Accurate segmentation of camouflage objects in aerial imagery is vital for improving the efficiency of UAV-based reconnaissance and rescue missions.However,camouflage object segmentation is increasingly challenging du...Accurate segmentation of camouflage objects in aerial imagery is vital for improving the efficiency of UAV-based reconnaissance and rescue missions.However,camouflage object segmentation is increasingly challenging due to advances in both camouflage materials and biological mimicry.Although multispectral-RGB based technology shows promise,conventional dual-aperture multispectral-RGB imaging systems are constrained by imprecise and time-consuming registration and fusion across different modalities,limiting their performance.Here,we propose the Reconstructed Multispectral-RGB Fusion Network(RMRF-Net),which reconstructs RGB images into multispectral ones,enabling efficient multimodal segmentation using only an RGB camera.Specifically,RMRF-Net employs a divergentsimilarity feature correction strategy to minimize reconstruction errors and includes an efficient boundary-aware decoder to enhance object contours.Notably,we establish the first real-world aerial multispectral-RGB semantic segmentation of camouflage objects dataset,including 11 object categories.Experimental results demonstrate that RMRF-Net outperforms existing methods,achieving 17.38 FPS on the NVIDIA Jetson AGX Orin,with only a 0.96%drop in mIoU compared to the RTX 3090,showing its practical applicability in multimodal remote sensing.展开更多
Camouflaged people are extremely expert in actively concealing themselves by effectively utilizing cover and the surrounding environment. Despite advancements in optical detection capabilities through imaging systems,...Camouflaged people are extremely expert in actively concealing themselves by effectively utilizing cover and the surrounding environment. Despite advancements in optical detection capabilities through imaging systems, including spectral, polarization, and infrared technologies, there is still a lack of effective real-time method for accurately detecting small-size and high-efficient camouflaged people in complex real-world scenes. Here, this study proposes a snapshot multispectral image-based camouflaged detection model, multispectral YOLO(MS-YOLO), which utilizes the SPD-Conv and Sim AM modules to effectively represent targets and suppress background interference by exploiting the spatial-spectral target information. Besides, the study constructs the first real-shot multispectral camouflaged people dataset(MSCPD), which encompasses diverse scenes, target scales, and attitudes. To minimize information redundancy, MS-YOLO selects an optimal subset of 12 bands with strong feature representation and minimal inter-band correlation as input. Through experiments on the MSCPD, MS-YOLO achieves a mean Average Precision of 94.31% and real-time detection at 65 frames per second, which confirms the effectiveness and efficiency of our method in detecting camouflaged people in various typical desert and forest scenes. Our approach offers valuable support to improve the perception capabilities of unmanned aerial vehicles in detecting enemy forces and rescuing personnel in battlefield.展开更多
Brain functional networks model the brain's ability to exchange information across different regions,aiding in the understanding of the cognitive process of human visual attention during target searching,thereby c...Brain functional networks model the brain's ability to exchange information across different regions,aiding in the understanding of the cognitive process of human visual attention during target searching,thereby contributing to the advancement of camouflage evaluation.In this study,images with various camouflage effects were presented to observers to generate electroencephalography(EEG)signals,which were then used to construct a brain functional network.The topological parameters of the network were subsequently extracted and input into a machine learning model for training.The results indicate that most of the classifiers achieved accuracy rates exceeding 70%.Specifically,the Logistic algorithm achieved an accuracy of 81.67%.Therefore,it is possible to predict target camouflage effectiveness with high accuracy without the need to calculate discovery probability.The proposed method fully considers the aspects of human visual and cognitive processes,overcomes the subjectivity of human interpretation,and achieves stable and reliable accuracy.展开更多
The object detectors can precisely detect the camouflaged object beyond human perception.The investigations reveal that the CNNs-based(Convolution Neural Networks)detectors are vulnerable to adversarial attacks.Some w...The object detectors can precisely detect the camouflaged object beyond human perception.The investigations reveal that the CNNs-based(Convolution Neural Networks)detectors are vulnerable to adversarial attacks.Some works can fool detectors by crafting the adversarial camouflage attached to the object,leading to wrong prediction.It is hard for military operations to utilize the existing adversarial camouflage due to its conspicuous appearance.Motivated by this,this paper proposes the Dual Attribute Adversarial Camouflage(DAAC)for evading the detection by both detectors and humans.Generating DAAC includes two steps:(1)Extracting features from a specific type of scene to generate individual soldier digital camouflage;(2)Attaching the adversarial patch with scene features constraint to the individual soldier digital camouflage to generate the adversarial attribute of DAAC.The visual effects of the individual soldier digital camouflage and the adversarial patch will be improved after integrating with the scene features.Experiment results show that objects camouflaged by DAAC are well integrated with background and achieve visual concealment while remaining effective in fooling object detectors,thus evading the detections by both detectors and humans in the digital domain.This work can serve as the reference for crafting the adversarial camouflage in the physical world.展开更多
