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Boundary-guided context-aware network for camouflaged object detection

NEURAL COMPUTING & APPLICATIONS 2023年第20期35卷 15075-15093页

作者： Xiao, Jin Chen, Tianyou Hu, Xiaoguang Zhang, Guofeng Wang, Shaojie Beihang Univ Sch Automat Sci & Elect Engn 37 Xueyuan Rd Beijing 100191 Peoples R China

camouflaged object detection (COD) aims to identify and segment items that are seamlessly assimilate into the surroundings. Compared with the traditional image segmentation, the indefinable boundaries of camouflaged objects and high intrinsic similarities between the targets and the surrounding background make COD more challenging. Although many models have been designed to settle this issue, these algorithms suffer from inaccurate contours. Besides, existing COD methods mainly strive to expand the receptive field and excavate rich contextual cues using convolutional layers with large dilation rates. However, the tiny detailed information may be degenerated in this process due to the large ``holes'' in these dilated convolutional layers. This paper develops a novel boundary-guided context-aware network (BCNet) to deal with these problems. Specifically, we design a high-resolution feature enhancement module to excavate multi-scale information and enlarge the receptive field without destroying the tiny details within the input feature maps. The proposed module is effective in enhancing the single- layer feature and boosting the COD performance. Besides, a boundary-guided feature interaction module is designed to aggregate multi-level features and investigate the complementary relationship between the targets and the corresponding contours. We evaluate BCNet on four benchmark datasets to verify the effectiveness of the key modules. The experimental results demonstrate that BCNet achieves a real-time inference speed (49 FPS) on a Titan XP GPU and surpasses 19 cutting-edge contenders. Source code will be available at https://***/clelouch/ BCNet.

关键词： Boundary guidance camouflaged object detection Convolutional neural network High-resolution feature enhancement

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Guided multi-scale refinement network for camouflaged object detection

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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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Lightweight camouflaged object detection model based on multilevel feature fusion

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COMPLEX & INTELLIGENT SYSTEMS 2024年第3期10卷 4409-4419页

作者： Li, Qiaoyi Wang, Zhengjie Zhang, Xiaoning Du, Hongbao Beijing Inst Technol Sch Mechatron Engn Beijing 100081 Peoples R China

The intrinsic similarity between camouflaged objects and background environment impedes the automatic detection/segmentation of camouflaged objects, and novel network architectures for deep learning are promising to overcome this challenge and improve detection accuracy. However, these existing network architectures for distinguishing between camouflaged objects and their backgrounds do not account for the constraint of detection speed, which results in high computational complexity and the inability to meet the requirements of rapid detection. Therefore, based on the human visual system, this study proposes a single-stage lightweight camouflage object detection network using multilevel feature fusion, integrating features of various feature layers and receptive field sizes. Using three benchmark datasets for normal camouflaged objects, the lightweight network (LINet) model demonstrated an accuracy superior to those of six existing mainstream camouflaged object detection methods. Its detection speed, 126.3 frames per second, is significantly higher than those of the existing mainstream methods, enabling rapid detection with a maximum increase of 187.62%. The accuracy of LINet is the minimum and maximum for Resnet101 and Resnet152, respectively. These findings pave the way for diverse applications of camouflaged target detection algorithms.

关键词： camouflaged object detection Lightweight Multilevel feature fusion detection speed Feature extraction network

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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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camouflaged object detection via Dual-branch Fusion and Dual Self-similarity constraints

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PATTERN RECOGNITION 2024年 157卷

作者： Yang, Haozhe Zhu, Yuan Sun, Ke Ding, Haoyang Lin, Xianming Xiamen Univ Sch Informat Key Lab Multimedia Trusted Percept & Efficient Com Minist Educ China Xiamen 361005 Peoples R China

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.

关键词： camouflaged object detection Dual-branch feature fusion Self-similarity Edge enhancement Small-scale object localization

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camouflaged object detection via Neighbor Connection and Hierarchical Information Transfer

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COMPUTER VISION AND IMAGE UNDERSTANDING 2022年 221卷

作者： Zhang, Cong Wang, Kang Bi, Hongbo Liu, Ziqi Yang, Lina Northeast Petr Univ Dept Elect Informat Engn Daqing 163318 Peoples R China

camouflaged object detection (COD) aims to detect objects with high similarity to the background. Unlike general object detection, COD is a more challenging task because the target boundaries are vague and the location is difficult to determine. In this paper, we propose a novel COD framework, which consists of two main components, namely, Neighbor Connection Mode (NCM) and Hierarchical Information Transfer (HIT). NCM aggregates the features from the neighboring layers of the encoder network to enhance the complementation of various level information. Our NCM not only reduces the burden of dense connection that consumes a lot of computing memory and redundant features but also weakens the phenomenon of the long-term transmission of context. We also propose a HIT module to transfer the features of different dilated rates inside each level hierarchically, which expands the receptive field of each branch and enhances the relationship between different features. Our method accurately detects camouflaged objects by considering full level information and a large receptive field. The experiments on three COD datasets show that our model achieves state-of-the-art performance.

