Camouflage Detection Research Articles

Search and recovery network for camouflaged object detection

Camouflaged object detection aims to accurately identify objects blending into the background. However, existing methods often struggle, especially with small object or multiple objects, due to their reliance on singular strategies. To address this, we introduce a novel Search and Recovery Network (SRNet) using a bionic approach and auxiliary features. SRNet comprises three key modules: the Region Search Module (RSM), Boundary Recovery Module (BRM), and Camouflaged Object Predictor (COP). The RSM mimics predator behavior to locate potential object regions, enhancing object location detection. The BRM refines texture features and recovers object boundaries. The COP fuse multilevel features to predict final segmentation maps. Experimental results on three benchmark datasets show SRNet's superiority over SOTA models, particularly with small and multiple objects. Notably, SRNet achieves performance improvements without significantly increasing model parameters. Moreover, the method exhibits promising performance in downstream tasks such as defect detection, polyp segmentation and military camouflage detection.

Camouflage detection: Optimization-based computer vision for Alligator sinensis with low detectability in complex wild environments

Alligator sinensis is an extremely rare species that possesses excellent camouflage, allowing it to fit perfectly into its natural environment. The use of camouflage makes detection difficult for both humans and automated systems, highlighting the importance of modern technologies for animal monitoring. To address this issue, we present YOLO v8-SIM, an innovative detection technique specifically developed to significantly enhance the identification precision. YOLO v8-SIM utilizes a sophisticated dual-layer attention mechanism, an optimized loss function called inner intersection-over-union (IoU), and a technique called slim-neck cross-layer hopping. The results of our study demonstrate that the model achieves an accuracy rate of 91 %, a recall rate of 89.9 %, and a mean average precision (mAP) of 92.3 % and an IoU threshold of 0.5. In addition, the model operates at a frame rate of 72.21 frames per second (FPS) and excels at accurately recognizing objects that are partially visible or smaller in size. To further improve our initiatives, we suggest creating an open-source collection of data that showcases A. sinensis in its native environment while using camouflage techniques. These developments collectively enhance the ability to detect disguised animals, thereby promoting the monitoring and protection of biodiversity, and supporting ecosystem sustainability.

Reparameterized underwater object detection network improved by cone-rod cell module and WIOU loss

To overcome the challenges in underwater object detection across diverse marine environments—marked by intricate lighting, small object presence, and camouflage—we propose an innovative solution inspired by the human retina's structure. This approach integrates a cone-rod cell module to counteract complex lighting effects and introduces a reparameterized multiscale module for precise small object feature extraction. Moreover, we employ the Wise Intersection Over Union (WIOU) technique to enhance camouflage detection. Our methodology simulates the human eye's cone and rod cells' brightness and color perception using varying sizes of deep and ordinary convolutional kernels. We further augment the network's learning capability and maintain model lightness through structural reparameterization, incorporating multi-branching and multiscale modules. By substituting the Complete Intersection Over Union (CIOU) with WIOU, we increase penalties for low-quality samples, mitigating the effect of camouflaged information on detection. Our model achieved a MAP_0.75 of 72.5% on the Real-World Underwater Object Detection (RUOD) dataset, surpassing the leading YOLOv8s model by 5.8%. Additionally, the model's FLOPs and parameters amount to only 10.62 M and 4.62B, respectively, which are lower than most benchmark models. The experimental outcomes affirm our design's efficacy in addressing underwater object detection's various disturbances, offering valuable technical insights for related oceanic image processing challenges.

Low-Light Sparse Polarization Demosaicing Network (LLSPD-Net): Polarization Image Demosaicing Based on Stokes Vector Completion in Low-Light Environment.

Polarization imaging has achieved a wide range of applications in military and civilian fields such as camouflage detection and autonomous driving. However, when the imaging environment involves a low-light condition, the number of photons is low and the photon transmittance of the conventional Division-of-Focal-Plane (DoFP) structure is small. Therefore, the traditional demosaicing methods are often used to deal with the serious noise and distortion generated by polarization demosaicing in low-light environment. Based on the aforementioned issues, this paper proposes a model called Low-Light Sparse Polarization Demosaicing Network (LLSPD-Net) for simulating a sparse polarization sensor acquisition of polarization images in low-light environments. The model consists of two parts: an intensity image enhancement network and a Stokes vector complementation network. In this work, the intensity image enhancement network is used to enhance low-light images and obtain high-quality RGB images, while the Stokes vector is used to complement the network. We discard the traditional idea of polarization intensity image interpolation and instead design a polarization demosaicing method with Stokes vector complementation. By using the enhanced intensity image as a guide, the completion of the Stokes vector is achieved. In addition, to train our network, we collected a dataset of paired color polarization images that includes both low-light and regular-light conditions. A comparison with state-of-the-art methods on both self-constructed and publicly available datasets reveals that our model outperforms traditional low-light image enhancement demosaicing methods in both qualitative and quantitative experiments.

