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Frequency-aware Camouflaged Object Detection

Jiaying Lin, Xin Tan, Ke Xu, Lizhuang Ma, Rynson W.H. Lau
2022 ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)  
background, and a frequency-based method, called FBNet, for camouflaged object detection.  ...  Camouflaged object detection (COD) is important as it has various potential applications.  ...  RFB was first proposed for object detection [28] , to enhance the deep features of discriminative regions, and then extended for salient object detection (SOD) [45] .  ... 
doi:10.1145/3545609 fatcat:swyifk45p5h4zmtf7ihzy6xghu

Deep Gradient Learning for Efficient Camouflaged Object Detection [article]

Ge-Peng Ji, Deng-Ping Fan, Yu-Cheng Chou, Dengxin Dai, Alexander Liniger, Luc Van Gool
2022 arXiv   pre-print
This paper introduces DGNet, a novel deep framework that exploits object gradient supervision for camouflaged object detection (COD).  ...  ., a context and a texture encoder. The essential connection is the gradient-induced transition, representing a soft grouping between context and texture features.  ...  [34] introduce the concept of difficulty-aware learning based on the Transformer for both camouflaged and salient object detection.  ... 
arXiv:2205.12853v2 fatcat:gp45aow7yvg3fac3xufgbaypzi

Cascade and Fusion: A Deep Learning Approach for Camouflaged Object Sensing

Kaihong Huang, Chunshu Li, Jiaqi Zhang, Beilun Wang
2021 Sensors  
The demand for the sensor-based detection of camouflage objects widely exists in biological research, remote sensing, and military applications.  ...  To address this problem, we propose Camouflaged Object Detection with Cascade and Feedback Fusion (CODCEF), a deep learning framework based on an RGB optical sensor that leverages a cascaded structure  ...  [6] formulated texture-aware refinement modules emphasizing the difference between the texture-aware features. Dong et al.  ... 
doi:10.3390/s21165455 pmid:34450897 pmcid:PMC8400738 fatcat:hesbrywborbadfami6ixjlnkh4

A novel deep neural network for hidden target detection in images

Rabeb Hendaoui, Dept. of Computer Engineering Karadeniz Technical University, Turkey, Vasif Nabiyev, Dept. of Computer Engineering Karadeniz Technical University, Turkey
2021 Maǧallaẗ Al-Kuwayt li-l-ʿulūm  
Experimental results on the camouflaged people dataset demonstrate that our proposed method can achieve state-of-the-art performance for hidden target detection.  ...  The significant similarity between the hidden target and the background makes it difficult to find camouflaged people, such as warriors in warfare, or even camouflaged objects in natural environments.  ...  Comparison We compared our model to DDCN (Zheng et al. 2018) , a technique for detecting camouflaged people, as well as to other state-of-the-art deep learning detection approaches, including Amulet ,  ... 
doi:10.48129/kjs.15249 fatcat:trns5hkaqzeojbairk6544hshi

Deep Camouflage Images

Qing Zhang, Gelin Yin, Yongwei Nie, Wei-Shi Zheng
In particular, we design an attention-aware camouflage loss to adaptively mask out information that make the hidden objects visually standout, and also leave subtle yet enough feature clues for viewers  ...  Previous methods basically rely on hand-crafted features and texture synthesis to create camouflage images.  ...  Unlike existing methods which depend on hand-crafted features, we introduce the first deep-learning approach for creating camouflage images.  ... 
doi:10.1609/aaai.v34i07.6981 fatcat:digoqzr5qvhjdlxnb3q3hjp3my

Inferring Camouflaged Objects by Texture-Aware Interactive Guidance Network

Jinchao Zhu, Xiaoyu Zhang, Shuo Zhang, Junnan Liu
2021 AAAI Conference on Artificial Intelligence  
Feature interaction guidance decoder (FGD) interactively refines multi-level features of camouflaged object detection and texture detection level by level.  ...  Herein, we design a texture label to facilitate our network for accurate camouflaged object segmentation.  ...  We would like to thank reviewers for their constructive suggestions.  ... 
dblp:conf/aaai/ZhuZZL21 fatcat:nfzllb4htvd73esla7e7kqsvsa

Context-aware Cross-level Fusion Network for Camouflaged Object Detection [article]

Yujia Sun, Geng Chen, Tao Zhou, Yi Zhang, Nian Liu
2021 arXiv   pre-print
Camouflaged object detection (COD) is a challenging task due to the low boundary contrast between the object and its surroundings.  ...  In addition, the appearance of camouflaged objects varies significantly, e.g., object size and shape, aggravating the difficulties of accurate COD.  ...  Figure 2 shows the overall architecture of the proposed C 2 F-Net, which fuses context-aware cross-level features to improve the camouflaged object detection performance.  ... 
arXiv:2105.12555v1 fatcat:vxwa44qslvgc5lcx4zqtytomoq

Anabranch network for camouflaged object segmentation

Trung-Nghia Le, Tam V. Nguyen, Zhongliang Nie, Minh-Triet Tran, Akihiro Sugimoto
2019 Computer Vision and Image Understanding  
Camouflaged objects attempt to conceal their texture into the background and discriminating them from the background is hard even for human beings.  ...  The main objective of this paper is to explore the camouflaged object segmentation problem, namely, segmenting the camouflaged object(s) for a given image.  ...  We also thank Vamshi Krishna Madaram and Zhe Huang (University of Dayton) for their support in the annotation of the CAMO dataset. We gratefully acknowledge NVIDIA for the support of GPU.  ... 
doi:10.1016/j.cviu.2019.04.006 fatcat:sl3ggf46sjfdlc6siinwnwfs4y

