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检索条件"主题词=Camouflaged object detection"

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A camouflaged object detection Model Based on Deep Learning

A Camouflaged Object Detection Model Based on Deep Learning

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IEEE International Conference on Artificial Intelligence and Information Systems (ICAIIS)

作者： Wang, Yong Li, Ling Yang, Xin Wang, Xinxin Liu, Hui Army Engn Univ PLA Field Engn Coll Nanjing Peoples R China Army Engn Univ PLA Grad Sch Nanjing Peoples R China

ISBN: (纸本)9781728165905

camouflaged object detection is a hard assignment due to their textures are similar to the background. The main intention of this paper is probe into a problem about the camouflaged object detection, that is, detecting its camouflaged object for a given image. This problem has not been well studied in spite of a large area of potential applications such as camouflage military targets detection and wildlife protection. To address this problem, a camouflage object detection method based on deep learning is proposed. The suggested method can detect camouflaged object which can extract deep features automatically. It can also provide detection probability which reflect camouflage efficiency. Experimental results show that the deep learning measure can effectively detect different scene, representing the camouflage level of low, medium and high respectively.

关键词： camouflaged object detection features extraction Deep Learning

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Dual Guidance Enhancing camouflaged object detection via Focusing Boundary and Localization Representation

Dual Guidance Enhancing Camouflaged Object Detection via Foc...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Li, Songlin Li, Xiuhong Li, Zhe Ma, Hongbing Sheng, Jiabao Li, Boyuan Xinjiang Univ Sch Comp Sci & Technol Urumgi Peoples R China Hong Kong Polytech Univ Dept Elect & Elect Engn Hong Kong Peoples R China Stanford Univ Dept Elect Engn Stanford CA 94305 USA Tsinghua Univ Dept Elect Engn Beijing Peoples R China Hong Kong Polytech Univ Dept Hlth Technol & Informat Hong Kong Peoples R China Stanford Univ Dept Radiat Oncol Stanford CA 94305 USA

ISBN: (纸本)9798350390155;9798350390162

camouflaged object detection (COD) aims to segment objects that blend into their surrounding environment. However, low-level features in the shallow layers of neural networks, although rich in edge information, often contain a significant amount of redundant information, making it difficult to represent boundary details accurately. On the other hand, deep high-level features retain semantic information for object localization, but the gradual decrease in resolution can introduce biases in representing localization information. To address this issue, we propose a novel boundary and localization representation network (BLR-Net) that guides high-level features to focus on representing localization information while directing low-level features to emphasize boundary details. Firstly, we propose a multi-scale enhanced feature module (MEFM) to capture multi-scale information from backbone features and obtain aggregated feature representations. Next, we propose an extraction boundary module (EBM) that models object boundary features, providing essential boundary information. Subsequently, we introduce a guided learning module (GLM) that utilizes localization features to guide high-level features toward localization representation learning and boundary features to guide low-level features toward boundary representation learning. Finally, we propose a cross-level feature fusion module (CFFM) that aggregates contextual semantic information and gradually fuses multi-level fusion features from the bottom to the top to predict camouflaged objects. Extensive experiments on four benchmark COD datasets demonstrate that BLR-Net outperforms other state-of-the-art COD models.

关键词： camouflaged object detection boundary localization contextual semantic information

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Boosting camouflaged object detection with Dual-Task Interactive Transformer 26

Boosting Camouflaged Object Detection with Dual-Task Interac...

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26th International Conference on Pattern Recognition / 8th International Workshop on Image Mining - Theory and Applications (IMTA)

作者： Liu, Zhengyi Zhang, Zhili Tan, Yacheng Wu, Wei Anhui Univ Sch Comp Sci & Technol Hefei Anhui Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

camouflaged object detection intends to discover the concealed objects hidden in the surroundings. Existing methods follow the bio-inspired framework, which first locates the object and second refines the boundary. We argue that the discovery of camouflaged objects depends on the recurrent search for the object and the boundary. The recurrent processing makes the human tired and helpless, but it is just the advantage of the transformer with global search ability. Therefore, a dual-task interactive transformer is proposed to detect both accurate position of the camouflaged object and its detailed boundary. The boundary feature is considered as Query to improve the camouflaged object detection, and meanwhile the object feature is considered as Query to improve the boundary detection. The camouflaged object detection and the boundary detection are fully interacted by multi-head self-attention. Besides, to obtain the initial object feature and boundary feature, transformer-based backbones are adopted to extract the foreground and background. The foreground is just object, while foreground minus background is considered as boundary. Here, the boundary feature can be obtained from blurry boundary region of the foreground and background. Supervised by the object, the background and the boundary ground truth, the proposed model achieves state-of-the-art performance in public datasets. https://***/liuzywen/COD

关键词： camouflaged object detection boundary detection transformer interactive multi-task learning

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Edge-guided Contextual Attention Fusion Network for camouflaged object detection 24

Edge-guided Contextual Attention Fusion Network for Camoufla...

