Camouflaged Object Detection Research Articles

Feature-aware and iterative refinement network for camouflaged object detection

Feature-aware and iterative refinement network for camouflaged object detection

CoFiNet: Unveiling camouflaged objects with multi-scale finesse

Camouflaged Object Detection (COD) is a critical aspect of computer vision aimed at identifying concealed objects, with applications spanning military, industrial, medical and monitoring domains. To address the problem of poor detail segmentation effect, we introduce a novel method for camouflaged object detection, named CoFiNet. Our approach primarily focuses on multi-scale feature fusion and extraction, with special attention to the model’s segmentation effectiveness for detailed features, enhancing its ability to effectively detect camouflaged objects. CoFiNet adopts a coarse-to-fine strategy. A multi-scale feature integration module is laveraged to enhance the model’s capability of fusing context feature. A multi-activation selective kernel module is leveraged to grant the model the ability to autonomously alter its receptive field, enabling it to selectively choose an appropriate receptive field for camouflaged objects of different sizes. During mask generation, we employ the dual-mask strategy for image segmentation, separating the reconstruction of coarse and fine masks, which significantly enhances the model’s learning capacity for details. Comprehensive experiments were conducted on four different datasets, demonstrating that CoFiNet achieves state-of-the-art performance across all datasets. The experiment results of CoFiNet underscore its effectiveness in camouflaged object detection and highlight its potential in various practical application scenarios.

A three-stage model for camouflaged object detection

Camouflaged objects are typically assimilated into their backgrounds and exhibit fuzzy boundaries. The complex environmental conditions and the high intrinsic similarity between camouflaged targets and their surroundings pose significant challenges in accurately locating and segmenting these objects in their entirety. While existing methods have demonstrated remarkable performance in various real-world scenarios, they still face limitations when confronted with difficult cases, such as small targets, thin structures, and indistinct boundaries. Drawing inspiration from human visual perception when observing images containing camouflaged objects, we propose a three-stage model that enables coarse-to-fine segmentation in a single iteration. Specifically, our model employs three decoders to sequentially process subsampled features, cropped features, and high-resolution original features. This proposed approach not only reduces computational overhead but also mitigates interference caused by background noise. Furthermore, considering the significance of multi-scale information, we have designed a multi-scale feature enhancement module that enlarges the receptive field while preserving detailed structural cues. Additionally, a boundary enhancement module has been developed to enhance performance by leveraging boundary information. Subsequently, a mask-guided fusion module is proposed to generate fine-grained results by integrating coarse prediction maps with high-resolution feature maps. Our network shows superior performance without introducing unnecessary complexities. Upon acceptance of the paper, the source code will be made publicly available at https://github.com/clelouch/TSNet.

When CNN meet with ViT: decision-level feature fusion for camouflaged object detection

When CNN meet with ViT: decision-level feature fusion for camouflaged object detection

FRBNet: feature-iterative reinforcement and boundary-directed network for camouflaged object detection

FRBNet: feature-iterative reinforcement and boundary-directed network for camouflaged object detection

SPANet: Spatial perceptual activation network for camouflaged object detection

AbstractCamouflaged object detection (COD) aims to segment objects embedded in the environment from the background. Most existing methods are easily affected by background interference in cluttered environments and cannot accurately locate camouflage areas, resulting in over‐segmentation or incomplete segmentation structures. To effectively improve the performance of COD, we propose a spatial perceptual activation network (SPANet). SPANet extracts the spatial positional relationship between each object in the scene by activating spatial perception and uses it as global information to guide segmentation. It mainly consists of three modules: perceptual activation module (PAM), feature inference module (FIM), and interaction recovery module (IRM). Specifically, the authors design a PAM to model the positional relationship between the camouflaged object and the surrounding environment to obtain semantic correlation information. Then, a FIM that can effectively combine correlation information to suppress background interference and re‐encode to generate multi‐scale features is proposed. In addition, to further fuse multi‐scale features, an IRM to mine the complementary information and differences between features at different scales is designed. Extensive experimental results on four widely used benchmark datasets (i.e. CAMO, CHAMELEON, COD10K, and NC4K) show that the authors’ method outperforms 13 state‐of‐the‐art methods.

