Camouflaged object detection via context and texture-aware hierarchical interaction.

Camouflaged object detection (COD), in a broad sense, aims to detect image objects that have high degrees of similarity to the background. COD is more challenging than conventional object detection because of the high degree of “fusion” between a camouflaged object and the background. In this paper, we focused on the accurate detection of camouflaged objects, conducting an in-depth study on COD and addressing the common detection problems of high miss rates and low confidence levels. We proposed a ternary cascade perception-based method for detecting camouflaged objects and constructed a cascade perception network (CPNet). The innovation lies in the proposed ternary cascade perception module (TCPM), which focuses on extracting the relationship information between features and the spatial information of the camouflaged target and the location information of key points. In addition, a cascade aggregation pyramid (CAP) and a joint loss function have been proposed to recognize camouflaged objects accurately. We conducted comprehensive experiments on the COD10K dataset and compared our proposed approach with other seventeen-object detection models. The experimental results showed that CPNet achieves optimal results in terms of six evaluation metrics, including an average precision (AP)50 that reaches 91.41, an AP75 that improves to 73.04, and significantly higher detection accuracy and confidence.

A systematic review of image-level camouflaged object detection with deep learning

Camouflaged objects share very similar colors but have different semantics with the surroundings. Cognitive scientists observe that both the global contour (i.e., boundary) and the local pattern (i.e., texture) of camouflaged objects are key cues to help humans find them successfully. Inspired by the cognitive scientist's observation, we propose a novel boundary-and-texture enhancement network (FindNet) for camouflaged object detection (COD) from single images. Different from most of existing COD methods, FindNet embeds both the boundary-and-texture information into the camouflaged object features. The boundary enhancement (BE) module is leveraged to focus on the global contour of the camouflaged object, and the texture enhancement (TE) module is utilized to focus on the local pattern. The enhanced features from BE and TE, which complement each other, are combined to obtain the final prediction. FindNet performs competently on various conditions of COD, including slightly clear boundaries but very similar textures, fuzzy boundaries but slightly differentiated textures, and simultaneous fuzzy boundaries and textures. Experimental results exhibit clear improvements of FindNet over fifteen state-of-the-art methods on four benchmark datasets, in terms of detection accuracy and boundary clearness. The code will be publicly released.

Camouflaged Object Detection (COD) aims to identify objects that blend seamlessly into their surroundings. The inherent visual complexity of camouflaged objects, including their low contrast with the background, diverse textures, and subtle appearance variations, often obscures semantic cues, making accurate segmentation highly challenging. Existing methods primarily rely on visual features, which are insufficient to handle the variability and intricacy of camouflaged objects, leading to unstable object perception capability and ambiguous segmentation results. To tackle these limitations, we introduce a novel COD task, class-guided camouflaged object detection (CGCOD), which extends the conventional COD task by incorporating object-specific class knowledge to enhance detection robustness and accuracy. To facilitate this task, we present a new dataset, CamoClass, comprising camouflaged objects with class annotations. Furthermore, we propose a multi-stage framework, CGNet, which incorporates a plug-and-play class prompt generator and a simple yet effective class-guided detector. This establishes a new paradigm for COD, bridging the gap between contextual understanding and class-guided detection. Extensive experimental results demonstrate the effectiveness of our flexible framework in improving the performance of proposed and existing detectors by leveraging class-level textual information. The Camoclass dataset and the corresponding source code will be made publicly available upon acceptance at: https://github.com/bbdjj/CGCOD.

Enhancing camouflaged object detection through contrastive learning and data augmentation techniques

Moving object detection and tracking have various applications, including surveillance, anomaly detection, vehicle navigation, etc. The literature on object detection and tracking is rich enough, and there exist several essential survey papers. However, the research on camouflage object detection and tracking is limited due to the complexity of the problem. Existing work on this problem has been done based on either biological characteristics of the camouflaged objects or computer vision techniques. In this paper, we review the existing camouflaged object detection and tracking techniques using computer vision algorithms from the theoretical point of view. This paper also addresses several issues of interest as well as future research direction in this area. We hope this paper will help the reader to learn the recent advances in camouflaged object detection and tracking.

Camouflaged object detection via cross-level refinement and interaction network

Bi-RRNet: Bi-level recurrent refinement network for camouflaged object detection

Camouflaged object detection (COD) aims to accurately detect objects that blend in with their backgrounds. While deep learning-based methods have significantly improved detection accuracy, the persistent similarity in chromatic and textural characteristics between camouflaged objects and their environments continues to pose substantial challenges, particularly in complex scenarios. Existing COD methods still encounter two primary challenges: (1) the difficulty in effectively fusing detailed features with semantic representations, leading to suboptimal balance between local and global features in the fused features; (2) progressive information dilution in conventional top-down decoder architectures, resulting in imprecise boundary localization. To address these dual challenges of multi-scale feature enhancement and semantic information dilution, we propose a D ual-Branch C ross-Layer I nformation F low Network (DCIFNet) for camouflaged object detection in complex scenes. Specifically, our framework integrates a hybrid CNN-ViT architecture for parallel extraction of localized patterns and global contextual features. Subsequently, a global local fusion (GLF) module is employed to iteratively and adaptively fuse the global and local features at each stage using gating weights. Additionally, a novel Dense Connection Decoder (DCD) module is designed to mitigate the information dilution problem associated with traditional top-down decoders. Extensive experiments show that DCIFNet outperforms other mainstream COD models in most performance metrics on four widely used benchmark datasets. On the COD10K dataset, compared with the latest SOTA work of PRNet, DCIFNet improves the metrics of \({S_{\alpha}}\) , \({E_{\phi}}\) , and \(F_{\beta}^{w}\) by 0.11 \(\%\) , 0.11 \(\%\) , and 1.38 \(\%\) , respectively, demonstrating a significant performance enhancement. The code is available https://github.com/WateverOk/DCIFNet .