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3rd International Conference on Cyber Security, Artificial Intelligence and Digital Economy (CSAIDE)

作者： Hu, Bo Chen, Sibao Anhui Univ Sch Comp Sci & Technol Anhui Prov Key Lab Informat Mat & Intelligent Sen Hefei 230601 Anhui Peoples R China

ISBN: (纸本)9798400718212

camouflaged object detection (COD), with the aim of detecting camouflaged objects from similar backgrounds, is a rewarding but challenging task. A major challenge is that intrinsic similarity between foreground object and background surroundings makes existing methods based on CNNs difficult to accurately identify objects. For that purpose, we propose a novel edge-guided contextual attention fusion network (ECAFNet) in this paper. Specifically, instead of using traditional CNN encoder, we adopt an effective transformer encoder, which can learn more robust and precise representations for the image features. Besides, we introduce three novel modules including edge generation module (EGM) to get object edges for more accurate segmentation of objects, edge-guided receptive field module (ERFM) to enlarge receptive field to capture more robust features, and contextual attention fusion module (CAFM) to closely fuse multiscale features. Extensive experiments on three challenging benchmark datasets indicate that the proposed ECAFNet is an efficient COD method and significantly surpasses the state-of-the-art models.

关键词： camouflaged object detection Transformer encoder Contextual-aware learning Feature fusion

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Depth-aided camouflaged object detection 23

Depth-aided Camouflaged Object Detection

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31st ACM International Conference on Multimedia (MM)

作者： Wang, Qingwei Yang, Jinyu Yu, Xiaosheng Wang, Fangyi Chen, Peng Zheng, Feng China Three Gorges Univ Yichang Peoples R China Southern Univ Sci & Technol Shenzhen Peoples R China Univ Birmingham Birmingham W Midlands England

ISBN: (纸本)9798400701085

camouflaged object detection (COD) aims to identify and segment objects that blend into their surroundings. Since the color and texture of the camouflaged objects are extremely similar to the surrounding environment, it is super challenging for vision models to precisely detect them. Inspired by research on biology and evolution, we introduce depth information as an additional cue to help break camouflage, which can provide spatial information and texture-free separation for foreground and background. To dig clues of camouflaged objects in both RGB and depth modalities, we innovatively propose Depth-aided camouflaged object detection (DaCOD), which involves two key components. We firstly propose the Multi-modal Collaborative Learning (MCL) module, which aims to collaboratively learning deep features from both RGB and depth channels via a hybrid backbone. Then, we propose a novel Cross-modal Asymmetric Fusion (CAF) strategy, which asymmetrically fuse RGB and depth information for complementary depth feature enhancement to produce accurate predictions. We conducted numerous experiments of the proposed DaCOD on three widely-used challenging COD benchmark datasets, in which DaCOD outperforms the current state-of-the-arts by a large margin. All resources are available at https://***/qingwei-wang/DaCOD.

关键词： camouflaged object detection RGB-D Fusion Cross-modal Learning

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Emphasizing Boundary-Positioning and Leveraging Multi-scale Feature Fusion for camouflaged object detection 6th

Emphasizing Boundary-Positioning and Leveraging Multi-scale ...

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

作者： Li, Songlin Li, Xiuhong Li, Zhe Li, Boyuan Zhou, Chenyu Chen, Fan Qiu, Tianchi Li, Zeyu Xinjiang Univ Sch Informat Sci & Engn Xinjiang Peoples R China Hong Kong Polytech Univ Dept Elect & Elect Engn Hong Kong Peoples R China Dalian Minzu Univ Sch Comp Sci & Engn Dalian Peoples R China

ISBN: (纸本)9789819985548;9789819985555

camouflaged object detection (COD) aims to identify objects that blend in with their surroundings and have numerous practical applications. However, COD is a challenging task due to the high similarity between camouflaged objects and their surroundings. To address the problem of identifying camouflaged objects, we investigated how humans observe such objects. We found that humans typically first scan the entire image to obtain an approximate location of the target object. They then observe the differences between the boundary of the target object and its surrounding environment to refine their perception of the object. This continuous refinement process helps humans eventually identify the camouflaged object. Based on this observation, we propose a novel COD method that emphasizes boundary positioning and leverages multi-scale feature fusion. Our model includes two important modules: the Enhanced Feature Module (EFM) and the Boundary and Positioning joint-guided Feature Fusion Module (BPFM). The EFM provides multi-scale information and obtains aggregated feature representations, resulting in more robust feature representations for the initial positioning of the camouflaged object. In BPFM, we mimic human observation of camouflaged objects by injecting boundary and positioning information into each level of the backbone features, working together to refine the target object in blurred regions progressively. We validated the effectiveness of our model on three benchmark datasets (COD10K, CAMO, CHAMELEON), and the results showed that our proposed method significantly outperforms existing COD models.

