Camouflage Assessment Research Articles

An HVS-derived network for assessing the quality of camouflaged targets with feature fusion

High-value assets on the battlefield typically require adequate camouflage to evade detection and annihilation by enemy scouts. Consequently, artificial camouflage technology is extensively acknowledged and utilized as a crucial defensive tactic in the military sphere. The quality of camouflage performance was assessed by military observers through the human visual system (HVS). This method involved locating the camouflaged objects and rating the camouflaged degree against the background. Current camouflage assessment methods typically involved the manual extraction and aggregation of objective features throughout an image. These approaches fall short in constructing a correlation mapping between objective features and subjective perceptions of camouflaged objects, culminating in imprecise assessments and discrepancies. To address these issues, this paper presents the first three-stage full-reference learning framework for locating camouflaged objects, extracting camouflage features, and assessing camouflage quality. Given the lack of datasets specifically designed for evaluating camouflage quality, we have contributed a datasets focused on human-camouflaged targets. The experimental results show that the three-stage framework is remarkably accurate in assessing the camouflage quality, leading to an explainable network. The camouflaged people quality assessment(CPQA) dataset is available at http://github.com/samsunq/CPQA_Datasets.git.

SPCC: A superpixel and color clustering based camouflage assessment

SPCC: A superpixel and color clustering based camouflage assessment

Camouflaged Adversarial Patch Attack on Object Detector

Adversarial attacks have received great attentions for their capacity to distract state-of-the-art neural networks by modifying objects in physical domain. Patch-based attack especially have got much attention for its optimization effectiveness and feasible adaptation to any objects to attack neural network-based object detectors. However, despite their strong attack performance, generated patches are strongly perceptible for humans, violating the fundamental assumption of adversarial examples. In this paper, we propose a camouflaged adversarial patch optimization method using military camouflage assessment metrics for naturalistic patch attacks. We also investigate camouflaged attack loss functions, applications of various camouflaged patches on army tank images, and validate the proposed approach with extensive experiments attacking Yolov5 detection model. Our methods produce more natural and realistic looking camouflaged patches while achieving competitive performance.

Camouflage Assessment of Aluminium Coated Textiles for Woodland and Desertland Combat Background in Visible and Infrared Spectrum under UV Vis IR Background Illumination

Aluminium powder (AP) formulated polyamide 6, 6 (PA 6, 6), cotton fabric and hollow tubular polyester beg (HTPB) were experimented for spectral, chromatic and imaging principle of woodland and desertland camouflage textiles in visible (Vis) and infrared (IR) spectrum under three-dimensional background illumination of ultraviolet (UV)-Vis-IR for concealment and detection of target signature, AP formulated fabric practiced by defense professional. Cotton and polyamide 6, 6 fabric, HTPB were coated and padded with AP powder formulated polyurethane (PU) based binder. Reflection profile of woodland combat background (CB) and coated-padded textiles have been depicted in terms of multidimensional illumination properties of Vis imaging such as chromatic intensity, spectral reflection, Kubelka-Munk (K-M) reflectance, color rendering and photonic response. A symmetrical chromatic value (L*, a*, b*) of AP coated fabric has been portrayed for concealment of target signature against woodland CB. Spectral reflection, chromatic illumination, K-M reflectance and scanning electron microscopy have also been critically confirmed five-time durability of color fastness to wash, tested by AATCC method 61-2013. HTPB-phase change material (PCM) formulation has been found temperature controllable effect of target object (TOB) in terms of heat energy at limited temperature, 30°C. Heat energy versus target concealment of IR imaging principle can be implemented for high-performance camouflage textiles design. The reflection profile of AP coated fabric can be applied for multidimensional camouflage coloration/patterning/HTPB-PCM based IR camouflage technology and direct applications of defence pavilion/tents/clothing against woodland/desertland CB. Therefore, the key phenomenon of this article has been focused on remote sensing properties of eucalyptus tree, scientific name, Eucalyptus Mannifera, a common CB of woodland and AP formulated fabric for defence application against woodland and desertland CB in terms of concealment of defence surveillance under consideration of laboratory and field trialling of woodland and desertland camouflage textiles assessment in Vis-IR spectrum. Therefore, a standardised methodology of camouflage textiles assessment for CDRI has been established under a new technique with conformist machine and technology which may be a new contribution to carry out further research and development for the fighting protection in combat environment under maintaining a standardized sequence from laboratory trialling to field experimentation in a limited timeframe of research.

