Camouflaged Object Detection Research Articles

Deep Texture-Aware Features for Camouflaged Object Detection

Camouflaged object detection is a challenging task that aims to identify objects having similar texture to the surroundings. This paper presents to amplify the subtle texture difference between camouflaged objects and the background for camouflaged object detection by formulating multiple texture-aware refinement modules to learn the texture-aware features in a deep convolutional neural network. The texture-aware refinement module computes the biased co-variance matrices of feature responses to extract the texture information, adopts an affinity loss to learn a set of parameter maps that help to separate the texture between camouflaged objects and the background, and leverages a boundary-consistency loss to explore the structures of object details. We evaluate our network on the benchmark datasets for camouflaged object detection both qualitatively and quantitatively. Experimental results show that our approach outperforms various state-of-the-art methods by a large margin.

Camouflaged Object Detection Based on Ternary Cascade Perception

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.

Deep Gradient Learning for Efficient Camouflaged Object Detection

This paper introduces deep gradient network (DGNet), a novel deep framework that exploits object gradient supervision for camouflaged object detection (COD). It decouples the task into two connected branches, i.e., a context and a texture encoder. The essential connection is the gradient-induced transition, representing a soft grouping between context and texture features. Benefiting from the simple but efficient framework, DGNet outperforms existing state-of-the-art COD models by a large margin. Notably, our efficient version, DGNet-S, runs in real-time (80 fps) and achieves comparable results to the cutting-edge model JCSOD-CVPR21 with only 6.82% parameters. The application results also show that the proposed DGNet performs well in the polyp segmentation, defect detection, and transparent object segmentation tasks. The code will be made available at https://github.com/GewelsJI/DGNet.

Specificity-preserving RGB-D saliency detection

Salient object detection (SOD) in RGB and depth images has attracted increasing research interest. Existing RGB-D SOD models usually adopt fusion strategies to learn a shared representation from RGB and depth modalities, while few methods explicitly consider how to preserve modality-specific characteristics. In this study, we propose a novel framework, the specificity-preserving network (SPNet), which improves SOD performance by exploring both the shared information and modality-specific properties. Specifically, we use two modality-specific networks and a shared learning network to generate individual and shared saliency prediction maps. To effectively fuse cross-modal features in the shared learning network, we propose a cross-enhanced integration module (CIM) and propagate the fused feature to the next layer to integrate cross-level information. Moreover, to capture rich complementary multi-modal information to boost SOD performance, we use a multi-modal feature aggregation (MFA) module to integrate the modality-specific features from each individual decoder into the shared decoder. By using skip connections between encoder and decoder layers, hierarchical features can be fully combined. Extensive experiments demonstrate that our SPNet outperforms cutting-edge approaches on six popular RGB-D SOD and three camouflaged object detection benchmarks. The project is publicly available at https://github.com/taozh2017/SPNet.

Nowhere to Disguise: Spot Camouflaged Objects via Saliency Attribute Transfer.

Both salient object detection (SOD) and camouflaged object detection (COD) are typical object segmentation tasks. They are intuitively contradictory, but are intrinsically related. In this paper, we explore the relationship between SOD and COD, and then borrow successful SOD models to detect camouflaged objects to save the design cost of COD models. The core insight is that both SOD and COD leverage two aspects of information: object semantic representations for distinguishing object and background, and context attributes that decide object category. Specifically, we start by decoupling context attributes and object semantic representations from both SOD and COD datasets through designing a novel decoupling framework with triple measure constraints. Then, we transfer saliency context attributes to the camouflaged images through introducing an attribute transfer network. The generated weakly camouflaged images can bridge the context attribute gap between SOD and COD, thereby improving the SOD models' performances on COD datasets. Comprehensive experiments on three widely-used COD datasets verify the ability of the proposed method. Code and model are available at: https://github.com/wdzhao123/SAT.

Zero-Shot Camouflaged Object Detection.