Denoising Diffusion Implicit Model for Camouflaged Object Detection

Camouflaged object detection (COD) is a challenging task that involves identifying objects that closely resemble their background. In order to detect camouflaged objects more accurately, we propose a diffusion model for the COD network called DMNet. DMNet formulates COD as a denoising diffusion process from noisy boxes to prediction boxes. During the training stage, random boxes diffuse from ground-truth boxes, and DMNet learns to reverse this process. In the sampling stage, DMNet progressively refines random boxes to prediction boxes. In addition, due to the camouflaged object’s blurred appearance and the low contrast between it and the background, the feature extraction stage of the network is challenging. Firstly, we proposed a parallel fusion module (PFM) to enhance the information extracted from the backbone. Then, we designed a progressive feature pyramid network (PFPN) for feature fusion, in which the upsample adaptive spatial fusion module (UAF) balances the different feature information by assigning weights to different layers. Finally, a location refinement module (LRM) is constructed to make DMNet pay attention to the boundary details. We compared DMNet with other classical object-detection models on the COD10K dataset. Experimental results indicated that DMNet outperformed others, achieving optimal effects across six evaluation metrics and significantly enhancing detection accuracy.

Contextual feature fusion and refinement network for camouflaged object detection

Contextual feature fusion and refinement network for camouflaged object detection

A survey on deep learning-based camouflaged object detection

A survey on deep learning-based camouflaged object detection

A Cross-Level Iterative Subtraction Network for Camouflaged Object Detection

Camouflaged object detection (COD) is a challenging task, aimed at segmenting objects that are similar in color and texture to their background. Sufficient multi-scale feature fusion is crucial for accurately segmenting object regions. However, most methods usually focus on information compensation, overlooking the difference between features, which is important for distinguishing the object from the background. To this end, we propose the cross-level iterative subtraction network (CISNet), which integrates information from cross-layer features and enhances details through iteration mechanisms. CISNet involves a cross-level iterative structure (CIS) for feature complementarity, where texture information is used to enrich high-level features and semantic information is used to enhance low-level features. In particular, we present a multi-scale strip convolution subtraction (MSCSub) module within CIS to extract difference information between cross-level features and fuse multi-scale features, which improves the feature representation and guides accurate segmentation. Furthermore, an enhanced guided attention (EGA) module is presented to refine features by deeply mining local context information and capturing a broader range of relationships between different feature maps in a top-down manner. Extensive experiments conducted on four benchmark datasets demonstrate that our model outperforms the state-of-the-art COD models in all evaluation metrics.

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.

Dual cross perception network with texture and boundary guidance for camouflaged object detection

Camouflaged object detection (COD) is a task needs to segment objects that subtly blend into their surroundings effectively. Edge and texture information of the objects can be utilized to reveal the edges of camouflaged objects and detect texture differences between camouflaged objects and the surrounding environment. However, existing methods often fail to fully exploit the advantages of these two types of information. Considering this, our paper proposes an innovative Dual Cross Perception Network (DCPNet) with texture and boundary guidance for camouflaged object detection. DCPNet consists of two essential modules, namely Dual Cross Fusion Module (DCFM) and the Subgroup Aggregation Module (SAM). DCFM utilizes attention techniques to emphasize the information that exists in edges and textures by cross-fusing features of the edge, texture, and basic RGB image, which strengthens the ability to capture edge information and texture details in image analysis. SAM gives varied weights to low-level and high-level features in order to enhance the comprehension of objects and scenes of various sizes. Several experiments have demonstrated that DCPNet outperforms 13 state-of-the-art methods on four widely used assessment metrics.

Camouflaged Object Detection via Dual-branch Fusion and Dual Self-similarity constraints

Camouflaged Object Detection (COD) remains a challenging task due to the inherent difficulty in distinguishing concealed objects from their surroundings, primarily owing to their high visual similarity. Current research efforts have been insufficient in distinguishing the similarity differences between camouflaged objects and their corresponding backgrounds, leading to suboptimal performance, particularly when locating small-scale objects. To solve this issue, we introduce a novel Dual-branch Fusion and Dual Self-similarity Network (DSNet) comprising three modules. The first, a dual-branch fusion, mimics human observation of camouflaged objects and extracts multi-angle information. Dual-branch features are then decoded using a symmetric joint decoder module with channel interaction via multi-stage inter-group interaction. Inspired by natural organisms’ self-similarity, the self-similarity constraint module employs global and mutual constraints to identify subtle foreground-background differences. DSNet shows superior performance in experiments, and the constraint module can enhance other models as a plug-and-play component, further boosting overall performance.

