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Guided multi-scale refinement network for camouflaged object detection

MULTIMEDIA TOOLS AND APPLICATIONS 2023年第4期82卷 5785-5801页

作者： Xu, Xiuqi Chen, Shuhan Lv, Xiao Wang, Jian Hu, Xuelong Yangzhou Univ Sch Informat Engn Yangzhou Jiangsu Peoples R China Chongqing Special Equipment Inspect & Res Inst Chongqing Peoples R China

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.

关键词： camouflaged object detection Multi-scale global perception Guided multi-scale refinement

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MGL: Mutual Graph Learning for camouflaged object detection

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2023年 32卷 1897-1910页

作者： Zhai, Qiang Li, Xin Yang, Fan Jiao, Zhicheng Luo, Ping Cheng, Hong Liu, Zicheng Univ Elect Sci & Technol China Ctr Robot Chengdu 611731 Peoples R China AIQ Abu Dhabi U Arab Emirates Brown Univ Radiol AI Lab Providence RI 02912 USA Univ Hong Kong Sch Comp Sci Hong Kong Peoples R China Microsoft AI Percept & Mixed Real Redmond WA 98101 USA

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://***/fanyang587/MGL.

关键词： Graph learning camouflaged object detection

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camouflaged object detection Based on Ternary Cascade Perception

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REMOTE SENSING 2023年第5期15卷 1188页

作者： Jiang, Xinhao Cai, Wei Ding, Yao Wang, Xin Yang, Zhiyong Di, Xingyu Gao, Weijie Xian Res Inst High Technol Xian 710064 Peoples R China

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 AP(75) that improves to 73.04, and significantly higher detection accuracy and confidence.

关键词： camouflaged object detection cascade perception computer vision deep learning pattern recognition

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Key object detection: Unifying Salient and camouflaged object detection Into One Task 7th

Key Object Detection: Unifying Salient and Camouflaged Objec...

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7th Chinese Conference on Pattern Recognition and Computer Vision

作者： Yin, Pengyu Fu, Keren Zhao, Qijun Sichuan Univ Coll Comp Sci Chengdu Peoples R China Sichuan Univ Natl Key Lab Fundamental Sci Synthet Vis Chengdu Peoples R China

ISBN: (纸本)9789819788576;9789819788583

Visual salient object detection (SOD) aims to discover eye-catching salient objects, while camouflaged object detection (COD) seeks to segment objects that are visually hidden in their surrounding environment. In this paper, considering the difficulty in defining salient and camouflaged properties of data when large-scale automated processing is launched, we propose a novel key object detection (KOD) task, which attempts to unify SOD and COD into one single task. This task treats both salient and camouflaged objects as key objects and aims to detect such key objects with salient and camouflaged properties. For KOD, we design a dual-branch network called DVNet, with a segmentation branch detecting key objects with salient and camouflaged properties in the scene, and additionally a judgment branch showcasing their saliency/camouflage degrees. Moreover, DVNet introduces a feature purification module (FPM) to focus more intently on mining features of key objects and employs a round-robin training strategy to enhance learning consistency. To demonstrate the effectiveness of DVNet for the KOD task, we construct a benchmark test set named KOD10K. We conduct extensive experiments on KOD10K and public SOD and COD datasets. DVNet achieves the best performance on KOD10K and also achieves decent performance on both SOD and COD datasets, demonstrating the significance of the proposed KOD task and the superiority of DVNet in detecting salient and camouflaged objects simultaneously.

关键词： Key object detection Salient object detection camouflaged object detection Feature purification module

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Attention and Boundary Induced Feature Refinement Network for camouflaged object detection 7th

Attention and Boundary Induced Feature Refinement Network fo...