The goal of Camouflaged object detection (COD) is to detect objects that are visually embedded in their surroundings. Existing COD methods only focus on detecting camouflaged objects from seen classes, while they suffer from performance degradation to detect unseen classes. However, in a real-world scenario, collecting sufficient data for seen classes is extremely difficult and labeling them requires high professional skills, thereby making these COD methods not applicable. In this paper, we propose a new zero-shot COD framework (termed as ZSCOD), which can effectively detect the never unseen classes. Specifically, our framework includes a Dynamic Graph Searching Network (DGSNet) and a Camouflaged Visual Reasoning Generator (CVRG). In details, DGSNet is proposed to adaptively capture more edge details for boosting the COD performance. CVRG is utilized to produce pseudo-features that are closer to the real features of the seen camouflaged objects, which can transfer knowledge from seen classes to unseen classes to help detect unseen objects. Besides, our graph reasoning is built on a dynamic searching strategy, which can pay more attention to the boundaries of objects for reducing the influences of background. More importantly, we construct the first zero-shot COD benchmark based on the COD10K dataset. Experimental results on public datasets show that our ZSCOD not only detects the camouflaged object of unseen classes but also achieves state-of-the-art performance in detecting seen classes.

FSNet: Focus Scanning Network for Camouflaged Object Detection

Camouflaged object detection (COD) aims to discover objects that blend in with the background due to similar colors or textures, etc. Existing deep learning methods do not systematically illustrate the key tasks in COD, which seriously hinders the improvement of its performance. In this paper, we introduce the concept of focus areas that represent some regions containing discernable colors or textures, and develop a two-stage focus scanning network for camouflaged object detection. Specifically, a novel encoder-decoder module is first designed to determine a region where the focus areas may appear. In this process, a multi-layer Swin transformer is deployed to encode global context information between the object and the background, and a novel cross-connection decoder is proposed to fuse cross-layer textures or semantics. Then, we utilize the multi-scale dilated convolution to obtain discriminative features with different scales in focus areas. Meanwhile, the dynamic difficulty aware loss is designed to guide the network paying more attention to structural details. Extensive experimental results on the benchmarks, including CAMO, CHAMELEON, COD10K, and NC4K, illustrate that the proposed method performs favorably against other state-of-the-art methods.

MGL: Mutual Graph Learning for Camouflaged Object Detection.

Camouflaged object detection, which aims to detect/segment the object(s) that blend in with their surrounding, remains challenging for deep models due to the intrinsic similarities between foreground objects and background surroundings. Ideally, an effective model should be capable of finding valuable clues from the given scene and integrating them into a joint learning framework to co-enhance the representation. Inspired by this observation, we propose a novel Mutual Graph Learning (MGL) model by shifting the conventional perspective of mutual learning from regular grids to graph domain. Specifically, an image is decoupled by MGL into two task-specific feature maps - one for finding the rough location of the target and the other for capturing its accurate boundary details. Then, the mutual benefits can be fully exploited by reasoning their high-order relations through graphs recurrently. It should be noted that our method is different from most mutual learning models that model all between-task interactions with the use of a shared function. To increase information interactions, MGL is built with typed functions for dealing with different complementary relations. To overcome the accuracy loss caused by interpolation to higher resolution and the computational redundancy resulting from recurrent learning, the S-MGL is equipped with a multi-source attention contextual recovery module, called R-MGL_v2, which uses the pixel feature information iteratively. Experiments on challenging datasets, including CHAMELEON, CAMO, COD10K, and NC4K demonstrate the effectiveness of our MGL with superior performance to existing state-of-the-art methods. The code can be found at https://github.com/fanyang587/MGL.

PoolNet+: Exploring the Potential of Pooling for Salient Object Detection.

We explore the potential of pooling techniques on the task of salient object detection by expanding its role in convolutional neural networks. In general, two pooling-based modules are proposed. A global guidance module (GGM) is first built based on the bottom-up pathway of the U-shape architecture, which aims to guide the location information of the potential salient objects into layers at different feature levels. A feature aggregation module (FAM) is further designed to seamlessly fuse the coarse-level semantic information with the fine-level features in the top-down pathway. We can progressively refine the high-level semantic features with these two modules and obtain detail enriched saliency maps. Experimental results show that our proposed approach can locate the salient objects more accurately with sharpened details and substantially improve the performance compared with the existing state-of-the-art methods. Besides, our approach is fast and can run at a speed of 53 FPS when processing a 300 ×400 image. To make our approach better applied to mobile applications, we take MobileNetV2 as our backbone and re-tailor the structure of our pooling-based modules. Our mobile version model achieves a running speed of 66 FPS yet still performs better than most existing state-of-the-art methods. To verify the generalization ability of the proposed method, we apply it to the edge detection, RGB-D salient object detection, and camouflaged object detection tasks, and our method achieves better results than the corresponding state-of-the-art methods of these three tasks. Code can be found at http://mmcheng.net/poolnet/.

Mask-and-Edge Co-Guided Separable Network for Camouflaged Object Detection

Mask-and-Edge Co-Guided Separable Network for Camouflaged Object Detection

Predictive Uncertainty Estimation for Camouflaged Object Detection.

