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Depth alignment interaction network for camouflaged object detection

MULTIMEDIA SYSTEMS 2024年第1期30卷 51-51页

作者： Bi, Hongbo Tong, Yuyu Zhang, Jiayuan Zhang, Cong Tong, Jinghui Jin, Wei Northeast Petr Univ Sanya Offshore Oil & Gas Res Inst Daqing 163319 Peoples R China Natl Univ Def Technol Coll Elect Countermeasures Hefei 230037 Peoples R China Northeast Petr Univ Sch Elect Informat Engn Daqing 163319 Peoples R China Guizhou Univ State Key Lab Publ Big Data Guiyang 550025 Peoples R China

Many animals actively change their own characteristics, such as color and texture, through camouflage, a natural defense mechanism, making them difficult to be detected in the natural environment, which makes the task of camouflaged object detection extremely challenging. Biological research shows that the eyes of animals have three-dimensional perception ability, and the obtained depth information can provide useful object positioning clues for finding camouflaged objects. However, almost all the current studies for camouflaged object detection do not combine depth maps with RGB images. Therefore, combining depth maps with traditional unimodal RGB images is of great research significance to improve the accuracy of camouflaged object detection. In this paper, we propose a depth alignment interaction network for camouflaged object detection in which the depth maps used are generated from existing monocular depth estimation networks. To address the problem that the quality of the generated depth maps varies, we propose a depth alignment index method to evaluate the quality of the depth maps. The method dynamically assigns the proportion of depth maps in the fusion process to depth maps of different quality according to their alignment with RGB images. Then, to fully extract the fused artifact features, we design an expanded pyramid interaction module, which first expands the receptive field of the features in each layer. Then, the features at the higher levels interacted with the features at the lower levels by connecting them step-by-step to further refine the predicted camouflaged area. Extensive experiments on 4 camouflaged object detection datasets demonstrate the effectiveness of our solution for camouflaged object detection.

关键词： camouflaged object detection Depth alignment index Expanded pyramid interaction Deep learning

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Attention-induced semantic and boundary interaction network for camouflaged object detection

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COMPUTER VISION AND IMAGE UNDERSTANDING 2023年第1期233卷

作者： Zhang, Qiao Sun, Xiaoxiao Chen, Yurui Ge, Yanliang Bi, Hongbo Northeast Petr Univ Dept Elect Informat Engn Daqing 163318 Peoples R China Shangqiu Univ Dept Mech & Elect Informat Shangqiu 476000 Peoples R China

The high intrinsic similarity between camouflaged objects and the background makes camouflaged object detection (COD) more challenging than traditional salient object detection (SOD). Existing deep-learning methods often fall into the following shortcomings: (1) When dealing with high-level features, it is difficult to fully and accurately extract semantic information, which is crucial for locating camouflaged targets. (2) The camouflaged targets identified by existing methods in complex scenes have rough boundaries and incomplete spatial information. (3) Existing methods cannot effectively exert the uniqueness and complementarity of multiple features when integrating multiple features. To this end, we propose an attention-induced semantic and boundary interaction network for accurately identifying camouflaged objects. Specifically, we propose a contrastive positioning module (CPM), which adopts a contrastive learning way to separate the camouflaged object from the background, thereby obtaining the exact location of the camouflaged target and retaining more semantic information. Then, we design a boundary exploration module (BEM), which can reduce background noise interference, focus on the structural details of camouflaged targets, and explore rich fine-grained spatial information. Finally, we propose an attention-induced interaction module (AIM) to fuse multivariate information (i.e., semantic location information, boundary information, and the side output information of the backbone), and multivariate information is complementarily fused under attention guidance to generate more powerful camouflaged target features. Extensive experiments show that the proposed method on the different backbones (i.e., ResNet-50, Res2Net-50, PVTv2-B2, EfficientNet-B1) produces state-of-the-art results on several camouflaged benchmarks. The code is available https://***/zhangqiao970914/ASBI.

关键词： Deep learning camouflaged object detection Feature interaction Attention

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Reverse cross-refinement network for camouflaged object detection

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IMAGE AND VISION COMPUTING 2024年 150卷

作者： Ye, Qian Zhou, Yaqin Huo, Guanying Liu, Yan Zhou, Yan Li, Qingwu Hohai Univ Coll Informat Sci & Engn Changzhou 213200 Peoples R China Hohai Univ Jiangsu Key Lab Power Transmiss & Distribut Equipm Changzhou 213200 Peoples R China

Due to the high intrinsic similarity between camouflaged objects and the background, camouflaged objects often exhibit blurred boundaries, making it challenging to distinguish the boundaries of objects. Existing methods still focus on the overall regional accuracy but not on the boundary quality and are not competent to identify camouflaged objects from the background in complex scenarios. Thus, we propose a novel reverse cross-refinement network called RCR-Net. Specifically, we design a diverse feature enhancement module that simulates the correspondingly expanded receptive fields of the human visual system by using convolutional kernels with different dilation rates in parallel. Also, the boundary attention module is used to reduce the noise of the bottom features. Moreover, a multi-scale feature aggregation module is proposed to transmit the diverse features from pixel-level camouflaged edges to the entire camouflaged object region in a coarse-to-fine manner, which consists of reverse guidance, group guidance, and position guidance. Reverse guidance mines complementary regions and details by erasing already estimated object regions. Group guidance and position guidance integrate different features through simple and effective splitting and connecting operations. Extensive experiments show that RCR-Net outperforms the existing 18 state-of-the-art methods on four widely-used COD datasets. Especially, compared with the existing top-1 model HitNet, RCR-Net significantly improves the performance by similar to 16.4% (Mean Absolute Error) on the CAMO dataset, showing that RCR-Net could accurately detect camouflaged objects.