A Binary Object Detection Pattern Model to Assist the Visually Impaired in detecting Normal and Camouflaged Faces

This study presents a novel Binary Object Detection Pattern Model (BODPM) to detect objects with face key points and recognize them using the KERAS dataset. The proximity and accuracy of the recognized items were evaluated using computer vision techniques. The object recognition time interval and duration were recorded and stored permanently in a database, and the information was communicated to the visually impaired user as voice output. The normal face, without wearing a mask, was identified using binary patterns with proximity detection. Camouflaged objects were detected in a maximum probability range of 100%. The proposed method was tested, calculating accuracy and score, and compared with existing models, showcasing remarkable performance. The proposed method of normal and camouflage detection is a novel prediction with proximity analysis of objects in a frame.

MangroveSeg: Deep-Supervision-Guided Feature Aggregation Network for Mangrove Detection and Segmentation in Satellite Images

Mangrove forests are significant participants in coastal ecological environment systems. For the development of protection strategies, it is crucial to automatically and accurately detect the distribution and area of mangroves using satellite images. Although many deep-learning-based mangrove detection and segmentation algorithms have made notable progress, the complex regional structures and the great similarity between mangroves and the surrounding environment, as well as the diversity of mangroves, render the task still challenging. To cover these issues, we propose a novel deep-supervision-guided feature aggregation network for mangrove detection and segmentation called MangroveSeg, which is based on a U-shaped structure with ResNet, combining an attention mechanism and a multi-scale feature extraction framework. We also consider the detection and segmentation of mangroves as camouflage detection problems for the improvement and enhancement of accuracy. To determine more information from extracted feature maps in a hidden layer, a deep supervision model is introduced in up-sampling to enhance feature representation. The spatial attention mechanism with attention gates is utilized to highlight significant regions and suppress task-independent feature responses. The feature fusion module can obtain multi-scale information by binding each layer to the underlying information and update feature mappings. We validated our framework for mangrove detection and segmentation using a satellite image dataset, which includes 4000 images comprising 256 × 256 pixels; we used 3002 for training and 998 for testing. The satellite images dataset was obtained from the Dongzhaigang National Nature Reserve located in Haikou City, Hainan Province, China. The proposed method achieved a 89.58% overall accuracy, 89.02% precision, and 80.7% mIoU. We also used the trained MangroveSeg model to detect mangroves on satellite images from other regions. We evaluated the statistical square measure of some mangrove areas and found that the evaluation accuracy can reach 96% using MangroveSeg. The proposed MangroveSeg model can automatically and accurately detect the distribution and area of mangroves from satellite images, which provides a method for monitoring the ecological environment.

Camouflage Detection and Its Association with Cognitive Style: A Functional Connectivity Study.

Background: Individual differences exist in performance in tasks that require visual search, such as camouflage detection (CD). Field dependence/independence (FD/I), as assessed using the Group Embedded Figures Test (GEFT), is an extensively studied dimension of cognitive style that classifies participants based on their visual perceptual styles. Materials and Methods: In the present study, we utilized fMRI on 46 healthy participants to investigate the underlying neural mechanisms specific to the cognitive styles of FD/FI while performing a CD task using both activation magnitude and an exploratory functional connectivity (FC) analysis. Group differences between high and low performers on the two extremes of the accuracy continuum of GEFT were studied. Results: No statistically significant group differences were observed using whole-brain voxel-wise comparison. However, the exploratory FC analysis revealed an enhanced communication between various regions subserving the cognitive traits required for visual search by FI participants over and above their FD counterparts. Conclusion: These enhanced connectivities suggest additional recruitment of cognitive functions to provide computational support that might facilitate superior performance in CD task by the participants who display a field-independent cognitive style.

Measuring and Predicting Sensor Performance for Camouflage Detection in Multispectral Imagery.

To improve the management of multispectral sensor systems on small reconnaissance drones, this paper proposes an approach to predict the performance of a sensor band with respect to its ability to expose camouflaged targets under a given environmental context. As a reference for sensor performance, a new metric is introduced that quantifies the visibility of camouflaged targets in a particular sensor band: the Target Visibility Index (TVI). For the sensor performance prediction, several machine learning models are trained to learn the relationship between the TVI for a specific sensor band and an environmental context state extracted from the visual band by multiple image descriptors. Using a predicted measure of performance, the sensor bands are ranked according to their significance. For the training and evaluation of the performance prediction approach, a dataset featuring 853 multispectral captures and numerous camouflaged targets in different environments was created and has been made publicly available for download. The results show that the proposed approach can successfully determine the most informative sensor bands in most cases. Therefore, this performance prediction approach has great potential to improve camouflage detection performance in real-world reconnaissance scenarios by increasing the utility of each sensor band and reducing the associated workload of complex multispectral sensor systems.