In order to solve the problem of poor fusion between the spots of deformation camouflage and the background,a small-spot deformation camouflage design algorithm based on background texture matching is proposed in this...In order to solve the problem of poor fusion between the spots of deformation camouflage and the background,a small-spot deformation camouflage design algorithm based on background texture matching is proposed in this research.The combination of spots and textures improved the fusion of the spot pattern and the background.An adversarial autoencoder convolutional network was designed to extract background texture features.The image adversarial loss was added and the reconstruction loss was improved to improve the clarity of the generated texture pattern and the generalization ability of the model.The digital camouflage was formed by obtaining the mean value of the square area and replacing the main color.At the same time,the spots in the square area with a side length of 2 s were subjected to simple linear iterative clustering to form irregular small-spot camouflage.A dataset with a scale of 1050 was established in the experiment.The training results of three different loss functions were investigated.The results showed that the proposed loss function could enhance the generalization of the model and improve the quality of the generated texture image.A variety of digital camouflages with main colors and irregular small-spot camouflage were generated,and their efficiency was tested.On the one hand,intuitive evaluation was given by personnel observing the camouflage pattern embedded in the background and its contour map calculated by the canny operator.On the other hand,objective comparison result was formed by calculating the 4 evaluation indexes between the camouflage pattern and the background.Both results showed that the generated pattern had a high degree of fusion with the background.This model could balance the relationship between the spot size,the number of main colors and the actual effect according to actual needs.展开更多
The method of describing deformation camouflage spots based on feature space has some shortcomings,such as inaccurate description and difficult reproduction.Depending on the strong fitting ability of the generative ad...The method of describing deformation camouflage spots based on feature space has some shortcomings,such as inaccurate description and difficult reproduction.Depending on the strong fitting ability of the generative adversarial network model,the distribution of deformation camouflage spot pattern can be directly fitted,thus simplifying the process of spot extraction and reproduction.The requirements of background spot extraction are analyzed theoretically.The calculation formula of limiting the range of image spot pixels is given and two kinds of spot data sets,forestland and snowfield,are established.Spot feature is decomposed into shape,size and color features,and a GAN(Generative Adversarial Network)framework is established.The effects of different loss functions on network training results are analyzed in the experiment.In the meantime,when the input dimension of generator network is 128,the balance between sample diversity and quality can be achieved.The effects of sample generation are investigated in two aspects.Subjectively,the probability of the generated spots being distinguished in the background is counted,and the results are all less than 20% and mostly close to zero.Objectively,the features of the spot shape are calculated and the independent sample T-test is applied to verify that the features are from the same distribution,and all the P-Values are much higher than 0.05.Both subjective and objective methods prove that the spots generated by this method are similar to the background spots.The proposed method can directly generate the desired camouflage pattern spots,which provides a new technical method for the deformation camouflage pattern design and camouflage effect evaluation.展开更多
The evaluation index of camouflage patterns is important in the field of military application.It is the goal that researchers have always pursued to make the computable evaluation indicators more in line with the huma...The evaluation index of camouflage patterns is important in the field of military application.It is the goal that researchers have always pursued to make the computable evaluation indicators more in line with the human visual mechanism.In order to make the evaluation method more computationally intelligent,a Multi-Feature Camouflage Fused Index(MF-CFI)is proposed based on the comparison of grayscale,color and texture features between the target and the background.In order to verify the effectiveness of the proposed index,eye movement experiments are conducted to compare the proposed index with existing indexes including Universal Image Quality Index(UIQI),Camouflage Similarity Index(CSI)and Structural Similarity(SSIM).Twenty-four different simulated targets are designed in a grassland background,28 observers participate in the experiment and record the eye movement data during the observation process.The results show that the highest Pearson correlation coefficient is observed between MF-CFI and the eye movement data,both in the designed digital camouflage patterns and largespot camouflage patterns.Since MF-CFI is more in line with the detection law of camouflage targets in human visual perception,the proposed index can be used for the comparison and parameter optimization of camouflage design algorithms.展开更多