Uncertainty is inherent in machine learning methods, especially those for camouflaged object detection aiming to finely segment the objects concealed in background. The strong enquote center bias of the training dataset leads to models of poor generalization ability as the models learn to find camouflaged objects around image center, which we define as enquote model bias. Further, due to the similar appearance of camouflaged object and its surroundings, it is difficult to label the accurate scope of the camouflaged object, especially along object boundaries, which we term as enquote data bias. To effectively model the two types of biases, we resort to uncertainty estimation and introduce predictive uncertainty estimation technique, which is the sum of model uncertainty and data uncertainty, to estimate the two types of biases simultaneously. Specifically, we present a predictive uncertainty estimation network (PUENet) that consists of a Bayesian conditional variational auto-encoder (BCVAE) to achieve predictive uncertainty estimation, and a predictive uncertainty approximation (PUA) module to avoid the expensive sampling process at test-time. Experimental results show that our PUENet achieves both highly accurate prediction, and reliable uncertainty estimation representing the biases within both model parameters and the datasets.

MAGNet: A Camouflaged Object Detection Network Simulating the Observation Effect of a Magnifier.

In recent years, protecting important objects by simulating animal camouflage has been widely employed in many fields. Therefore, camouflaged object detection (COD) technology has emerged. COD is more difficult to achieve than traditional object detection techniques due to the high degree of fusion of objects camouflaged with the background. In this paper, we strive to more accurately and efficiently identify camouflaged objects. Inspired by the use of magnifiers to search for hidden objects in pictures, we propose a COD network that simulates the observation effect of a magnifier called the MAGnifier Network (MAGNet). Specifically, our MAGNet contains two parallel modules: the ergodic magnification module (EMM) and the attention focus module (AFM). The EMM is designed to mimic the process of a magnifier enlarging an image, and AFM is used to simulate the observation process in which human attention is highly focused on a particular region. The two sets of output camouflaged object maps were merged to simulate the observation of an object by a magnifier. In addition, a weighted key point area perception loss function, which is more applicable to COD, was designed based on two modules to give greater attention to the camouflaged object. Extensive experiments demonstrate that compared with 19 cutting-edge detection models, MAGNet can achieve the best comprehensive effect on eight evaluation metrics in the public COD dataset. Additionally, compared to other COD methods, MAGNet has lower computational complexity and faster segmentation. We also validated the model's generalization ability on a military camouflaged object dataset constructed in-house. Finally, we experimentally explored some extended applications of COD.

Survey of Camouflaged Object Detection Based on Deep Learning

Survey of Camouflaged Object Detection Based on Deep Learning

Rethinking Camouflaged Object Detection: Models and Datasets

Camouflaged object detection (COD) is an emerging visual detection task, which aims to locate and distinguish the disguised target in complex backgrounds by imitating the human visual detection system. Recently, COD has attracted increasing attention in computer vision, and a few models of camouflaged object detection have been successfully explored. However, most existing works primarily focus on modeling camouflaged object detection over in-depth analyzing existing COD structures. To the best of our knowledge, a systematic review for COD has not been publicly reported, especially for recently proposed deep learning-based COD models. To make up this vacancy, we firstly proposed a comprehensive review on both COD models and public benchmark datasets and provide potential directions for future COD studies. Specifically, we conduct a comprehensive summary of 39 existing COD models from 1998 to 2021. And then, to facilitate subsequent research on COD, we classify the existing structures into two categories, 27 traditional handcrafted feature-based structures and 12 structures based on deep learning. In addition, we further group traditional handcrafted feature-based structures into six sub-classes based on the detection mechanism: texture, color, motion, intensity, optical flow, and multi-modal fusion. Furthermore, we take an in-depth analysis of the deep learning-based structure based on both detection motivation and detection performance and evaluate the performance of each structure. Moreover, we sum up four widely used COD datasets and describe the details of each one. Finally, we also discuss the limitations of COD and the corresponding solutions to improve detection accuracy. We still mention the relevant applications of camouflaged object detection and its future research directions to promote the development of camouflaged object detection.