Predicting human camouflage detection with a principled computational model

Predicting human camouflage detection with a principled computational model

A Highly Interactive Honeypot-Based Approach to Network Threat Management

In this paper, considering the problem that the common defensive means in the current cyber confrontation often fall into disadvantage, honeypot technology is adopted to turn reactive into proactive to deal with the increasingly serious cyberspace security problem. We address the issue of common defensive measures in current cyber confrontations that frequently lead to disadvantages. To tackle the progressively severe cyberspace security problem, we propose the adoption of honeypot technology to shift from a reactive to a proactive approach. This system uses honeypot technology for active defense, tempting attackers into a predetermined sandbox to observe the attacker’s behavior and attack methods to better protect equipment and information security. During the research, it was found that due to the singularity of traditional honeypots and the limitations of low-interactivity honeypots, the application of honeypot technology has difficulty in achieving the desired protective effect. Therefore, the system adopts a highly interactive honeypot and a modular design idea to distinguish the honeypot environment from the central node of data processing, so that the honeypot can obtain more sufficient information and the honeypot technology can be used more easily. By managing honeypots at the central node, i.e., adding, deleting, and modifying honeypots and other operations, it is easy to maintain and upgrade the system, while reducing the difficulty of using honeypots. The high-interactivity honeypot technology not only attracts attackers into pre-set sandboxes to observe their behavior and attack methods, but also performs a variety of advanced functions, such as network threat analysis, virtualization, vulnerability perception, tracing reinforcement, and camouflage detection. We have conducted a large number of experimental comparisons and proven that our method has significant advantages compared to traditional honeypot technology and provides detailed data support. Our research provides new ideas and effective methods for network security protection.

The relationship between spatial color mixing with color and radiation similarity in digital camouflage study

With the development of optical detection technology, the camouflage design in the visible light band has been unable to meet the requirements of the visible light/infrared dual-band digital camouflage detection. The digital camouflage in the human eye vision system involves factors such as the color, shape and texture of the target and background, of which color dominates. And because spatial color mixing is one of the main features of digital camouflage, it is also a key method to improve the camouflage effect. In this paper, a quantitative spatial color mixing coefficient (SCMC) method is proposed to characterize the degree of color mixing of spatial colors. The relationship between spatial color mixing coefficient and color similarity in the visible light range and the relationship between spatial color mixing coefficient and radiation similarity in the infrared range are analyzed. The relationship provides theoretical support for the design and deployment of military equipment on the battlefield. The results show that in the visible light band, the spatial color mixing coefficient can better represent the spatial color mixing degree of digital camouflage, and has a specific relationship with color similarity. In the infrared band, the spatial color mixing coefficient cannot well represent the spatial color mixing degree of digital camouflage, but it can predict its relationship with radiation similarity.

Camouflage detection: experiments and a principled theory

Camouflage detection: experiments and a principled theory

Camouflage Clothes Detection

Abstract: Presentation of a study and implementation on task named camouflage clothes detection. The aim of the study is to identify clothes that “seamlessly” blend in with their surroundings. Usage of a dataset, called 8k_normal_vs_camouflage_clothes_images, which consists of about 8k images of camouflage clothes and 8k images of normal clothes. Residual Networks (ResNets) are used for this task. The high intrinsic similarities between background and object make camouflage detection a challenging task

Troop camouflage detection based on deep action learning

<p><span lang="EN-US">Detecting troop camouflage on the battlefield is crucial to beat or decide in critical situations to survive. This paper proposed a hybrid model based on deep action learning for camouflage recognition and detection. To involve deep action learning in this proposed system, deep learning based on you only look once (YOLOv3) with SquezeeNet and the fourth steps on action learning were engaged. Following the successful formulation of the learning cycle, an instrument examines the environment and performance in action learning with qualitative weightings; specific target detection experiments with view angle, target localization, and the firing point procedure were performed. For each deep action learning cycle, the complete process is divided into planning, acting, observing, and reflecting. If the results do not meet the minimal passing grade after the first cycle, the cycle will be repeated until the system succeeds in the firing point. Furthermore, this study found that deep action learning could enhance intelligence over earlier camouflage detection methods, while maintaining acceptable error rates. As a result, deep action learning could be used in armament systems if the environment is properly identified.</span></p>

Adopting Hyperspectral Anomaly Detection for Near Real-Time Camouflage Detection in Multispectral Imagery

Tactical reconnaissance using small unmanned aerial vehicles has become a common military scenario. However, since their sensor systems are usually limited to rudimentary visual or thermal imaging, the detection of camouflaged objects can be a particularly hard challenge. With respect to SWaP-C criteria, multispectral sensors represent a promising solution to increase the spectral information that could lead to unveiling camouflage. Therefore, this paper investigates and evaluates the applicability of four well-known hyperspectral anomaly detection methods (RX, LRX, CRD, and AED) and a method developed by the authors called local point density (LPD) for near real-time camouflage detection in multispectral imagery based on a specially created dataset. Results show that all targets in the dataset could successfully be detected with an AUC greater than 0.9 by multiple methods, with some methods even reaching an AUC relatively close to 1.0 for certain targets. Yet, great variations in detection performance over all targets and methods were observed. The dataset was additionally enhanced by multiple vegetation indices (BNDVI, GNDVI, and NDRE), which resulted in generally higher detection performances of all methods. Overall, the results demonstrated the general applicability of the hyperspectral anomaly detection methods for camouflage detection in multispectral imagery.

