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Progressive Region-to-Boundary Exploration Network for camouflaged object detection

IEEE TRANSACTIONS ON MULTIMEDIA 2025年 27卷 236-248页

作者： Yue, Guanghui Wu, Shangjie Zhou, Tianwei Li, Gang Du, Jie Luo, Yu Jiang, Qiuping Shenzhen Univ Sch Biomed Engn Guangdong Key Lab Biomed Measurements & Ultrasound Natl Reg Key Technol Engn Lab Med UltrasoundMed S Shenzhen 518060 Peoples R China Shenzhen Univ Marshall Lab Biomed Engn Shenzhen 518060 Peoples R China Shenzhen Univ Coll Management Shenzhen 518060 Peoples R China Baicheng Normal Univ Sch Mech & Control Engn Baicheng 137000 Peoples R China Guangdong Univ Technol Sch Comp Sci Guangzhou 510006 Peoples R China Ningbo Univ Fac Informat Sci & Engn Ningbo 315211 Peoples R China

camouflaged object detection (COD) aims to segment targeted objects that have similar colors, textures, or shapes to their background environment. Due to the limited ability in distinguishing highly similar patterns, existing COD methods usually produce inaccurate predictions, especially around the boundary areas, when coping with complex scenes. This paper proposes a Progressive Region-to-Boundary Exploration Network (PRBE-Net) to accurately detect camouflaged objects. PRBE-Net follows an encoder-decoder framework and includes three key modules. Specifically, firstly, both high-level and low-level features of the encoder are integrated by a region and boundary exploration module to explore their complementary information for extracting the object's coarse region and fine boundary cues simultaneously. Secondly, taking the region cues as the guidance information, a Region Enhancement (RE) module is used to adaptively localize and enhance the region information at each layer of the encoder. Subsequently, considering that camouflaged objects usually have blurry boundaries, a Boundary Refinement (BR) decoder is used after the RE module to better detect the boundary areas with the assistance of boundary cues. Through top-down deep supervision, PRBE-Net can progressively refine the prediction. Extensive experiments on four datasets indicate that our PRBE-Net achieves superior results over 21 state-of-the-art COD methods. Additionally, it also shows good results on polyp segmentation, a COD-related task in the medical field.

关键词： Feature extraction Decoding object detection Shape Biomedical imaging Accuracy Visualization Ultrasonic imaging Search problems Iterative methods camouflaged object detection deep neural network region and boundary exploration region enhancement boundary refinement

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ESNet: An Efficient Skeleton-guided Network for camouflaged object detection

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KNOWLEDGE-BASED SYSTEMS 2025年 311卷

作者： Ren, Peng Bai, Tian Sun, Fuming Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Peoples R China Dalian Minzu Univ Sch Informat & Commun Engn Dalian 116600 Peoples R China

Although most of the existing camouflaged object detection methods achieve significant progress, they still have limitations in the following aspects. Firstly, they usually ignore the internal topological structure of objects. Secondly, they are unable to simultaneously balance performance and efficiency. To address the above issues, we propose an Efficient Skeleton-guided Network (ESNet), which utilizes skeleton information to focus on the geometric structure and extension properties of objects. Specifically, to improve the ability of the network to represent the skeleton information, we design a lightweight skeleton encoder and make skeleton labels to supervise the skeleton features generated by the skeleton encoder. To enhance the ability of the network to perceive the internal structure of objects, we design a Skeleton Guidance Fusion Module (SGFM), which introduces skeleton information into semantic features. Furthermore, we introduce a Part- object Relational Mapping (PORM) component to further improve the micro integrity of camouflaged objects. Extensive experimental results show that ESNet achieves excellent performance, and it has only 10.8M parameters and real-time inference speed (140.3 FPS).