关键词： camouflaged object detection Cross refinement Reverse guidance Receptive field

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IPNet: Polarization-based camouflaged object detection via dual-flow network

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年第PartA期127卷

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

camouflaged object detection (COD) is a critical task in a variety of domains, such as medicine and military applications. The main challenge in COD is accurately detecting and extracting the concealed object from the complex background. The similarity between the camouflaged objects and their background significantly reduces the accuracy of object extraction. Polarization information can provide valuable insights into the characteristics of objects with different material properties and surface roughness. It reflects the difference in polarization information between the object and the background, which increases the contrast between the two and improves the object detection accuracy even under complex scenes. In this paper, we propose IPNet, an efficient cross-modal fusion network that utilizes both RGB intensity and linear polarization cues to generate scene representation with high contrast. Our novel network architecture dynamically fuses RGB intensity and polarization cues using an efficient cross-modal fusion module, leveraging cross-level contextual information to achieve robust detection. For training and evaluating the proposed network, we construct a polarization-based PCOD_1200 dataset that contains 89 subclasses and 1200 samples. A comprehensive set of experiments demonstrates the effectiveness of IPNet to fuse polarization and RGB intensity information and shows that our approach outperforms state-of-the-art methods.

关键词： camouflaged object detection Polarization image Feature fusion Dataset

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AFINet: camouflaged object detection via Attention Fusion and Interaction Network

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2024年 102卷

作者： Zhang, Qing Yan, Weiqi Zhao, Yilin Jin, Qi Zhang, Yu Shanghai Inst Technol Dept Comp Sci & Informat Engn Shanghai 201418 Peoples R China

Since the camouflaged objects share very similar colors and textures with the surroundings, there is still a great challenge in accurately locating and segmenting target objects with the varying sizes and shapes in different scenes. In this paper, we propose a novel Attention Fusion and Interaction network (AFINet) to detect the camouflaged objects by exploring the cross -level complementary information. Specifically, we first propose a Multi -Attention Interaction (MAI) module to fuse the cross -level features containing different characteristics by the attention interaction, thereby fully making use of the specific and complementary information from different levels to deal with scale variation. Furthermore, we design a Location and Boundary Guidance (LBG) module to make each side -output feature aware of where to learn, which can avoid the disturbances of the noncamouflaged regions by distinguishing the subtle differences. Comprehensive experiments and comparisons are conducted on four widely used benchmark datasets, demonstrating that the proposed network achieves stateof-the-art performance. The code and prediction maps will be available at https://***/ZhangQing0329/ AFINet.

关键词： camouflaged object detection Cross-level feature fusion Attention interaction and fusion Boundary guidance

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Edge-Guided camouflaged object detection via Multi-Level Feature Integration

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SENSORS 2023年第13期23卷 5789页

作者： Liu, Kangwei Qiu, Tianchi Yu, Yinfeng Li, Songlin Li, Xiuhong Xinjiang Univ Dept Informat Sci & Engn Key Lab Signal Detect & Proc Urumqi 830017 Peoples R China

camouflaged object detection (COD) aims to segment those camouflaged objects that blend perfectly into their surroundings. Due to the low boundary contrast between camouflaged objects and their surroundings, their detection poses a significant challenge. Despite the numerous excellent camouflaged object detection methods developed in recent years, issues such as boundary refinement and multi-level feature extraction and fusion still need further exploration. In this paper, we propose a novel multi-level feature integration network (MFNet) for camouflaged object detection. Firstly, we design an edge guidance module (EGM) to improve the COD performance by providing additional boundary semantic information by combining high-level semantic information and low-level spatial details to model the edges of camouflaged objects. Additionally, we propose a multi-level feature integration module (MFIM), which leverages the fine local information of low-level features and the rich global information of high-level features in adjacent three-level features to provide a supplementary feature representation for the current-level features, effectively integrating the full context semantic information. Finally, we propose a context aggregation refinement module (CARM) to efficiently aggregate and refine the cross-level features to obtain clear prediction maps. Our extensive experiments on three benchmark datasets show that the MFNet model is an effective COD model and outperforms other state-of-the-art models in all four evaluation metrics (S-alpha, E-phi, F-beta(omega), and MAE).