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7th Chinese Conference on Pattern Recognition and Computer Vision

作者： Zhong, Junmin Wang, Anzhi Guizhou Normal Univ Sch Big Data & Comp Sci Guiyang 550025 Peoples R China

ISBN: (纸本)9789819786848;9789819786855

The intrinsic similarity between camouflaged objects and background environment makes camouflaged object detection (COD) task more challenging than traditional object detection task. Since the boundary of the camouflage object is difficult to determine, the existing COD method often cannot accurately identify the boundary details and complete structure of camouflage object. To solve these challenges, we propose a novel attention and boundary guided feature refinement network (ABNet) for improving the performance of COD. Specifically, ABNet mainly includes three main modules: multi-resolution feature enhancement module (MFEM), attention-induced edge-aware module (AIEM), and boundary-guide feature interaction module (BFIM). The MFEM is introduced to enhance the single-layer feature and maintain high-quality detailed information. Additionally, the AIEM is designed to model edge features effectively from the enhanced feature. Finally, the BFIM is incorporated to focus on the structural details of camouflaged objects, which aims to explore multi-level features between global and local contextual information simultaneously for facilitating more complete detection. Extensive experiments have proved the effectiveness of the proposed model, and our model demonstrates competitive performance compared to existing state-of-the-art models on four benchmark datasets.

关键词： camouflaged object detection Attention mechanism Boundary cues

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ECLNet: A Compact Encoder-Decoder Network for Efficient camouflaged object detection 7th

ECLNet: A Compact Encoder-Decoder Network for Efficient Camo...

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7th Chinese Conference on Pattern Recognition and Computer Vision

作者： Yang, Longwu Chen, Haiyan Lu, Dongni Qin, Jie Nanjing Univ Aeronaut & Astronaut Nanjing 210016 Peoples R China

ISBN: (纸本)9789819784929;9789819784936

camouflaged object detection (COD) is a valuable yet challenging task due to the resemblance between target objects and their surroundings. Existing methods have made impressive progress on COD by focusing on accurate segmentation of regions and boundaries. However, they still suffer from two major limitations: (1) lack of trade-offs between model accuracy and efficiency. (2) ignorance of the correlation between localization and edge cues. To tackle these issues, we propose a novel Edge-localization Cooperative Learning Network (ECLNet), which employs a compact encoder-decoder framework to effectively exploit the interaction between localization and edge cues. Through cooperative learning and compact structure design, ECLNet is enabled to extract comprehensive features with less computational costs, thus achieving a satisfactory trade-off between model accuracy and efficiency. Specifically, we propose an Edge-localization Interaction Module (EIM), which employs cooperative learning to simultaneously obtain edge details and enhanced localization perception. Moreover, we design a Hierarchical Feature Fusion Module (HFFM) to further fuse extracted features under the guidance of localization and edge cues. Extensive experiments on three challenging COD benchmarks demonstrate that ECLNet achieves more efficient performance compared to 14 state-of-the-art methods, improving 18.2% and 3% in terms of FPS and F-beta(omega) on COD10K.

关键词： camouflaged object detection Efficient Network Cooperative Learning

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Boundary Guided Feature Fusion Network for camouflaged object detection 6th

Boundary Guided Feature Fusion Network for Camouflaged Objec...

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6th Chinese Conference on Pattern Recognition and Computer Vision (PRCV)

作者： Qiu, Tianchi Li, Xiuhong Liu, Kangwei Li, Songlin Chen, Fan Zhou, Chenyu Xinjiang Univ Sch Informat Sci & Engn Xinjiang Peoples R China

ISBN: (纸本)9789819985456;9789819985463

camouflaged object detection (COD) refers to the process of detecting and segmenting camouflaged objects in an environment using algorithmic techniques. The intrinsic similarity between foreground objects and the background environment limits the performance of existing COD methods, making it difficult to accurately distinguish between the two. To address this issue, we propose a novel Boundary Guided Feature fusion Network (BGF-Net) for camouflaged object detection in this paper. Specifically, we introduce a Contour Guided Module (CGM) aimed at modeling more explicit contour features to improve COD performance. Additionally, we incorporate a Feature Enhancement Module (FEM) with the goal of integrating more discriminative feature representations to enhance detection accuracy and reliability. Finally, we present a Boundary Guided Feature Fusion Module (BGFM) to boost object detection capabilities and perform camouflaged object predictions. BGFM utilizes multi-level feature fusion for contextual semantic mining, we incorporate the edges extracted by the CGM into the fused features to further investigate semantic information related to object boundaries. By adopting this approach, we are able to better integrate contextual information, thereby improving the performance and accuracy of our model. We conducted extensive experiments, evaluating our BGF-Net method using three challenging datasets.