关键词： camouflaged object detection Skeleton information Efficient

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Semantic-spatial guided context propagation network for camouflaged object detection

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APPLIED INTELLIGENCE 2025年第5期55卷 1-15页

作者： Ren, Junchao Zhang, Qiao Kang, Bingbing Zhong, Yuxi He, Min Ge, Yanliang Bi, Hongbo Northeast Petr Univ Sch Elect & Informat Engn Daqing 163318 Peoples R China China Univ Petr East China Sch Comp Sci & Technol Qingdao 266580 Peoples R China Pingdingshan Univ Henan Engn Lab Intelligent Med Internet Things Tec Pingdingshan 467000 Peoples R China China Mobile Commun Grp Heilongjiang Co Ltd Daqing Branch Daqing 163318 Heilongjiang Peoples R China

camouflaged object detection (COD) aims to detect objects that blend in with their surroundings and is a challenging task in computer vision. High-level semantic information and low-level spatial information play important roles in localizing camouflaged objects and reinforcing spatial cues. However, current COD methods directly connect high-level features with low-level features, ignoring the importance of the respective features. In this paper, we design a Semantic-spatial guided Context Propagation Network (SCPNet) to efficiently mine semantic and spatial features while enhancing their feature representations. Firstly, we design a twin positioning module (TPM) to explore semantic cues to accurately locate camouflaged objects. Afterward, we introduce a spatial awareness module (SAM) to mine spatial cues in shallow features deeply. Finally, we develop a context propagation module (CPM) to assign semantic and spatial cues to multi-level features and enhance their feature representations. Experimental results show that our SCPNet outperforms state-of-the-art methods on three challenging datasets. Codes will be made available at https://***/RJC0608/SCPNet.

关键词： camouflaged object detection Deep learning Semantic information Spatial awareness Context propagation

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Polarization-based camouflaged object detection with high-resolution adaptive fusion Network

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 146卷

作者： Wang, Xin Xu, Junfeng Ding, Jiajia Hefei Univ Technol Sch Comp & Informat Hefei 230009 Anhui Peoples R China Intelligent Interconnected Syst Lab Anhui Prov Hefei 230009 Anhui Peoples R China

In comparison to traditional object detection or segmentation tasks, camouflaged object detection (COD) poses greater challenges, as humans are often perplexed or deceived by the inherent similarities between foreground objects and their background surroundings. Polarization information serves as a valuable asset for discerning the attributes of objects with varied characteristics and surface texture. Taking inspiration from the polarization vision systems observed in animals, this study presents the High-Resolution Intensity & Polarization Fusion (HIPF) Net, a high-efficiency cross-modal fusion network that leverages trichromatic intensity and linear orthogonal polarization cues to produce a scene representation that is rich in texture and edge details. Specifically, the Early Adaptive Stokes Fusion (EASF) module maximizes the utilization of information from linear orthogonal polarization images. Subsequently, the Mix-Attention Feature Interaction Module (MAI) is introduced to facilitate complementary interaction among low-level features. Additionally, the Attentional Receptive Field Block (ARFB) enables the model to uncover concealed cues effectively, capturing objects of various sizes. Finally, the Weighted Cross-Level Decoder(WCFD) is designed to dynamically fuse and assign weights to cross-level contextual information for robust detection. Training and extensive validation of our model are performed on the polarization-based dataset as well as non-polarization-based datasets, with experimental results demonstrating that HIPFNet consistently outperforms state-of-the-art methods. Source codes are available at https://***/CVhfut/HIPFNet.

关键词： Polarization vision camouflaged object detection High-resolution Cross-modal & cross-level Fusion

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Frequency-Guided Spatial Adaptation for camouflaged object detection

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IEEE TRANSACTIONS ON MULTIMEDIA 2025年 27卷 72-83页

作者： Zhang, Shizhou Kong, Dexuan Xing, Yinghui Lu, Yue Ran, Lingyan Liang, Guoqiang Wang, Hexu Zhang, Yanning Northwestern Polytech Univ Sch Comp Sci Natl Engn Lab Integrated Aerosp Ground Ocean Big D Xian 710072 Peoples R China Northwestern Polytech Univ Shenzhen Res & Dev Inst Shenzhen 518057 Peoples R China Northwest Univ Sch Informat & Technol Xian 710127 Peoples R China Xijing Univ Xian Key Lab Human Machine Integrat & Control Tech Xian 710123 Peoples R China

camouflaged object detection (COD) aims to segment camouflaged objects which exhibit very similar patterns with the surrounding environment. Recent research works have shown that enhancing the feature representation via the frequency information can greatly alleviate the ambiguity problem between the foreground objects and the background. With the emergence of vision foundation models, like InternImage, Segment Anything Model etc, adapting the pretrained model on COD tasks with a lightweight adapter module shows a novel and promising research direction. Existing adapter modules mainly care about the feature adaptation in the spatial domain. In this paper, we propose a novel frequency-guided spatial adaptation method for COD task. Specifically, we transform the input features of the adapter into frequency domain. By grouping and interacting with frequency components located within non overlapping circles in the spectrogram, different frequency components are dynamically enhanced or weakened, making the intensity of image details and contour features adaptively adjusted. At the same time, the features that are conducive to distinguishing object and background are highlighted, indirectly implying the position and shape of camouflaged object. We conduct extensive experiments on four widely adopted benchmark datasets and the proposed method outperforms 26 state-of-the-art methods with large margins. Code will be released.

