir(i) were calculated. The observation angle of the sample was changed, and multi-angle spectral acquisition was performed on the mask area. The BRDF response function was established, and the directional deviation ΔR(i) was calculated.

[0187] The camouflage performance is comprehensively scored based on the intensity index Ir(i) and the directional deviation ΔR(i). A two-dimensional heat map is generated based on the camouflage performance score to show the camouflage exposure risk level and an automatic detection report is generated.

[0188] This embodiment also provides a computer device applicable to the optical performance testing method for camouflage materials, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the optical performance testing method for camouflage materials as proposed in the above embodiment.

[0189] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0190] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements the optical performance detection method for camouflage materials as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0191] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A system for detecting the optical properties of camouflage materials, characterized in that: include, The structure recognition and illumination control module is used to acquire surface images of camouflage material samples, identify the sample surface structure, activate a full-spectrum light source, simulate gradual illumination from weak to strong light, and perform RGB imaging based on standard illuminance, including: High-resolution grayscale images of the sample surface are acquired, and a two-dimensional fast Fourier transform is performed on the images to obtain a spectrum. The spectrum amplitude is calculated and normalized before the main frequency corresponding to the direction of maximum amplitude in the spectrum is extracted. If the main frequency is greater than the preset threshold U, the energy proportion of the main frequency is further evaluated. If the energy proportion of the main frequency is greater than the preset threshold u, the region is considered to have the possibility of artificial structure color interference. Activate the controllable full-spectrum LED array light source and set the light intensity to five fixed levels, including I1 to I5; At each illuminance level, the reflectance spectrum curves of the material sample surface were collected, and the normalized reflectance R(λ) was calculated based on the collected data. j ,I i For all wavelengths under the i-th illuminance, the reflectance R(λ,I) i ), calculate its mean and the standard deviation of reflectance under that illuminance The five sets of standard deviation results are compared, and the illuminance level number corresponding to the smallest one is selected as the standard illuminance. Based on the determined standard illuminance, the industrial camera system is started to acquire sample images and obtain three-channel RGB images. The region segmentation module applies a superpixel segmentation algorithm to the image, automatically dividing it into sub-regions. For each region, a hyperspectral reflectance scan is performed, and polarization direction light intensity images are acquired simultaneously. The Stokes vector is calculated, and a polarization-enhanced image is constructed. This includes: The RGB image matrix after linearization of light intensity is obtained by processing each component of each pixel individually according to the standard sRGB gamma inverse correction. For each pixel, the linear R, G, B values ​​are converted into X, Y, Z channel values. The XYZ components of each pixel are normalized. A perceptual mapping function f(p) is defined to establish a color-luminance nonlinear response. The Lab channel value of each pixel is calculated, and the Lab space three-channel matrix is ​​output. ; The clustering window spacing S is calculated based on the preset number of regions K, and a two-dimensional spatial coordinate matrix is ​​constructed for each pixel (x,y) in the image. For each pixel (x, y) in the image, construct the five-dimensional joint feature vector required for SLIC clustering. ; All pixels The feature combination forms a five-dimensional tensor structure. ; Based on S, K cluster center points are initialized in a uniform grid on the image. For each cluster center, the composite distance between the current pixel and the center is calculated. The pixel (x,y) is assigned to the cluster center number with the smallest distance. The mean of the five-dimensional features of each region is recalculated as the new center. The moving distance m between the old center and the new center is calculated. If all m are less than the preset threshold β, the clustering is stopped, and the final pixel assignment mask matrix is ​​obtained. For each region corresponding to number i, 8-neighborhood connectivity analysis is performed. The closed boundary point set of the main connected component is extracted using a contour tracing algorithm and saved as a region vector mask. Then, the centroid coordinates of the region are calculated. ; Extract the centroid coordinates of each sub-region and set them as the target sampling point. Perform pixel-by-pixel scanning on each sub-region to obtain a two-dimensional spectral matrix. Start the DoFP polarization camera to output images in four polarization directions. Calculate the Stokes vector components for each pixel (x,y). The polarization enhancement image of this region is calculated by combining the obtained Stokes vector components. ; The region mask generation module is used to adaptively segment the polarization reflection intensity map using the Otsu algorithm, correct the boundaries using morphological erosion and dilation operations, and generate a salient polarization reflection region mask IE(x,y) for the target, including: Traverse the entire polarization reflection intensity map and normalize it to obtain the normalized image pixel values; Perform a difference calculation on the entire row of pixels for each image row; The standard Sobel operator is applied to the entire image to obtain the horizontal gradient map and the vertical gradient map, and the local edge structure intensity of the image is calculated. A pixel energy function is constructed, and the pixel-level energy term E(x,y) is calculated by fusing brightness change and edge suppression information. The energy of all pixels is accumulated row by row to obtain the row energy integration. The row with the maximum energy y1 is searched as the structural boundary to divide the image into upper and lower sub-images. If the pixel in the y-th row is less than y1, it is the upper half of the image; otherwise, it is the lower half of the image. The normalized image is subjected to median filtering to obtain the median map M(x,y). The median map is subjected to mean filtering to obtain the local average map G(x,y). Based on M(x,y) and G(x,y), a joint gray-level pair (i,j) for each pixel (x,y) is constructed. The gray-level pairs are assigned to the upper and lower halves of the image according to their respective regions. The frequency of all (i,j) in the two regions is counted to form a joint gray-level histogram. Define a joint grayscale threshold combination (s,t) and divide (i,j) into 4 regions according to (s,t) in each histogram; Calculate the sum of the relative frequencies of all gray-level pairs (i,j) belonging to region k in the entire image from the joint gray-level histogram. Using the gray values ​​of the median-filtered image M(x,y) as the main variable, calculate the joint gray-level mean for each region. and population mean According to the two-dimensional Otsu definition, a comprehensive scoring index for inter-class differences is calculated. For each (s,t), the inter-class variance trace values ​​of the upper and lower halves of the joint gray-level histogram are calculated. The two trace values ​​are multiplied to obtain the joint scoring function. ; Iterate through all rating results and find the threshold combination with the highest rating; In the upper and lower sub-images, each pixel (x, y) is traversed separately to construct the upper and lower sub-image masks. The upper and lower region masks are then merged into a full-image mask. ; For mask image Connected regions are marked, regions with an area smaller than a preset threshold are removed, and morphological closing and opening operations are performed on the removed mask to obtain the final polarization salient region mask. The interference scoring module is used to select hyperspectral reflectance curves within the IE(x,y) range, and to use fast Fourier transform to detect periodic fringe fluctuations and calculate the interference score Ir(i). The multi-angle BRDF measurement module is used to change the observation angle of the sample, perform multi-angle spectral acquisition on the mask area, establish the BRDF response function, and calculate the directional deviation ∆R(i). The scoring module is used to comprehensively score the camouflage performance based on the intensity index Ir(i) and the directional deviation ∆R(i). It generates a two-dimensional heat map based on the camouflage performance score, displays the camouflage exposure risk level, and automatically generates a detection report.