Exploring Depth Contribution for Camouflaged Object Detection [article]

Mochu Xiang, Jing Zhang, Yunqiu Lv, Aixuan Li, Yiran Zhong, Yuchao Dai
2022 arXiv   pre-print
Secondly, we introduce a multi-modal confidence-aware loss function via a generative adversarial network to weigh the contribution of depth for camouflaged object detection.  ...  two solutions work cooperatively, achieving effective depth contribution exploration for camouflaged object detection.  ...  As far as we know, there exist no deep camouflaged object detection models exploring the depth contribution.  ... 
arXiv:2106.13217v3 fatcat:hqwydqy6uvbbddswbehozrgy4q

I Can Find You! Boundary-Guided Separated Attention Network for Camouflaged Object Detection

Hongwei Zhu, Peng Li, Haoran Xie, Xuefeng Yan, Dong Liang, Dapeng Chen, Mingqiang Wei, Jing Qin
2022 AAAI Conference on Artificial Intelligence  
Beyond the existing camouflaged object detection (COD) wisdom, BSA-Net utilizes two-stream separated attention modules to highlight the separator (or say the camouflaged object's boundary) between an image's  ...  We validate on three benchmark datasets that our BSA-Net is very beneficial to detect camouflaged objects with the blurred boundaries and similar colors/patterns with their backgrounds.  ...  (Ren et al. 2021) propose a texture-aware module to amplify the subtle texture difference between the camouflaged object and the background. Yan et al.  ... 
dblp:conf/aaai/ZhuL0YLCWQ22 fatcat:h2leikp7knhjtphiipkpvbtvti

Camouflaged Object Detection via Context-aware Cross-level Fusion [article]

Geng Chen, Si-Jie Liu, Yu-Jia Sun, Ge-Peng Ji, Ya-Feng Wu, Tao Zhou
2022 arXiv   pre-print
To address these challenges, we propose a novel Context-aware Cross-level Fusion Network (C2F-Net), which fuses context-aware cross-level features for accurately identifying camouflaged objects.  ...  Camouflaged object detection (COD) aims to identify the objects that conceal themselves in natural scenes.  ...  [47] proposed a two-step texture-aware refinement module to amplify the differences between the camouflaged object and its surroundings for accurate COD. Ji et al.  ... 
arXiv:2207.13362v1 fatcat:vz5zdjbzpvd7da5a2lwdzq2anm

Interactive Evolution of Camouflage

Craig Reynolds
2011 Artificial Life  
GP searches the space of texture description programs for those which appear least conspicuous to the predator.  ...  This paper presents an abstract computation model of the evolution of camouflage in nature.  ...  I The texture synthesis library contained 52 texture producing elements. Some of the names are self-descriptive, for oth-ers, and for description of parameter types for each, see (Reynolds, 2009  ... 
doi:10.1162/artl_a_00023 pmid:21370960 fatcat:jgqhpwphjnho3blnxuh2p7alxu

Towards Deeper Understanding of Camouflaged Object Detection [article]

Yunqiu Lv, Jing Zhang, Yuchao Dai, Aixuan Li, Nick Barnes, Deng-Ping Fan
2022 arXiv   pre-print
To detect and segment the whole scope of a camouflaged object, camouflaged object detection (COD) is introduced as a binary segmentation task, with the binary ground truth camouflage map indicating the  ...  Furthermore, we observe that it is some specific parts of camouflaged objects that make them detectable by predators.  ...  [38] employed the co-occurrence matrix based texture features to detect the camouflaged defect. Xue et al. [39] and Pike et al.  ... 
arXiv:2205.11333v1 fatcat:ztotpv76wzgdva6vnj4jyje3fi

Rapid detection of camouflaged artificial target based on polarization imaging and deep learning

Ying Shen, Wenfu Lin, Zhifeng Wang, Jie Li, Xinquan Sun, Xianyu Wu, Shu Wang, Feng Huang
2021 IEEE Photonics Journal  
However, with the development of camouflage materials and camouflage shielding performance, the anti-optical detection technology for camouflaged targets continues to improve.  ...  In this paper, we combine the advantages of polarization imaging and deep learning to achieve rapid detection of artificial targets camouflaged in natural scenes.  ...  In order to improve the memory ability of the shallow layers in the deep network, one 3×3 convolutional layer is used to preserve shallow features for subsequent deep convolution layers.  ... 
doi:10.1109/jphot.2021.3103866 fatcat:dakq4plipjbgln273tuitzvydq

Fast Camouflaged Object Detection via Edge-based Reversible Re-calibration Network [article]

Ge-Peng Ji, Lei Zhu, Mingchen Zhuge, Keren Fu
2021 arXiv   pre-print
Camouflaged Object Detection (COD) aims to detect objects with similar patterns (e.g., texture, intensity, colour, etc) to their surroundings, and recently has attracted growing research interest.  ...  As camouflaged objects often present very ambiguous boundaries, how to determine object locations as well as their weak boundaries is challenging and also the key to this task.  ...  Introduction Camouflaged object detection (COD) aims to detect concealed/camouflaged objects 1 in visual scenes.  ... 
arXiv:2111.03216v1 fatcat:nytfe5n3j5er5c7ybiw5fxybdu
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