Color vision deficiencies and camouflage: a comparative study between normal and CVD observers

There is a belief that observers with color vision deficiencies (CVD) perform better in detecting camouflaged objects than normal observers. Some studies have concluded contradictory findings when studying the performance of normal and CVD observers in the camouflage detection tasks in different conditions. This work presents a literature review on this topic, dividing it into three different and contradictory types of results: better performance for CVD, for normal observers, or same performance. Besides, two psychophysical experiments have been designed and carried out in a calibrated computer monitor on both normal and CVD human observers to measure the searching times of the different types of observers needed to find camouflaged stimuli in two different types of stimuli. Results show the trend that, in our experimental conditions, normal observers need shorter searching times than CVD observers in finding camouflaged stimuli both in images of natural scenes and in images with synthetic stimuli.

Malicious code dynamic traffic camouflage detection based on deep reinforcement learning in power system

In order to solve the problem that malicious code intrudes into software in various forms, which leads to its security performance degradation and cannot be used normally, this paper proposes a malicious code dynamic traffic camouflage detection method based on deep reinforcement learning in power system. The average mutual information between codes is calculated by deep reinforcement learning, and the weighted information gain of each code type feature is obtained. Different types of code feature set classifiers are generated, and an optimal classifier is output for each type of code feature set. The features are reduced by Linear Discriminant Analysis (LDA), and the network code is classified according to the extracted features. The potential malicious code is detected according to the explicit rules of deep reinforcement learning. Simulation results show that the detection method can improve the accuracy of malicious code classification, and the detection performance is increased to about 35%.

Polarization Imaging Detection of Individual Camouflage Based on Two-Stream Fusion Network

Polarization Imaging Detection of Individual Camouflage Based on Two-Stream Fusion Network

Training detection of camouflaged targets in natural scenes: Backgrounds and targets both matter

As target-background similarity increases, search performance declines, but this pattern can be attenuated with training. In the present study we (1) characterized training and transfer effects in visual search for camouflaged targets in naturalistic scenes, (2) evaluated whether transfer effects are preserved 3 months after training, (3) tested the suitability of the perceptual learning hypothesis (i.e., using learned scene statistics to aid camouflaged target detection) for explaining camouflage search improvements over training, and (4) provide guidance for camouflage detection training in practice. Participants were assigned to one of three training groups: adaptive camouflage (difficulty varied by performance), massed camouflage (difficulty increased over time), or an active control (no camouflage), and trained over 14 sessions. Additional sessions measured transfer (immediately post training) and retention of training benefits (10 days and 3 months post training). Both the adaptive and massed training groups showed improved camouflaged target detection up to 3 months following training, relative to the control. These benefits were observed only with backgrounds and targets that were similar to those experienced during training and are broadly consistent with the perceptual learning hypothesis. In practice, training interventions should utilize stimuli similar to the operational environment in which detection is expected to occur.

Camouflage detection with texture statistical characterization in autonomous systems

PurposeThe purpose of the research work is to detect camouflaged objects in autonomous systems of military applications and civilian applications such as detecting insects in paddy fields, identifying duplicate products in different texture environments.Design/methodology/approachCamouflaged objects detection is performed by smoothing texture with nonlinear models and characterizing with statistical methods to detect the objects.FindingsThere are few challenges in existing camouflaged objects detection due to the complexities involved in the detection process. This work proposes a constructive approach with texture statistical characterization for camouflage detection. The proposed technique is found to be better than existing methods while assessing its performance using precision and recall.Research limitations/implicationsEven though there is lot of research work carried, there are few challenges for autonomous systems in camouflage detection due to the complexities involved in the detection process such as texture modeling and dynamic background problems and environment conditions for autonomous system.Practical implicationsCamouflage detection finds potential applications in security systems, surveillance, military and autonomous systems. The proposed work is implemented in different environments for camouflage detection.Social implicationsSocial problems such as image acquisition environment, time of day, desert, forest and grass fields of paddy.Originality/valueThe proposed method detects camouflaged objects in autonomous systems where it is applied for images of different kinds. It is found to be effective on images recorded in battlefield and challenging environments.