Camouflaged object detection (COD) is a challenging task that aims to identify objects that blend into their backgrounds. This has various applications in computer vision, including military surveillance, animal behavior analysis, and image editing. However, existing COD methods often face issues of ambiguous boundaries and inaccurate localization due to ineffective feature fusion and boundary guidance. To address these challenges, we propose a new hierarchical synergistic prediction framework, named detail perception synergistic network (DPSNet), which leverages an incremental strategy and a novel detail perception loss (DP Loss) for accurate COD. Specifically, our DPSNet consists of a baseline network and an enhanced network, generating preliminary and fine-grained predictions, respectively. To adapt to the morphological variations of camouflaged objects, we design a morphological awareness adaptation module (MAAM) that adaptively fuses inconsistent morphological and texture features. To enhance the contextual semantic feature representations and obtain clear predictions, we propose a semantic aggregation enhancement module (SAEM) that aggregates contextual semantic features with a deep aggregate group with different receptive fields. Furthermore, to guide the network in learning boundary information for more accurate predictions, we present a DP Loss that balances global information and hard samples. Without any additional tricks, extensive experiments demonstrate that our proposed DPSNet achieves real-time performance and significantly surpasses 37 state-of-the-art (SOTA) methods across four standard metrics on four widely used benchmark datasets. The code will be available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/fxle/DPSNet</uri>.

Camouflaged object detection (COD) is challenging for both human and computer vision, as targets often blend into the background by sharing similar color, texture, or shape. While many feature enhancement techniques exist, single-view methods tend to overemphasize certain Recognizing that camouflaged objects exhibit different concealment strategies under varying observational perspectives, we propose HUNTNet, a network that establishes a dynamic detection mechanism to decouple target features from RGB images and perform topological decamouflage across multiple homomorphic feature spaces through a unified feature focusing architecture. We adopt PVTv2 as the backbone to extract multi-perspective spatial features. Detail representation is enhanced via a feature module that integrates Dual-Channel Recursive (DCR), Wavelet-Gabor Transform (WGT), and Anisotropic Gradient Responding (AGR), which together improve boundary discrimination and edge contour detection. To further boost performance, the Simplicial Feature Integration (SFI) module recursively fuses multi-layer features, enabling high-resolution focus on target regions. Experiments show that HUNTNet surpasses state-of-the-art methods in both accuracy and generalization, offering a robust solution for COD and improving segmentation in complex scenes. Our code is available at https://github.com/HaolinJi817/HUNTNet.

Camouflaged Object Detection (COD) aims to discover objects that are finely disguised in the environment. Its challenge is that the targets generally have similar textures and colors to their surroundings. In this paper, we propose a novel network, named attention-based neighbor selective aggregation network (ANSA-Net), which can effectively and efficiently detect camouflaged objects. Specifically, our ANSA-Net contains two novel modules, namely, neighbor selective aggregation (NSA) and high-level feature guided attention (HLGA). The NSA is designed to locate concealed targets by fusing multi-scale features adaptively. Furthermore, the HLGA is designed to improve the semantic information of features by employing attention maps derived from high-level features. Experiments show that ANSA-Net exhibits relatively accurate detection performance on four COD datasets, outperforming existing state-of-the-art methods.

Precisely locating the edges of objects camouflaged as background is an exceedingly complicated task. Existing camouflaged object detection (COD) methods can accurately pinpoint the positions of objects, but the edges remain ambiguous. To tackle this challenge, we propose a multi-stage refinement framework to locate the precise edges of camouflaged objects coarse-to-fine, coined SAE, which integrates “shrink” and “expose” in each stage. In this paper, we transform the process by which humans capture object edges into a multi-stage shrinking process. Firstly, we obtain the edge probability distribution by subtracting a negative prediction result from a positive prediction result, thus proposing the positive-negative edge shrink module. The edge probability distribution from the previous stage is then explicitly propagated to the current stage to enhance the edge texture details. Second, we introduce edge exposure attention to expose significant edges, which also reflects the impact of the previous stage’s edge state and edge probability distribution on the current stage’s edge state. Finally, to eliminate state transition and edge deformation anomalies, we propose the abnormal localization restriction to ensure the proper execution of “shrink” and “expose”. Experimental results show that our model significantly outperforms recent SOTA methods on three COD benchmarks.

Local to global purification strategy to realize collaborative camouflaged object detection

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.