The temperature difference between the exposed surface of an underground silo and the surrounding soil surface is significant,which means a silo can be easily found by infrared detection.We designed an infrared camouf...The temperature difference between the exposed surface of an underground silo and the surrounding soil surface is significant,which means a silo can be easily found by infrared detection.We designed an infrared camouflage cloak consisting of an imitative layer and an insulation layer for the silos.The imitative layer is used to imitate the thermal response of the soil to the surrounding environment.The insulation layer is used to weaken the impact of the internal temperature field of the silo on the lower boundary of the imitative layer.A silo model including surrounding soil and a soil model without silo were established,and the influences of the material and thickness of each layer on the infrared camouflage effect were analyzed.The results show that when using a silicone rubber containing alumina powder with a volume fraction of 3.18% as the imitative material,its thermal inertia is in consistent with that of the soil.Meanwhile,it was found that the thickness of the imitative layer doesn't need to be greater than its thermal penetration depth to achieve the infrared camouflage,and the absence of the insulation layer will cause hot spots on the silo surface in winter to weaken the camouflage effect.The optimized thicknesses of the imitative layer and the insulation layer are 22 cm and 4 cm re spectively.The simulations indicate that with the application of the cloak,the maximum value of the absolute values of the temperature differences between the average temperatures of the silo surface and the surrounding soil surface temperatures drops from 1,59℃ to 0.31℃ in summer and from 1.92℃ to 0.21℃ in winter.This designed cloak can achieve an all-weather and full-time passive infrared camouflage.展开更多
To address the problems of missing inside and incomplete edge contours in camouflaged target detection results,we propose a camouflaged moving target detection algorithm based on local minimum difference constraints(L...To address the problems of missing inside and incomplete edge contours in camouflaged target detection results,we propose a camouflaged moving target detection algorithm based on local minimum difference constraints(LMDC).The algorithm first uses the mean to optimize the initial background model,removes the stable background region by global comparison,and extracts the edge point set in the potential target region so that each boundary point(seed)grows along the center of the target.Finally,we define the minor difference constraints term,combine the seed path and the target space consistency,and calculate the attributes of each pixel in the potential target area to realize camouflaged moving target detection.The algorithm of this paper is verified based on a public data sofa video and test videos and compared with the five classic algorithms.The experimental results show that the proposed algorithm yields good results based on integrity,accuracy,and a number of objective evaluation indexes,and its overall performance is better than that of the compared algorithms.展开更多
The human visual system is still an important factor in military warfare;military personnel receive training on effective search strategies,and camouflage that can effectively conceal objects and personnel is a key co...The human visual system is still an important factor in military warfare;military personnel receive training on effective search strategies,and camouflage that can effectively conceal objects and personnel is a key component of a successful integrated survivability strategy.Previous methods of camouflage assessment have,amongst others,used psychophysics to generate distinctiveness metrics.However,the population from which the human observers are drawn is often not well defined,or necessarily appropriate.In this experiment we designed a new platform for testing multiple patterns based on a camouflaged object detection task,and investigate whether trained military observers perform better than civilians.We use a two-alternative forced choice paradigm,with participants searching images of woodland for a replica military helmet displaying Olive Green,Multi Terrain Pattern,US Marine Pattern or,as a conspicuous control,UN Peacekeeper Blue.Our data show that there is no difference in detection performance between the two observer groups but that there are clear differences in the effectiveness of the different helmet colour patterns in a temperate woodland environment.We conclude that when tasks involve very short stimulus presentation times,task-specific training has little effect on the success of target detection and thus this paradigm is particularly suitable for robust estimates of camouflage efficacy.展开更多
In order to examine the possibility to improve its camouflage properties standard cotton fabric with camouflage print was impregnated with poly(vinyl butyral),PVB and fullerene-like nanoparticles of tungsten disulfide...In order to examine the possibility to improve its camouflage properties standard cotton fabric with camouflage print was impregnated with poly(vinyl butyral),PVB and fullerene-like nanoparticles of tungsten disulfide,PVB/IF-WS_(2).FTIR analysis excluded any possible chemical interaction of IF-WS_(2) with PVB and the fabric.The camouflage behavior of the impregnated fabric has been examined firstly in the VIS part of the spectrum.Diffuse reflection,specular gloss and color coordinates were measured for three different shades(black,brown and dark green).Thermal imaging was applied to examine the camouflage abilities of this impregnation in IR part of the spectrum.The obtained results show that PVB/IF-WS_(2) impregnation system induced enhacement of the materials camouflage properties,i.e.that IF-WS_(2) have a positive effect on spectrophotometric characteristics of the fabric.展开更多