关键词： camouflaged object detection multi-level feature integration attention mechanism boundary semantic information

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Alternate guidance network for boundary-aware camouflaged object detection

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MACHINE VISION AND APPLICATIONS 2023年第4期34卷 69-69页

作者： Yu, Jinhao Chen, Shuhan Lu, Lu Chen, Zeyu Xu, Xiuqi Hu, Xuelong Zhu, Jinrong Yangzhou Univ Sch Informat Engn Yangzhou Peoples R China

camouflaged object, similar to the background, shows indistinct boundaries and low-contrast features, which brings great challenges to the detection task. Moreover, existing models still suffer from coarse object boundaries. Motivated by the complementary relationship between boundaries and camouflaged object regions, we propose an alternate guidance network named AGNet for better interaction between them. Specifically, we first propose feature selective module to select high discriminative features and simultaneously filter out noisy background features. The proposed AGNet follows a locate and refine manner, where multi-scale convolution is applied to expand receptive field for accurate initial coarse localization. Finally, a novel alternate guidance module is designed and embedded into each side-output to refine the previous localization progressively. Contributed by it, the complementary characteristic between the region and boundary features can be well captured, which is beneficial to obtain more complete detection. Experimental results on five COD datasets prove the effectiveness of our model, and it is superior to existing state-of-the-art models in object accuracy and boundary accuracy.

关键词： camouflaged object detection Feature selective module Multi-scale convolution Alternate guidance module

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Attention guided multi-level feature aggregation network for camouflaged object detection

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IMAGE AND VISION COMPUTING 2024年 144卷

作者： Wang, Anzhi Ren, Chunhong Zhao, Shuang Mu, Shibiao Guizhou Normal Univ Sch Big Data & Comp Sci Huaxi Univ Town Guiyang 550025 Guizhou Peoples R China Yiwu Ind & Commercial Coll Sch Mechatron & IT 2 Xueyuan Rd Yiwu 322000 Zhejiang Peoples R China

camouflaged object detection (COD) aims to identify objects that are visually blended into their highly similar surroundings, which is an extremely complex and challenging visual task in real -world scenarios, and has recently attracted increasing research interest in the field of computer vision due to its valuable applications. The existing deep learning based methods of COD have the following problems: 1) the ambiguous boundary of the camouflaged objects in prediction map, 2) an inaccurate detection of the camouflaged object with accurate and complete structure details. To this end, an attention guided multi-level feature aggregation network is proposed for this task, which is based on three key designs. First, by embedding spatial pyramid attention (SPA) in ResNetlike backbone network, better multi-level features are extracted to identify the camouflaged objects with complete internal details. Second, an edge context module is designed to make full use of edge information, which highlights camouflaged object structure and generates accurate edge localization of COD. Third, the feature aggregation module based on local attention is used to fuse these enhanced multi-level features and edge context cues, which consists of two major components: the iterative Attentional Feature Fusion (iAFF) module and the Dual-branch Global Context Module (DGCM). Compared with the existing 16 state -of -the -art methods, extensive experiments on four widely-used benchmark datasets under four authoritative evaluation metrics illustrate that the proposed method is very beneficial to the COD task.

关键词： camouflaged object detection Attention Feature aggregation Edge context

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CubeNet: X-shape connection for camouflaged object detection

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PATTERN RECOGNITION 2022年 127卷 108644-108644页

作者： Zhuge, Mingchen Lu, Xiankai Guo, Yiyou Cai, Zhihua Chen, Shuhan Shandong Univ Sch Software Jinan Peoples R China Chinese Univ Geosci Sch Comp Sci Wuhan Peoples R China Tongji Univ Coll Surveying & Geoinformat Shanghai Peoples R China Yangzhou Univ Sch Informat Engn Yangzhou Peoples R China

camouflaged object detection (COD) aims to detect out-of-attention regions in an image. Current binary segmentation solutions fail to tackle COD easily, since COD is more challenging due to object often accompany with weak boundaries, low contrast, or similar patterns to the background. That is, we need a more efficient scheme to address this problem. In this work, we propose a new COD framework called CubeNet by introducing X connection to the standard encoder-decoder architecture. Specifically, CubeNet consists of two square fusion decoder (SFD) and a sub edge decoder (SED). The special designed SFD takes full advantage of low-level and high-level features extracted from encoder-decoder blocks, providing more powerful representations at each stage. To explicitly modeling the weak boundaries of the objects, we introduced a SED between the two SFD. With such kind of holistic designs, these three decoder modules resolve the challenging ambiguity of camouflaged object detection. CubeNet significantly advance the cutting-edge model on three challenging COD datasets (i.e., COD10K, CAMO, and CHAMELEON), and achieves the real-time (50fps) inference.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： camouflaged object detection Neural network Edge guidance Novel feature aggregation

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Search and recovery network for camouflaged object detection

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IMAGE AND VISION COMPUTING 2024年 151卷

作者： Liu, Guangrui Wu, Wei Inner Mongolia Univ Sch Comp Sci Hohhot 010021 Peoples R China

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.

关键词： camouflaged object detection Bionic object search Boundary feature

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