Assessment method for camouflage performance based on visual perception

Camouflage stealth is of great significance in concealing military targets and countering military reconnaissance, but the assessment methods for camouflage performance remain some problems, such as incomplete evaluation indexes and inconsistency with the visual perception. This study proposes a comprehensive assessment model, named comprehensive similarity, CSsub for short. The model bases on subjective visual evaluation and contains three metrics of image color appearance, structure, and texture. The metrics are analysed by the image color appearance similarity (ICAS), structure similarity (ISS), and texture similarity (ITS), respectively, to measure the camouflage effectiveness between background and camouflage target images. The three weights, in CSsub, are fitted through the results of subjective experiments. In the experiments, five ships are camouflaged in six sea states, a total of 30 camouflage scenes. Finally, seven evaluation methods, including the proposed model, are analyzed, and compared to predict camouflage effectiveness. The results show that CSsub model has the best prediction performance. And the ICAS index, which is applied in camouflage assessment for the first time, accounts for a significant weight in the model, indicating that color has a great impact on image perception. So, this method based on visual perception has more superiority than others and can be used as an accurate and reliable assessment model in predicting camouflage performance.

A platform for initial testing of multiple camouflage patterns

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.

위장패턴과 환경이미지의 컬러와 형태소를 중심으로 카무플라주 패턴의 위장성 평가 - 한국환경에서 한국군복과 미군복의 위장성 평가를 중심으로 -

위장패턴과 환경이미지의 컬러와 형태소를 중심으로 카무플라주 패턴의 위장성 평가 - 한국환경에서 한국군복과 미군복의 위장성 평가를 중심으로 -

Optimization of color design for military camouflage in CIELAB color space

AbstractThe assessment of military camouflage is a key consideration in the modern military field. Traditionally, the assessment relies on traditional human visual detection tests because a large scale multi‐level and multi‐factor experiments are time‐ and resource‐consuming. One aspect of camouflage assessment, to which this current study pertains, entails improving upon or “enhancing” an existing or “selected” design. The current study presents a new and practical approach for enhancing the selected military camouflage by utilizing response surface methodology (RSM) of %L*, %a*, and %b* in CIELAB color space. Ten participants were recruited to evaluate 35 variations of %L*, %a*, and %b* on camouflage similarity index (CSI) and reaction time (RT). Based on RSM, the optimum combination occurs at L*: 61.4966, a*: −5.6505, and b*: 10.5114. In addition, a predictive algorithm to calculate the optimum shift of %L*, %a*, and %b* from the original camouflage to the improved camouflage derived from RSM is also proposed. The optimum shift occurs at −25%L*, −55%a*, and + 80%b*. In the end, a new design guideline is proposed for the enhancement of selected military camouflage, which adopts the present study's research findings.

Camouflage assessment: Machine and human

A vision model is designed using low-level vision principles so that it can perform as a human observer model for camouflage assessment. In a camouflaged-object assessment task, using military patterns in an outdoor environment, human performance at detection and recognition is compared with the human observer model. This involved field data acquisition and subsequent image calibration, a human experiment, and the design of the vision model. Human and machine performance, at recognition and detection, of military patterns in two environments was found to correlate highly. Our model offers an inexpensive, automated, and objective method for the assessment of camouflage where it is impractical, or too expensive, to use human observers to evaluate the conspicuity of a large number of candidate patterns. Furthermore, the method should generalize to the assessment of visual conspicuity in non-military contexts.

Developing and Evaluating a Target-Background Similarity Metric for Camouflage Detection

BackgroundMeasurement of camouflage performance is of fundamental importance for military stealth applications. The goal of camouflage assessment algorithms is to automatically assess the effect of camouflage in agreement with human detection responses. In a previous study, we found that the Universal Image Quality Index (UIQI) correlated well with the psychophysical measures, and it could be a potentially camouflage assessment tool.MethodologyIn this study, we want to quantify the camouflage similarity index and psychophysical results. We compare several image quality indexes for computational evaluation of camouflage effectiveness, and present the results of an extensive human visual experiment conducted to evaluate the performance of several camouflage assessment algorithms and analyze the strengths and weaknesses of these algorithms.SignificanceThe experimental data demonstrates the effectiveness of the approach, and the correlation coefficient result of the UIQI was higher than those of other methods. This approach was highly correlated with the human target-searching results. It also showed that this method is an objective and effective camouflage performance evaluation method because it considers the human visual system and image structure, which makes it consistent with the subjective evaluation results.

Developing a similarity index for static camouflaged target detection

Measurement of camouflage performance is of fundamental importance for military stealth applications. The goal of camouflage assessment algorithms is to automatically assess the effectiveness of camouflage in agreement with human detection responses. We propose a new method for computational evaluation of camouflage effectiveness. We evaluate the performance of the new method and other existing algorithms in a comparative setting which utilised an extensive human visual experiment. The results show that the new method is correlated with the human target searching results with higher correlations than the existing algorithms.