This paper reports an alternative approach to the evaluation of infrared camouflage effectiveness via a multi-fractal method. By calculating multi-fractal spectra of the target region and the background regions in an ...This paper reports an alternative approach to the evaluation of infrared camouflage effectiveness via a multi-fractal method. By calculating multi-fractal spectra of the target region and the background regions in an infrared image, the spectrum shape features and the discrete Frechet distances among these spectra were used to analyze the camouflage effectiveness of the target qualitatively and quantitatively,and the correlation coefficients of the spectra were further used as the index of camouflage effectiveness.It was found that the camouflaged target had better camouflage effectiveness than the target without camouflage in the same one background, and the same one camouflaged target had different camouflage effectiveness in different backgrounds. On the whole, the target matching well with its background had high camouflage effectiveness value. This approach can expand the application of multi-fractal theory in infrared camouflage technology, which should be useful for the research of infrared camouflage materials, the design of camouflage patterns as well as the deployment of military equipment in battlefield.展开更多
This paper proposes a cooperative guidance law for attacking a ground target with the impact angle constraint based on the motion camouflage strategy in the line-of-sight(LOS)frame.A dynamic model with the impact angl...This paper proposes a cooperative guidance law for attacking a ground target with the impact angle constraint based on the motion camouflage strategy in the line-of-sight(LOS)frame.A dynamic model with the impact angle constraint is established according to the relative motion between multiple missiles and the target.The process of cooperative guidance law design is divided into two stages.Firstly,based on the undirected graph theory,a new finite-time consensus protocol on the LOS direction is derived to guarantee relative distances reach consensus.And the value of acceleration command is positive,which is beneficial for engineering realization.Secondly,the acceleration command on the normal direction of the LOS is designed based on motion camouflage and finite-time convergence,which can ensure the missiles reach the target with the desired angle and satisfy the motion camouflage state.The finitetime stability analysis is proved by the Lyapunov theory.Numerical simulations for stationary and maneuver targets have demonstrated the effectiveness of the cooperative guidance law proposed.展开更多
A quick and accurate extraction of dominant colors of background images is the basis of adaptive camouflage design.This paper proposes a Color Image Quick Fuzzy C-Means(CIQFCM)clustering algorithm based on clustering ...A quick and accurate extraction of dominant colors of background images is the basis of adaptive camouflage design.This paper proposes a Color Image Quick Fuzzy C-Means(CIQFCM)clustering algorithm based on clustering spatial mapping.First,the clustering sample space was mapped from the image pixels to the quantized color space,and several methods were adopted to compress the amount of clustering samples.Then,an improved pedigree clustering algorithm was applied to obtain the initial class centers.Finally,CIQFCM clustering algorithm was used for quick extraction of dominant colors of background image.After theoretical analysis of the effect and efficiency of the CIQFCM algorithm,several experiments were carried out to discuss the selection of proper quantization intervals and to verify the effect and efficiency of the CIQFCM algorithm.The results indicated that the value of quantization intervals should be set to 4,and the proposed algorithm could improve the clustering efficiency while maintaining the clustering effect.In addition,as the image size increased from 128×128 to 1024×1024,the efficiency improvement of CIQFCM algorithm was increased from 6.44 times to 36.42 times,which demonstrated the significant advantage of CIQFCM algorithm in dominant colors extraction of large-size images.展开更多
The paper explores the possibilities of using carbonyl iron in the form of a powder for the manufacture of radar-absorbing paints-reducing the radar signature of the objects that they cover.The attenuation values in t...The paper explores the possibilities of using carbonyl iron in the form of a powder for the manufacture of radar-absorbing paints-reducing the radar signature of the objects that they cover.The attenuation values in the range of 4-18 GHz for various coating thicknesses,ranging from 0.5 to 2.00 mm with 0.5 mm increment,and for different absorber content-75%and 80%,as well as the use of two different binders in the form of epoxy resins with hardeners,were investigated.For the frequency of 18 GHz and a 1.5 mm thick coating with a 75%absorber content,Epidian 112 resin and Saduramid 10/50 hardener used as a binder,and the maximum attenuation level obtained equalled 20.2 d B at 16 GHz.Additionally,the absorber particle size ranging from 3 to 4μm and its higher mass content resulted in achieving the reflection loss above-12 d B in the entire 8-12.5 GHz range for layers between 1-and 1.5 mm thickness.The qualitative assessment of the tested samples in the context of camouflage in the radar range was also performed,using statistical analysis.展开更多
基金National Natural Science Foundation of China(Grant Nos.62005049 and 62072110)Natural Science Foundation of Fujian Province(Grant No.2020J01451).