关键词： camouflaged object detection Contextual information Feature fusion Feature enhancement

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Bi-directional Boundary-object interaction and refinement network for camouflaged object detection

Bi-directional Boundary-object interaction and refinement ne...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yang, Jicheng Zhang, Qing Zhao, Yilin Li, Yuetong Liu, Zeming Shanghai Inst Technol Shanghai Peoples R China

ISBN: (纸本)9798350390155;9798350390162

Due to the high intrinsic similarity between the camouflaged objects and the background, the predicted edge cue might be inaccurate or even erroneous. This will degrade the detection performance when such edge cues are directly integrated with the camouflaged object features. To solve this issue, we propose a bi-directional camouflaged object detection network to progressive complement and correct the boundary feature and the object feature in an interactive learning manner, thereby achieving prediction with fine structures. Specifically, we design a bi-directional transmission and correction (BTC) module, which contains the boundary and object interaction (BOI) module and the Feature Calibration Fusion (FCF) module, to explore the correlation between the boundary feature and the object feature and then provide the important complementary cues to refine each other, thereby progressively compensating the deficiencies and correcting the mistakes. Experimental results on four benchmark datasets demonstrate the effectiveness and superiority of our network. Our code is publicly available at: https://***/Jcogito/BIRNet.

关键词： camouflaged object detection feature interaction feature refinement boundary cue

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Semi-Supervised camouflaged object detection: Multi Information Fusion Combined with Adaptive Receptive Field Selection Network 7th

Semi-Supervised Camouflaged Object Detection: Multi Informat...

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7th Chinese Conference on Pattern Recognition and Computer Vision

作者： Yang, Guang Xiao, Feng Liu, Ruyu Zhang, Jiawei Zhang, Jianhua Chen, Shengyong Tianjin Univ Technol Tianjin 300380 Peoples R China Hangzhou Normal Univ Hangzhou 311121 Peoples R China

ISBN: (纸本)9789819788576;9789819788583

camouflaged object detection is focused on segmenting objects concealed within their surroundings. This technology can be applied in various fields such as medical image analysis, wildlife conservation, autonomous driving, and others. Existing semi-supervised camouflage object detection methods often suffer from poor network performance due to the accumulation of incorrect pseudo labels, and they fail to fully utilize multi-scale features or account for the diverse scale contexts necessary for various sizes of camouflage objects. In this paper, we propose an innovative semi-supervised learning strategy. We employ a dual-branch network named CAMNet, utilizing salient maps corresponding to camouflage objects to aid detection. We also introduce a Multi-Information Fusion Feature Perception module (MIF) and an Adaptive Receptive Field Selection module (ARFS), which are integrated into the network. Ultimately, we perform thorough comparative experiments on the R2C7K, COD-Water, and COD-Jungle datasets, showcasing superior performance in contrast to current state-of-the-art methods. We also conduct ablation experiments, further confirming the effectiveness of the proposed modules.

关键词： camouflaged object detection Semi-supervised learning Computer vision Deep learning

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Using High-Quality Feature for Weakly-Supervised camouflaged object detection 8th

Using High-Quality Feature for Weakly-Supervised Camouflaged...

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8th International Joint Conference on Web and Big Data and Web-Age Information Management (APWeb-WAIM)

作者： Wu, Weijie Tong, Yiqiu Jiang, Qijun Chen, Lina Gao, Hong Zhejiang Normal Univ Coll Phys & Elect Informat Engn Jinhua Zhejiang Peoples R China Zhejiang Normal Univ Sch Comp Sci & Technol Jinhua Zhejiang Peoples R China

ISBN: (纸本)9789819772438;9789819772445

camouflaged objects appear almost identical to the background, which leads to low-level features carrying a large amount of noise, and redundant information being included in high-level features. Therefore, we propose the Detailed and Semantic Feature Extraction Network (DSNet) to recognize camouflaged objects from the background. Firstly, we use the Top-k Sparse Attention module (TKSA) to avoid interference from noise in low-level features. Secondly, we introduce the Spatial and Channel reconstruction Convolution module (SCConv) to remove redundant information in high-level features, at the same time, we propose the Asymmetric Logical Semantic Relation module (ALSR) to capture more semantic information in high-level features. Finally, extensive experiments show that our method achieves significant improvements compared to other weakly-supervised camouflaged object detection methods on three datasets.

关键词： Weakly-Supervised camouflaged object detection Feature Extraction

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