Boundary enhancement and refinement network for camouflaged object detection

Boundary enhancement and refinement network for camouflaged object detection

Mscnet: Mask stepwise calibration network for camouflaged object detection

Mscnet: Mask stepwise calibration network for camouflaged object detection

EHSGNet: A novel edge and high-level semantic guided network for CFRP subsurface defects detection

Infrared thermography, due to its fast detection speed, low cost, and intuitive results, is widely used in carbon fiber reinforced plastics (CFRP) materials detection. However, CFRP infrared image noise and lower resolution lead to intrinsic similarly and edge disruption, making it impossible to obtain satisfactory results. Additionally, the current methods require a lot of preprocessing and post-processing operations. Since camouflaged object detection has achieved good results in solving intrinsic similarly and edge disruption. Therefore, this paper proposes an edge and high-level semantic guided network (EHSGNet) based on camouflaged object detection for infrared thermography CFRP subsurface defect detection, achieving end-to-end detection of CFRP. This paper also constructed a dataset containing CFRP samples with both natural defects and artificially simulated internal defects to validate our method. Since the temperature change is achieved in the temperature-controlled chamber by means of hot air heating, it can heat the sample uniformly and improve the quality of CFRP infrared images, so our experiments will be carried out in the temperature-controlled chamber. The experiments were conducted on CFRP specimens with different sizes, shapes and depths of artificial defects. The experimental results demonstrate that the proposed EHSGNet outperforms current approaches significantly on our dataset, with precision (PRC) and recall (RCL) reaching 94.3% and 93.1%, respectively.

Efficient Camouflaged Object Detection Network Based on Global Localization Perception and Local Guidance Refinement

Efficient Camouflaged Object Detection Network Based on Global Localization Perception and Local Guidance Refinement

DASOD: Detail-aware salient object detection

Salient object detection (SOD) is a challenging task in computer vision. Current SOD approaches have made significant progress, but they fail in challenging scenarios. This paper categorizes the existing challenges in SOD into four groups: images with complex backgrounds, low contrast, transparent objects, and occluded objects. Then, the Detail-Aware Salient Object Detection (DASOD) method is proposed to address these challenging scenarios. To the best of our knowledge, DASOD is the first method that considers mentioned challenging situations together and detects salient objects in images through camouflaged object detection (COD). DASOD has two main stages: 1) pseudo-mask generation and 2) refinement. It first generates a pseudo-mask using the body label and super-resolution technique, then refines the pseudo-mask with the detail map produced by the pseudo-edge generator to detect salient objects with clear boundaries in the pseudo-mask refinement module. This module quantifies uncertainty using the conditional normalizing flows (cFlow) based conditional variational auto-encoder (cVAE) to generate reliable results. Extensive experiments are conducted on six datasets, and the performance of DASOD is compared with 18 state-of-the-art methods. The results demonstrate that DASOD outperforms its competitors and can accurately detect the salient objects when the image background is cluttered and the contrast between foreground and background is low. Also, it effectively detects the transparent and occluded objects in images. It achieves MAE rates of 0.052, 0.033, 0.027, 0.024, 0.059, and 0.088 on DUT-OMRON, DUTS-TE, ECSSD, HKU-IS, PASCAL-S, and SCAS datasets, respectively. All the implementation source codes and results are available at: https://github.com/BaharehAsheghi/DASOD.

Two guidance joint network based on coarse map and edge map for camouflaged object detection

Two guidance joint network based on coarse map and edge map for camouflaged object detection

SDRNet: Camouflaged object detection with independent reconstruction of structure and detail

The simultaneous reconstruction of structure and detail is a prevalent strategy in camouflaged object detection. However, the reconstruction features required for structure and detail exhibit disparities, a facet overlooked in existing methods. Therefore, we present a novel methodology, termed SDRNet, which employs a dual-branch approach for the independent reconstruction of structure and detail, aiming to discern camouflaged targets and their edges. Specifically, we propose a decomposition block to segregate encoded features into distinct structure and detail components. Furthermore, structure enhancement block and detail enhancement block are proposed as feature enhancement methods to boost the capacity of structure and detail information. Subsequently, the introduced structure fusion block and detail fusion block progressively amalgamate the enhanced features. Additionally, the shared feature block is designed to serve as a bridge for the interaction between structure and detail information. Experimental results demonstrate that SDRNet outperforms existing state-of-the-art methods significantly on benchmark datasets. Our code is available at https://github.com/whyandbecause/SDRNet/.