文摘Accurate segmentation of camouflage objects in aerial imagery is vital for improving the efficiency of UAV-based reconnaissance and rescue missions.However,camouflage object segmentation is increasingly challenging due to advances in both camouflage materials and biological mimicry.Although multispectral-RGB based technology shows promise,conventional dual-aperture multispectral-RGB imaging systems are constrained by imprecise and time-consuming registration and fusion across different modalities,limiting their performance.Here,we propose the Reconstructed Multispectral-RGB Fusion Network(RMRF-Net),which reconstructs RGB images into multispectral ones,enabling efficient multimodal segmentation using only an RGB camera.Specifically,RMRF-Net employs a divergentsimilarity feature correction strategy to minimize reconstruction errors and includes an efficient boundary-aware decoder to enhance object contours.Notably,we establish the first real-world aerial multispectral-RGB semantic segmentation of camouflage objects dataset,including 11 object categories.Experimental results demonstrate that RMRF-Net outperforms existing methods,achieving 17.38 FPS on the NVIDIA Jetson AGX Orin,with only a 0.96%drop in mIoU compared to the RTX 3090,showing its practical applicability in multimodal remote sensing.
基金support by the National Natural Science Foundation of China (Grant No. 62005049)Natural Science Foundation of Fujian Province (Grant Nos. 2020J01451, 2022J05113)Education and Scientific Research Program for Young and Middleaged Teachers in Fujian Province (Grant No. JAT210035)。
文摘Camouflaged people are extremely expert in actively concealing themselves by effectively utilizing cover and the surrounding environment. Despite advancements in optical detection capabilities through imaging systems, including spectral, polarization, and infrared technologies, there is still a lack of effective real-time method for accurately detecting small-size and high-efficient camouflaged people in complex real-world scenes. Here, this study proposes a snapshot multispectral image-based camouflaged detection model, multispectral YOLO(MS-YOLO), which utilizes the SPD-Conv and Sim AM modules to effectively represent targets and suppress background interference by exploiting the spatial-spectral target information. Besides, the study constructs the first real-shot multispectral camouflaged people dataset(MSCPD), which encompasses diverse scenes, target scales, and attitudes. To minimize information redundancy, MS-YOLO selects an optimal subset of 12 bands with strong feature representation and minimal inter-band correlation as input. Through experiments on the MSCPD, MS-YOLO achieves a mean Average Precision of 94.31% and real-time detection at 65 frames per second, which confirms the effectiveness and efficiency of our method in detecting camouflaged people in various typical desert and forest scenes. Our approach offers valuable support to improve the perception capabilities of unmanned aerial vehicles in detecting enemy forces and rescuing personnel in battlefield.
基金sponsored by the National Defense Science and Technology Key Laboratory Fund(Grant No.61422062205)the Equipment Pre-Research Fund(Grant No.JCKYS2022LD9)。
文摘Brain functional networks model the brain's ability to exchange information across different regions,aiding in the understanding of the cognitive process of human visual attention during target searching,thereby contributing to the advancement of camouflage evaluation.In this study,images with various camouflage effects were presented to observers to generate electroencephalography(EEG)signals,which were then used to construct a brain functional network.The topological parameters of the network were subsequently extracted and input into a machine learning model for training.The results indicate that most of the classifiers achieved accuracy rates exceeding 70%.Specifically,the Logistic algorithm achieved an accuracy of 81.67%.Therefore,it is possible to predict target camouflage effectiveness with high accuracy without the need to calculate discovery probability.The proposed method fully considers the aspects of human visual and cognitive processes,overcomes the subjectivity of human interpretation,and achieves stable and reliable accuracy.