关键词： camouflaged object detection boundary positioning multi-scale features contextual information

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EDGE-GUIDED PIXEL LEVEL CONNECTED COMPONENT ASSISTED camouflaged object detection 31

EDGE-GUIDED PIXEL LEVEL CONNECTED COMPONENT ASSISTED CAMOUFL...

引用

2024 International Conference on Image Processing

作者： Wang, Qingwang Qu, Xin Zhou, Liyao Jin, Pengcheng Fu, Chengbiao Shen, Tao Kunming Univ Sci & Technol Fac Informat Engn & Automat Kunming Yunnan Peoples R China

ISBN: (纸本)9798350349405;9798350349399

Due to the inherent visual similarity between the camouflaged object and background, camouflaged object detection (COD) is widely recognized as a challenging task in the field of computer vision, and traditional object detection networks often struggle to extract features and accurately identify camouflaged objects. In this paper, we propose an edge-guided pixel level connected component assisted network for COD. Specifically, the edge prior is used to guide object feature extraction and the pixel level connected component obtained from the extracted feature is used to refine the bounding box of the object. We selectively employ a gray-polarization COD dataset to showcase the ability of feature extraction from backgrounds where camouflaged objects may blend in or be occluded. Numerous experiments demonstrate the superiority of our method compared to state-of-the-arts in the case of limited information.

关键词： camouflaged object detection edge-guided feature extraction connected component multi-type information collaboration

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A MULTICHANNEL LOCALIZATION METHOD FOR camouflaged object detection 30

A MULTICHANNEL LOCALIZATION METHOD FOR CAMOUFLAGED OBJECT DE...

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30th IEEE International Conference on Image Processing (ICIP)

作者： Rahman, Md. Rakibur Chowdhury, Md. Mafri Sarkar, Md. Shohanur Rahaman Karim, Md. Shahriar North South Univ Elect & Comp Engn Dhaka Bangladesh

ISBN: (纸本)9781728198354

This paper proposes a multichannel method for discriminative region localization in camouflaged object detection (COD) tasks. In one channel, processing the phase and amplitude of 2-D Fourier spectra generate a modified form of the original image, used later for a pixel-wise optimal local entropy analysis. The other channel implements a class activation map (CAM) and Global Average Pooling (GAP) for object localization. We combine the channels linearly to form the final localized version of the COD images. Experimentation in multiple COD datasets demonstrates that the proposed method successfully localizes regions containing more than 80% of the camouflaged objects. Our proposed method does not require memoryintensive devices or prior training on particular image features, making it easily integrable into a resource-constrained environment. The proposed approach is also applicable to non-COD images.

关键词： camouflaged object detection Local Entropy Class Activation Map

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TINYCOD: Tiny and Effective Model for camouflaged object detection 48

TINYCOD: Tiny and Effective Model for Camouflaged Object Det...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Xing, Haozhe Gao, Shuyong Tang, Hao Mok, Tsui Qin Kang, Yanlan Zhang, Wenqiang Information Technology and Electrical Engineering Eth Zurich Zurich Switzerland Fudan University Academy for Engineering & Technology Shanghai China Fudan University School of Computer Science Shanghai China

ISBN: (纸本)9781728163277

关键词： camouflaged object detection edge features feature fusion image segmentation lightweight model

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Enhanced camouflaged object detection for Agricultural Pest Management: Insights from Unified Benchmark Dataset Analysis 21

Enhanced Camouflaged Object Detection for Agricultural Pest ...

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21st International Bhurban Conference on Applied Sciences and Technology, IBCAST 2024

作者： Hussain, Syed Muhammad Zaidi, Samiya Ali Hyder, Afsah Ali Rizvi, Syed Muhammad Farhan, Muhammad School of Science and Engineering Habib University Karachi Pakistan

ISBN: (纸本)9798331516680

In recent decades, the severity of climate change has led to a rise in the frequency of agricultural pest attacks on farms causing significant economic damage and food shortages. Effective management of pests, specifically camouflaged pests, poses significant challenges in agriculture, requiring accurate automated detection and segmentation. In this study, we leverage state-of-the-art object detection and segmentation models, specifically the single-stage YOLOv8 model, fine-tuned on a large-scale Unified Benchmark camouflaged object detection Dataset (UBCODD) consisting of 52,447 images. Furthermore, we extend our analysis to benchmark agricultural pest datasets such as IP-102 and the Locust-Mini Dataset, showcasing competitive performance metrics. This integrated approach allows us to capture agricultural camouflaged pests with greater detail and accuracy. Our findings lay the groundwork for the advancement of single-stage object detectors and segmentation models in the field of agriculture. Moreover, we contribute to open-source initiatives in agricultural technology by generating bounding box annotations for the entire IP-102 and binary masks for the Agricultural Pests Image Dataset. This research signifies a significant advancement in agricultural pest recognition and segmentation using cutting-edge computer vision technologies. © 2024 IEEE.

关键词： camouflaged object detection camouflaged Pests camouflaged Segmentation Pest detection UBCODD

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