关键词： Adaptation models Foundation models Shape Fuses Frequency-domain analysis object detection Transforms Benchmark testing Feature extraction Spectrogram camouflaged object detection frequency-guided pretrained foundation model spatial adaptation

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Skeleton-Boundary-Guided Network for camouflaged object detection

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ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 2025年第3期21卷 1-21页

作者： Niu, Yuzhen Xu, Yeyuan Li, Yuezhou Zhang, Jiabang Chen., Yuzhong Fuzhou Univ Fuzhou Peoples R China Minist Educ Engn Res Ctr BigData Intelligence Fuzhou Peoples R China

camouflaged object detection (COD) aims to resolve the tough issue of accurately segmenting objects hidden in the surroundings. However, the existing methods suffer from two major problems: the incomplete interior and the inaccurate boundary of the object. To address these difficulties, we propose a three-stage skeletonboundary-guided network (SBGNet) for the COD task. Specifically, we design a novel skeleton-boundary label to be complementary to the typical pixel-wise mask annotation, emphasizing the interior skeleton and the boundary of the camouflaged object. Furthermore, the proposed feature guidance module (FGM) leverages the skeleton-boundary feature to guide the model to focus on both the interior and the boundary of the camouflaged object. Besides, we design a bidirectional feature flow path with the information interaction module (IIM) to propagate and integrate the semantic and texture information. Finally, we propose the dual feature distillation module (DFDM) to progressively refine the segmentation results in a fine-grained manner. Comprehensive experiments demonstrate that our SBGNet outperforms 20 state-of-the-art methods on three benchmarks in both qualitative and quantitative comparisons. CCS Concepts: center dot Computing methodologies -> Scene understanding

关键词： camouflaged object detection Skeleton-boundary guidance Bidirectional feature flow path Feature distillation

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Dual region mutual enhancement network for camouflaged object detection

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IMAGE AND VISION COMPUTING 2025年 158卷

作者： Yin, Chao Li, Xiaoqiang Shanghai Univ Sch Comp Engn & Sci Shanghai 200444 Peoples R China

camouflaged object detection (COD) is a promising yet challenging task that aims to segment objects hidden in intricate surroundings. Current methods often struggle with identifying background regions that resemble camouflaged objects, posing a significant challenge. To mitigate this issue, we propose a novel Dual Region Mutual Enhancement Network (DRMENet), which separately extracts camouflaged object and background region features and these branches mutually assist each other to refine their respective region features. Specifically, in the foreground segmentation branch, we utilize the Background-assisted Foreground Region Enhancement (BFRE) subnetwork to enhance camouflaged object region features with background information. BFRE subnetwork consists of two parts: the Background-subtracted Foreground Refinement (BFR) module and the Scale-wise Feature Capturing (SFC) module, where the former obtains corresponding camouflaged object region features through cross-layer refinement with the assistance of background region features, and the latter captures scale-wise features and outputs a side output for region prediction result. Additionally, considering the noise present in low-level visual features, we introduce the Semantic-Guided Refinement (SGR) module, which progressively refines visual features based on enhanced semantic features. Experiments on challenging datasets show DRMENet's superiority over the existing state-of-the-art methods. The source codes will be available at https://***/ycyinchao/DRMENet.