基金National Natural Science Foundation of China(grant number 61801512,grant number 62071484)Natural Science Foundation of Jiangsu Province(grant number BK20180080)to provide fund for conducting experiments。
文摘The object detectors can precisely detect the camouflaged object beyond human perception.The investigations reveal that the CNNs-based(Convolution Neural Networks)detectors are vulnerable to adversarial attacks.Some works can fool detectors by crafting the adversarial camouflage attached to the object,leading to wrong prediction.It is hard for military operations to utilize the existing adversarial camouflage due to its conspicuous appearance.Motivated by this,this paper proposes the Dual Attribute Adversarial Camouflage(DAAC)for evading the detection by both detectors and humans.Generating DAAC includes two steps:(1)Extracting features from a specific type of scene to generate individual soldier digital camouflage;(2)Attaching the adversarial patch with scene features constraint to the individual soldier digital camouflage to generate the adversarial attribute of DAAC.The visual effects of the individual soldier digital camouflage and the adversarial patch will be improved after integrating with the scene features.Experiment results show that objects camouflaged by DAAC are well integrated with background and achieve visual concealment while remaining effective in fooling object detectors,thus evading the detections by both detectors and humans in the digital domain.This work can serve as the reference for crafting the adversarial camouflage in the physical world.
基金funded by Natural Science Foundation of Jiangsu Province,China,grant number is BK20180579。
文摘In order to solve the problem of poor fusion between the spots of deformation camouflage and the background,a small-spot deformation camouflage design algorithm based on background texture matching is proposed in this research.The combination of spots and textures improved the fusion of the spot pattern and the background.An adversarial autoencoder convolutional network was designed to extract background texture features.The image adversarial loss was added and the reconstruction loss was improved to improve the clarity of the generated texture pattern and the generalization ability of the model.The digital camouflage was formed by obtaining the mean value of the square area and replacing the main color.At the same time,the spots in the square area with a side length of 2 s were subjected to simple linear iterative clustering to form irregular small-spot camouflage.A dataset with a scale of 1050 was established in the experiment.The training results of three different loss functions were investigated.The results showed that the proposed loss function could enhance the generalization of the model and improve the quality of the generated texture image.A variety of digital camouflages with main colors and irregular small-spot camouflage were generated,and their efficiency was tested.On the one hand,intuitive evaluation was given by personnel observing the camouflage pattern embedded in the background and its contour map calculated by the canny operator.On the other hand,objective comparison result was formed by calculating the 4 evaluation indexes between the camouflage pattern and the background.Both results showed that the generated pattern had a high degree of fusion with the background.This model could balance the relationship between the spot size,the number of main colors and the actual effect according to actual needs.
基金This research was funded by Natural Science Foundation of Jiangsu Province,grant number BK20180579.
文摘The method of describing deformation camouflage spots based on feature space has some shortcomings,such as inaccurate description and difficult reproduction.Depending on the strong fitting ability of the generative adversarial network model,the distribution of deformation camouflage spot pattern can be directly fitted,thus simplifying the process of spot extraction and reproduction.The requirements of background spot extraction are analyzed theoretically.The calculation formula of limiting the range of image spot pixels is given and two kinds of spot data sets,forestland and snowfield,are established.Spot feature is decomposed into shape,size and color features,and a GAN(Generative Adversarial Network)framework is established.The effects of different loss functions on network training results are analyzed in the experiment.In the meantime,when the input dimension of generator network is 128,the balance between sample diversity and quality can be achieved.The effects of sample generation are investigated in two aspects.Subjectively,the probability of the generated spots being distinguished in the background is counted,and the results are all less than 20% and mostly close to zero.Objectively,the features of the spot shape are calculated and the independent sample T-test is applied to verify that the features are from the same distribution,and all the P-Values are much higher than 0.05.Both subjective and objective methods prove that the spots generated by this method are similar to the background spots.The proposed method can directly generate the desired camouflage pattern spots,which provides a new technical method for the deformation camouflage pattern design and camouflage effect evaluation.
基金Natural Science Foundation of Jiangsu Province&Key Laboratory Foundation,grant number is BK20180579&6142206180204 respectively.
文摘The evaluation index of camouflage patterns is important in the field of military application.It is the goal that researchers have always pursued to make the computable evaluation indicators more in line with the human visual mechanism.In order to make the evaluation method more computationally intelligent,a Multi-Feature Camouflage Fused Index(MF-CFI)is proposed based on the comparison of grayscale,color and texture features between the target and the background.In order to verify the effectiveness of the proposed index,eye movement experiments are conducted to compare the proposed index with existing indexes including Universal Image Quality Index(UIQI),Camouflage Similarity Index(CSI)and Structural Similarity(SSIM).Twenty-four different simulated targets are designed in a grassland background,28 observers participate in the experiment and record the eye movement data during the observation process.The results show that the highest Pearson correlation coefficient is observed between MF-CFI and the eye movement data,both in the designed digital camouflage patterns and largespot camouflage patterns.Since MF-CFI is more in line with the detection law of camouflage targets in human visual perception,the proposed index can be used for the comparison and parameter optimization of camouflage design algorithms.