关键词： Deep learning camouflaged object detection Salient object detection

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camouflaged object detection via location-awareness and feature fusion

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IMAGE AND VISION COMPUTING 2024年 152卷

作者： Ge, Yanliang Zhong, Yuxi Ren, Junchao He, Min Bi, Hongbo Zhang, Qiao Northeast Petr Univ Dept Elect Informat Engn Daqing 163318 Peoples R China Daqing Branch China Mobile Commun Grp Heilongjiang Daqing 163318 Peoples R China China Univ Petr East China Sch Comp Sci & Technol Qingdao 266580 Peoples R China

camouflaged object detection aims to completely segment objects immersed in their surroundings from the background. However, existing deep learning methods often suffer from the following shortcomings: (1) They have difficulty inaccurately perceiving the target location;(2) The extraction of multi-scale feature is insufficient. To address the above problems, we proposed a camouflaged object detection network(LFNet) based on location-awareness and feature fusion. Specifically, we designed a status location module(SLM) that dynamically captures the structural features of targets across spatial and channel dimensions to achieve accurate segmentation. Beyond that, a residual feature fusion module(RFFM) was devised to address the challenge of insufficient multi-scale feature integration. Experiments conducted on three standard datasets(CAMO,COD10K and NC4K) demonstrate that LFNet achieves significant improvements compared with 15 state-of-the-art methods. The code will be available at https://***/ZX123445/LFNet.

关键词： camouflaged object detection Multi-scale feature Attention mechanism Location awareness

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

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APPLIED INTELLIGENCE 2024年第15-16期54卷 7531-7544页

作者： Tang, Zhe Tang, Jing Zou, Dengpeng Rao, Junyi Qi, Fang Cent South Univ Sch Comp Sci & Engn Changsha 410083 Hunan Peoples R China Changsha Xiangfeng Intelligent Equipment Co Ltd Changsha 410083 Hunan Peoples R China Jilin Univ Coll Commun Engn Changchun 130012 Jilin Peoples R China

camouflaged object detection (COD) entails identifying objects in an image that blend with the background. However, most traditional COD methods have not comprehensively considered the information provided by the overall region and edges of the objects. To address this problem, a new two-guidance joint network based on coarse and edge maps is proposed for COD. Particularly, an information guidance module is designed to inject edges and overall information into the network's backbone features. Meanwhile, a feature observation model based on skip connections and multi-scale perception is designed to capture multi-scale image details and structures. To avoid the loss of semantic information in low-level features, a full-image attention mechanism is designed to integrate high-level features into low-level features, thereby improving the resolution of the object masks. We compared the proposed network with state-of-the-art models on three well-known datasets, and the experimental results show the proposed network has significant improvement. By exploring valuable boundary information and overall object information, the proposed network can segment object edges while also considering the segmentation effect of the entire object. Our code has been open-sourced at https://***/Huah2019/TJNet.

关键词： camouflaged object detection Image segmentation Convolutional neural network Boundary-aware network

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CamoFormer: Masked Separable Attention for camouflaged object detection

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2024年第12期46卷 10362-10374页

作者： Yin, Bowen Zhang, Xuying Fan, Deng-Ping Jiao, Shaohui Cheng, Ming-Ming Van Gool, Luc Hou, Qibin Nankai Univ Sch Comp Sci VCIP Tianjin 300071 Peoples R China Nankai Int Adv Res Inst Shenzhen Futian Shenzhen 300071 Guangdong Peoples R China Bytedance Inc Beijing 100086 Peoples R China Swiss Fed Inst Technol CVL CH-8092 Zurich Switzerland

How to identify and segment camouflaged objects from the background is challenging. Inspired by the multi-head self-attention in Transformers, we present a simple masked separable attention (MSA) for camouflaged object detection. We first separate the multi-head self-attention into three parts, which are responsible for distinguishing the camouflaged objects from the background using different mask strategies. Furthermore, we propose to capture high-resolution semantic representations progressively based on a simple top-down decoder with the proposed MSA to attain precise segmentation results. These structures plus a backbone encoder form a new model, dubbed CamoFormer. Extensive experiments show that CamoFormer achieves new state-of-the-art performance on three widely-used camouflaged object detection benchmarks. To better evaluate the performance of the proposed CamoFormer around the border regions, we propose to use two new metrics, i.e., BR-M and BR-F. There are on average similar to 5% relative improvements over previous methods in terms of S-measure and weighted F-measure.

关键词： Transformers object detection Decoding Magnetic heads Head Feature extraction Visualization camouflaged object detection masked separable attention self-attention top-down decoder

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Fast camouflaged object detection via multi-scale feature-enhanced network

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SIGNAL IMAGE AND VIDEO PROCESSING 2024年第4期18卷 3903-3914页

作者： Zhou, Bingqin Yang, Kun Gao, Zhigang Hangzhou Dianzi Univ Sch Comp Sci & Technol Hangzhou 310018 Peoples R China Key Lab Brain Machine Collaborat Intelligence Zhej Hangzhou 310018 Peoples R China China Jiliang Univ Coll Informat Engn Hangzhou 310018 Peoples R China

The aim of camouflaged object detection (COD) is to identify objects that are hidden or camouflaged in the visual scene. Since camouflaged objects have fuzzy boundaries and are very similar to their surroundings, the task of COD, especially multi-scale COD, is still challenging. Based on ERRNet, we proposed a multi-scale feature-enhanced network (MSFENet). Specifically, we have developed a multi-scale feature enhancement module (MFEM), which adopts a coarse-to-fine manner to improve the ability of a single layer to represent multi-scale information. This module can extract more complete large-scale target feature information and retain much more small-scale target feature information and less regional background information. The experimental results on publicly available datasets show that our proposed MSFENet outperforms 10 mainstream methods. The ablation studies show that the proposed module is effective in improving the detection performance of multi-scale camouflaging objects and improving the overall performance. Compared with ERRNet, the average S alpha\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$S_\alpha $$\end{document}, E phi\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$E_\phi $$\end{document} and F beta W\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F_\beta <^>W $$\end{document} scores of the MSFENet are 1.2%, 0.6% and 2.5% higher for the multi-scale COD task. In addition, the proposed MSFENet can be directly use

关键词： camouflaged object detection Multi-scale feature enhancement Convolutional neural network Fast detection

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