Evaluating camouflage design using eye movement data

This study investigates the characteristics of eye movements during a camouflaged target search task. Camouflaged targets were randomly presented on two natural landscapes. The performance of each camouflage design was assessed by target detection hit rate, detection time, number of fixations on display, first saccade amplitude to target, number of fixations on target, fixation duration on target, and subjective ratings of search task difficulty. The results showed that the camouflage patterns could significantly affect the eye-movement behavior, especially first saccade amplitude and fixation duration, and the findings could be used to increase the sensitivity of the camouflage assessment. We hypothesized that the assessment could be made with regard to the differences in detectability and discriminability of the camouflage patterns. These could explain less efficient search behavior in eye movements. Overall, data obtained from eye movements can be used to significantly enhance the interpretation of the effects of different camouflage design.

Urban camouflage assessment through visual search and computational saliency

We present a new method to derive a multiscale urban camouflage pattern from a given set of background image samples. We applied this method to design a camouflage pattern for a given (semi-arid) urban environment. We performed a human visual search experiment and a computational evaluation study to assess the effectiveness of this multiscale camouflage pattern relative to the performance of 10 other (multiscale, disruptive and monotonous) patterns that were also designed for deployment in the same operating theater. The results show that the pattern combines the overall lowest detection probability with an average mean search time. We also show that a frequency-tuned saliency metric predicts human observer performance to an appreciable extent. This computational metric can therefore be incorporated in the design process to optimize the effectiveness of camouflage patterns derived from a set of background samples. © 2012 Society of Photo-Optical Instrumentation Engineers (SPIE)

CAMEO-SIM: a physics-based broadband scene simulation tool for assessment of camouflage, concealment, and deception methodologies

Assessment of camouflage, concealment, and deception (CCD) methodologies is not a trivial problem; conventionally the only method has been to carry out field trials, which are both expensive and subject to the vagaries of the weather. In recent years computing power has increased, such that there are now many research programs using synthetic environments for CCD assessments. Such an approach is at- tractive; the user has complete control over the environment parameters and many more scenarios can be investigated. The UK Ministry of De- fence is currently developing a synthetic scene generation tool for as- sessing the effectiveness of air vehicle camouflage schemes. The soft- ware is sufficiently flexible to allow it to be used in a broader range of applications, including full CCD assessment. The synthetic scene simu- lation system (CAMEO-SIM) has been developed, as an extensible sys- tem, to provide imagery within the 0.4 to 14 mm spectral band with as high a physical fidelity as possible. It consists of a scene design tool, an image generator, that incorporates both radiosity and ray-tracing pro- cesses, and an experimental trials tool. The scene design tool allows the user to develop a three-dimensional representation of the scenario of interest from a fixed viewpoint. Target(s) of interest can be placed any- where within this 3-D representation and may be either static or moving. Different illumination conditions and effects of the atmosphere can be modeled together with directional reflectance effects. The user has com- plete control over the level of fidelity of the final image. The output from the rendering tool is a sequence of radiance maps, which may be used by sensor models or for experimental trials in which observers carry out target acquisition tasks. The software also maintains an audit trail of all data selected to generate a particular image, both in terms of material properties used and the rendering options chosen. A range of verification tests has shown that the software computes the correct values for ana- lytically tractable scenarios. Validation tests using simple scenes have also been undertaken. More complex validation tests using observer tri- als are planned. The current version of CAMEO-SIM and how its images are used for camouflage assessment is described. The verification and validation tests undertaken are discussed. In addition, example images will be used to demonstrate the significance of different effects, such as spectral rendering and shadows. Planned developments of CAMEO-SIM are also outlined. © 2001 Society of Photo-Optical Instrumentation Engineers.

Assessing camouflage methods using textural features

Developments in the area of signature suppression make it progressively more difficult to recognize targets. The emphasis has been on the reduction of distinct features, like hot spots in the infrared band. Thus, to obtain a low false alarm rate, threat sensors have to utilize more information, i.e., spatial and spectral properties. The purpose of our work is to develop a general tool for camouflage assessment. The approach proposed applies texture descriptors to quantify the similarity between different parts of an image. In addition, other descriptors are used to distinguish man-made object characteristics. The selection of an appropriate set of features is discussed. The assumption is that an area containing observable targets has different statistics than other areas. Statistical properties along with detected target specific features have to be combined with methods used in data fusion. An experiment with a dataset of 44 reference images has been carried out, using a recently developed computer program called Terrtex. High correlation with perception experiments is achieved using only one or two texture features. The importance of a careful selection of background area size is finally discussed. (C) 2001 Society of Photo-optical instrumentation Engineers.