基金funded by the National Natural Science Foundation of China(contract grant number 51576188)。
文摘The temperature difference between the exposed surface of an underground silo and the surrounding soil surface is significant,which means a silo can be easily found by infrared detection.We designed an infrared camouflage cloak consisting of an imitative layer and an insulation layer for the silos.The imitative layer is used to imitate the thermal response of the soil to the surrounding environment.The insulation layer is used to weaken the impact of the internal temperature field of the silo on the lower boundary of the imitative layer.A silo model including surrounding soil and a soil model without silo were established,and the influences of the material and thickness of each layer on the infrared camouflage effect were analyzed.The results show that when using a silicone rubber containing alumina powder with a volume fraction of 3.18% as the imitative material,its thermal inertia is in consistent with that of the soil.Meanwhile,it was found that the thickness of the imitative layer doesn't need to be greater than its thermal penetration depth to achieve the infrared camouflage,and the absence of the insulation layer will cause hot spots on the silo surface in winter to weaken the camouflage effect.The optimized thicknesses of the imitative layer and the insulation layer are 22 cm and 4 cm re spectively.The simulations indicate that with the application of the cloak,the maximum value of the absolute values of the temperature differences between the average temperatures of the silo surface and the surrounding soil surface temperatures drops from 1,59℃ to 0.31℃ in summer and from 1.92℃ to 0.21℃ in winter.This designed cloak can achieve an all-weather and full-time passive infrared camouflage.
文摘To address the problems of missing inside and incomplete edge contours in camouflaged target detection results,we propose a camouflaged moving target detection algorithm based on local minimum difference constraints(LMDC).The algorithm first uses the mean to optimize the initial background model,removes the stable background region by global comparison,and extracts the edge point set in the potential target region so that each boundary point(seed)grows along the center of the target.Finally,we define the minor difference constraints term,combine the seed path and the target space consistency,and calculate the attributes of each pixel in the potential target area to realize camouflaged moving target detection.The algorithm of this paper is verified based on a public data sofa video and test videos and compared with the five classic algorithms.The experimental results show that the proposed algorithm yields good results based on integrity,accuracy,and a number of objective evaluation indexes,and its overall performance is better than that of the compared algorithms.
基金This work was supported by QinetiQ(contract number UoBMASTSUB/1000067064)and the EPSRC(grant number EP/M006905/1).
文摘The human visual system is still an important factor in military warfare;military personnel receive training on effective search strategies,and camouflage that can effectively conceal objects and personnel is a key component of a successful integrated survivability strategy.Previous methods of camouflage assessment have,amongst others,used psychophysics to generate distinctiveness metrics.However,the population from which the human observers are drawn is often not well defined,or necessarily appropriate.In this experiment we designed a new platform for testing multiple patterns based on a camouflaged object detection task,and investigate whether trained military observers perform better than civilians.We use a two-alternative forced choice paradigm,with participants searching images of woodland for a replica military helmet displaying Olive Green,Multi Terrain Pattern,US Marine Pattern or,as a conspicuous control,UN Peacekeeper Blue.Our data show that there is no difference in detection performance between the two observer groups but that there are clear differences in the effectiveness of the different helmet colour patterns in a temperate woodland environment.We conclude that when tasks involve very short stimulus presentation times,task-specific training has little effect on the success of target detection and thus this paradigm is particularly suitable for robust estimates of camouflage efficacy.
基金The authors acknowledge the support of Ministry of Education,Science and Technological Development of the Republic of Serbia,research grant No.451-03-68/2020-14/200325 and 451-03-68/2020-14/200287,as well as COST Action CERTBOND(CA18120)and COST Action CONTEXT(CA17107).