EINet: camouflaged object detection with pyramid vision transformer

Camouflaged object detection (COD) is a new computer vision challenge for locating and identifying camouflaged objects in complex situations. Camouflaged objects are more similar to their surroundings than conventional objects, and their appearance in terms of size and shape is also considerably different, making accurate identification of the COD tasks difficult. As a result, we propose an enhanced identification network (EINet) to strengthen the COD task’s identification capabilities. First, the pyramid vision transformer is used as an encoder for extracting more robust multiscale features. Second, the multiple texture refinement modules are exploited to refine the multiscale features. Third, an improved neighbor and hop connection decoder is designed to produce a coarse estimation map for guiding the detailed identification of camouflaged objects backward. Finally, numerous new reverse criss-cross block attention modules that gradually recognize fine-grained features at various scales is designed to allow for the accurate recognition of camouflaged objects. Extensive experiments have been conducted on four benchmarked datasets of camouflaged objects. The results of the experiments reveal that our EINet is a powerful COD model that outperforms current state-of-the-art models.

Satisfaction of Search Can Be Ameliorated by Perceptual Learning: A Proof-of-Principle Study.

When searching a visual image that contains multiple target objects of interest, human subjects often show a satisfaction of search (SOS) effect, whereby if the subjects find one target, they are less likely to find additional targets in the image. Reducing SOS or, equivalently, subsequent search miss (SSM), is of great significance in many real-world situations where it is of paramount importance to find all targets in a given image, not just one. However, studies have shown that even highly trained and experienced subjects, such as expert radiologists, are subject to SOS. Here, using the detection of camouflaged objects (or camouflage-breaking) as an illustrative case, we demonstrate that when naïve subjects are trained to detect camouflaged objects more effectively, it has the side effect of reducing subjects’ SOS. We tested subjects in the SOS task before and after they were trained in camouflage-breaking. During SOS testing, subjects viewed naturalistic scenes that contained zero, one, or two targets, depending on the image. As expected, before camouflage-training, subjects showed a strong SOS effect, whereby if they had found a target with relatively high visual saliency in a given image, they were less likely to have also found a lower-saliency target when one existed in the image. Subjects were then trained in the camouflage-breaking task to criterion using non-SOS images, i.e., camouflage images that contained zero or one target. Surprisingly, the trained subjects no longer showed significant levels of SOS. This reduction was specific to the particular background texture in which the subjects received camouflage training; subjects continued to show significant SOS when tested using a different background texture in which they did not receive camouflage training. A separate experiment showed that the reduction in SOS was not attributable to non-specific exposure or practice effects. Together, our results demonstrate that perceptual expertise can, in principle, reduce SOS, even when the perceptual training does not specifically target SOS reduction.

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.

Guided multi-scale refinement network for camouflaged object detection.

The purpose of camouflaged object detection (COD) is to identify the hidden camouflaged object in an input image. Compared with other binary segmentation tasks like salient object detection, COD needs to deal with more complex scenes, such as low contrast, similar foreground and background. In this work, we proposed a novel guided multi-scale refinement network for COD. Specifically, we first design a global perception module for coarse localization by stacking multi-scale residual block on the top of the backbone in a recurrent manner. Then, we propose the guided multi-scale refinement module to refine such initial prediction progressively, which is combined with multi-level side-output features in a prediction-to-feature fusion strategy. By plugging into side-output features for multi-scale guidance, the missing object parts and false detection can be well remedied. Experimental results show that our proposed network can more accurately locate the camouflaged object and salient object with sharpened details than existing state-of-the-art approaches. In addition, our model is also very efficient and compact, which enables potential real-world applications.

TPRNet: camouflaged object detection via transformer-induced progressive refinement network

TPRNet: camouflaged object detection via transformer-induced progressive refinement network

I Can Find You! Boundary-Guided Separated Attention Network for Camouflaged Object Detection

Can you find me? By simulating how humans to discover the so-called 'perfectly'-camouflaged object, we present a novel boundary-guided separated attention network (call BSA-Net). Beyond the existing camouflaged object detection (COD) wisdom, BSA-Net utilizes two-stream separated attention modules to highlight the separator (or say the camouflaged object's boundary) between an image's background and foreground: the reverse attention stream helps erase the camouflaged object's interior to focus on the background, while the normal attention stream recovers the interior and thus pay more attention to the foreground; and both streams are followed by a boundary guider module and combined to strengthen the understanding of boundary. The core design of such separated attention is motivated by the COD procedure of humans: find the subtle difference between the foreground and background to delineate the boundary of a camouflaged object, then the boundary can help further enhance the COD accuracy. We validate on three benchmark datasets that the proposed BSA-Net is very beneficial to detect camouflaged objects with the blurred boundaries and similar colors/patterns with their backgrounds. Extensive results exhibit very clear COD improvements on our BSA-Net over sixteen SOTAs.