关键词： camouflaged object detection Binary segmentation Feature interaction Feature enhancement Coarse-to-fine refinement

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Spatial Bi-Exploration for Robust camouflaged object detection

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IEEE SIGNAL PROCESSING LETTERS 2025年 32卷 1251-1255页

作者： Zhang, Jialin Wang, Xiao Yuan, Xin Mu, Nan Wang, Zheng Wuhan Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430081 Peoples R China Wuhan Univ Sci & Technol Hubei Prov Key Lab Intelligent Informat Proc & Rea Wuhan 430081 Peoples R China

camouflaged object detection (COD) aims to segment camouflaged objects hidden within their environment. Existing COD models, aside from image features, mostly focus on a single coarse-grained spatial structure, such as depth information, texture information, or edge information. However, when faced with complex scenes where the target and background textures are similar and overlapping, or when subjected to noise interference, this design often leads to insufficient detection accuracy and robustness. To address these issues, we proposed a strategy for multiple spatial explorations and designed Spatial Bi-Exploration Network (SPNet). SPNet conducts a comprehensive analysis of complex camouflage scenarios by jointly exploring depth spatial, contour spatial, and image feature information, thereby enhancing detection performance and maintaining robustness. Unlike existing methods, SPNet leverages dual exploration of depth and contour spaces to mitigate the vulnerability of coarse structures to noise. Depth spatial information aids the model in recognizing the deep relationships between objects and the background, reducing the impact of noise on object boundaries, while contour spatial information improves edge detection accuracy. This dual approach significantly enhances robustness, especially in the face of adversarial attacks. Extensive experiments on benchmark datasets demonstrate that our model not only outperforms existing methods in detection performance but also exhibits superior robustness against adversarial attacks.

关键词： Feature extraction Accuracy Robustness Image edge detection Noise object detection Loss measurement Data mining Predictive models Visualization Adversarial attack camouflaged object detection robust study spatial exploration

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Vision-Inspired Boundary Perception Network for Lightweight camouflaged object detection

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IEEE SIGNAL PROCESSING LETTERS 2025年 32卷 1176-1180页

作者： Chen, Chunyuan Liang, Weiyun Wang, Donglin Wang, Bin Xu, Jing Nankai Univ Coll Artificial Intelligence Tianjin 300350 Peoples R China Nankai Univ Ocean Engn Res Ctr Tianjin 300350 Peoples R China Beijing Sursen Informat Technol Co Ltd Beijing 100080 Peoples R China

Lightweight camouflaged object detection (COD) has garnered increasing attention due to its wide range of real-world applications and its efficiency on mobile devices. Existing lightweight COD methods typically attempt to utilize multi-scale fusion, frequency cues, and texture information to enhance the representation ability of lightweight backbone features. However, they still fall short in detecting precise and continuous object boundaries. To address this issue, we observe that two types of cells in the human visual system make great contributions to boundary perception. Motivated by this, we propose a boundary perception module (BPM) to enhance features with the awareness of fine-grained boundary, by mimicking the boundary perception process of aforementioned cells. In addition, we propose a bidirectional semantic enhancement module (BSEM) to effectively decode multi-level features in a lightweight manner. With BPM and BSEM, our proposed vision-inspired boundary perception network (BPNet) achieves superior performance against state-of-the-art methods and surpasses lightweight COD models by a large margin with the least parameters (3.64 M) and fastest speed (168FPS for the input size of 384 x 384).

关键词： Semantics Training Convolution object detection Feature extraction Artificial intelligence Fuses Data mining Visualization Performance evaluation camouflaged object detection lightweight model vision-inspired boundary perception

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Multi-view learning for camouflaged object detection with PVTv2

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INTERNATIONAL JOURNAL OF MULTIMEDIA INFORMATION RETRIEVAL 2025年第2期14卷 1-11页

作者： Yan, Pu Ruan, Kang Wang, Lili Zhao, Yang Wang, Xu Anhui Jianzhu Univ Sch Elect & Informat Engn Hefei 230601 Anhui Peoples R China

Recently, with the continuous development in the field of camouflaged object detection (COD), effectively separating objects highly similar to the background has become a focal point of research. Due to the high similarity between camouflaged objects and backgrounds, traditional single visual branch often perform poorly in such scenarios. To address this issue, we propose a multi-view learning detection network based on the Pyramid Vision Transformer, named Multi-view Learning for camouflaged object detection with PVTv2 (MVLNet). By utilizing the information from RGB and noise views, our method can provide a more comprehensive description of the relationship between objects and backgrounds to improve the accuracy and robustness for COD. Inspired by human visual attention during observation, we design a Global Context Aggregation Module by using a U-shaped structure and progressively increasing dilation rates to simulate the human behavior of zooming in and out. Extensive experiments demonstrate that the proposed MVLNet outperforms 23 other representative models on three public datasets.

关键词： camouflaged object detection Multi-view learning Global context aggregation module Pyramid vision transformer

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