文摘In order to examine the possibility to improve its camouflage properties standard cotton fabric with camouflage print was impregnated with poly(vinyl butyral),PVB and fullerene-like nanoparticles of tungsten disulfide,PVB/IF-WS_(2).FTIR analysis excluded any possible chemical interaction of IF-WS_(2) with PVB and the fabric.The camouflage behavior of the impregnated fabric has been examined firstly in the VIS part of the spectrum.Diffuse reflection,specular gloss and color coordinates were measured for three different shades(black,brown and dark green).Thermal imaging was applied to examine the camouflage abilities of this impregnation in IR part of the spectrum.The obtained results show that PVB/IF-WS_(2) impregnation system induced enhacement of the materials camouflage properties,i.e.that IF-WS_(2) have a positive effect on spectrophotometric characteristics of the fabric.
基金supported by the State Key Laboratory of Pulsed Power Laser Technology, College of Electronic Engineering, National University of Defense Technology, Hefei, China。
文摘This paper reports an alternative approach to the evaluation of infrared camouflage effectiveness via a multi-fractal method. By calculating multi-fractal spectra of the target region and the background regions in an infrared image, the spectrum shape features and the discrete Frechet distances among these spectra were used to analyze the camouflage effectiveness of the target qualitatively and quantitatively,and the correlation coefficients of the spectra were further used as the index of camouflage effectiveness.It was found that the camouflaged target had better camouflage effectiveness than the target without camouflage in the same one background, and the same one camouflaged target had different camouflage effectiveness in different backgrounds. On the whole, the target matching well with its background had high camouflage effectiveness value. This approach can expand the application of multi-fractal theory in infrared camouflage technology, which should be useful for the research of infrared camouflage materials, the design of camouflage patterns as well as the deployment of military equipment in battlefield.
基金This work was supported by the National Nature Science Foundation of China(11572097).
文摘This paper proposes a cooperative guidance law for attacking a ground target with the impact angle constraint based on the motion camouflage strategy in the line-of-sight(LOS)frame.A dynamic model with the impact angle constraint is established according to the relative motion between multiple missiles and the target.The process of cooperative guidance law design is divided into two stages.Firstly,based on the undirected graph theory,a new finite-time consensus protocol on the LOS direction is derived to guarantee relative distances reach consensus.And the value of acceleration command is positive,which is beneficial for engineering realization.Secondly,the acceleration command on the normal direction of the LOS is designed based on motion camouflage and finite-time convergence,which can ensure the missiles reach the target with the desired angle and satisfy the motion camouflage state.The finitetime stability analysis is proved by the Lyapunov theory.Numerical simulations for stationary and maneuver targets have demonstrated the effectiveness of the cooperative guidance law proposed.
文摘A quick and accurate extraction of dominant colors of background images is the basis of adaptive camouflage design.This paper proposes a Color Image Quick Fuzzy C-Means(CIQFCM)clustering algorithm based on clustering spatial mapping.First,the clustering sample space was mapped from the image pixels to the quantized color space,and several methods were adopted to compress the amount of clustering samples.Then,an improved pedigree clustering algorithm was applied to obtain the initial class centers.Finally,CIQFCM clustering algorithm was used for quick extraction of dominant colors of background image.After theoretical analysis of the effect and efficiency of the CIQFCM algorithm,several experiments were carried out to discuss the selection of proper quantization intervals and to verify the effect and efficiency of the CIQFCM algorithm.The results indicated that the value of quantization intervals should be set to 4,and the proposed algorithm could improve the clustering efficiency while maintaining the clustering effect.In addition,as the image size increased from 128×128 to 1024×1024,the efficiency improvement of CIQFCM algorithm was increased from 6.44 times to 36.42 times,which demonstrated the significant advantage of CIQFCM algorithm in dominant colors extraction of large-size images.
文摘The paper explores the possibilities of using carbonyl iron in the form of a powder for the manufacture of radar-absorbing paints-reducing the radar signature of the objects that they cover.The attenuation values in the range of 4-18 GHz for various coating thicknesses,ranging from 0.5 to 2.00 mm with 0.5 mm increment,and for different absorber content-75%and 80%,as well as the use of two different binders in the form of epoxy resins with hardeners,were investigated.For the frequency of 18 GHz and a 1.5 mm thick coating with a 75%absorber content,Epidian 112 resin and Saduramid 10/50 hardener used as a binder,and the maximum attenuation level obtained equalled 20.2 d B at 16 GHz.Additionally,the absorber particle size ranging from 3 to 4μm and its higher mass content resulted in achieving the reflection loss above-12 d B in the entire 8-12.5 GHz range for layers between 1-and 1.5 mm thickness.The qualitative assessment of the tested samples in the context of camouflage in the radar range was also performed,using statistical analysis.