2. The optical performance testing system for camouflage materials as described in claim 1, characterized in that: The process involves selecting hyperspectral reflectance curves within the IE(x,y) range, using Fast Fourier Transform to detect periodic fringe fluctuations, and calculating the interference score Ir(i). This involves traversing each sub-region numbered i. If the corresponding sample is a structural color material, the pixels marked by the IE(x,y) mask within that region are filtered, retaining only the spectral data from regions with significant polarization enhancement. The hyperspectral reflectance curves at all mask points within that region are then averaged to obtain a representative reflectance curve. The amplitude spectrum value at frequency index k is obtained by applying a one-dimensional fast Fourier transform operation. The average energy value in the high-frequency band is calculated as the interferometric score Ir(i) for the region.

3. The optical performance testing system for camouflage materials as described in claim 2, characterized in that: The process involves changing the sample observation angle, performing multi-angle spectral acquisition on the masked area, establishing a BRDF response function, and calculating the directional deviation ∆R(i). This involves controlling a three-axis servo motor platform to adjust the sample attitude, fixing the incident light source. After each angle setting, a hyperspectral imaging module is used to acquire hyperspectral reflectance data for each sub-region within the masked area IE(x, y) while maintaining standard illumination. For each sub-region i, at each angle... Calculate the BRDF function value at this location. ; Averaging is performed along the wavelength dimension to obtain the band-averaged BRDF response for the angle-region. ; For each sub-region i, the average BRDF response at all angles is taken as the mean. ; based on and Calculate the directional consistency deviation ∆R(i) of sub-region i.

4. The optical performance testing system for camouflage materials as described in claim 3, characterized in that: The method involves comprehensively scoring camouflage performance based on intensity index Ir(i) and directional deviation ΔR(i), generating a two-dimensional heatmap based on the camouflage performance score, displaying the camouflage exposure risk level, and automatically generating a detection report. Specifically, the interference score Ir(i) and directional deviation score ΔR(i) of each sub-region i are linearly weighted to construct a comprehensive optical camouflage performance score Score(i). The maximum score is mapped to 1, and the minimum score is mapped to 0, forming a normalized score matrix. Each sub-region uses its boundary as a primitive, and the normalized score matrix value is used to fill the color to form a transparent layer heatmap. A detection report is then generated based on the detection data.

5. A method for detecting the optical properties of camouflage materials, based on the optical property detection system for camouflage materials according to any one of claims 1 to 4, characterized in that: include, Surface images of camouflage material samples are acquired, RGB imaging is performed based on standard illumination and sub-regions are automatically divided, hyperspectral reflectance is scanned in each region, polarization direction light intensity images are acquired simultaneously, Stokes vectors are calculated and polarization-enhanced images are constructed. Adaptively segment and correct the boundaries of the polarization reflection intensity map to generate a mask IE(x,y) for the significant polarization reflection region of the target. Hyperspectral reflectance curves within the IE(x,y) range were selected, periodic fringe fluctuations were detected and interference scores Ir(i) were calculated. The observation angle of the sample was changed, and multi-angle spectral acquisition was performed on the mask area. The BRDF response function was established and the directional deviation ∆R(i) was calculated. The camouflage performance is comprehensively scored based on the intensity index Ir(i) and the directional deviation ∆R(i). A two-dimensional heat map is generated based on the camouflage performance score to show the camouflage exposure risk level and an automatic detection report is generated.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the optical performance detection method for camouflage materials as described in claim 5.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the optical performance detection method for camouflage materials as described in claim 5.

Images