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UEDG:Uncertainty-Edge Dual Guided camouflage object detection

IEEE TRANSACTIONS ON MULTIMEDIA 2024年 26卷 4050-4060页

作者： Lyu, Yixuan Zhang, Hong Li, Yan Liu, Hanyang Yang, Yifan Yuan, Ding Beihang Univ Sch Astronaut Beijing 100191 Peoples R China Beihang Univ Inst Artificial Intelligence Beijing 100191 Peoples R China

According to Darwinian evolutionary theory, numerous species in the wild have developed remarkable adaptive mechanisms, involving pattern rearrangement and environmental assimilation, to evade predators. These obfuscation strategies pose significant challenges for both individuals and algorithms when performing the camouflage object detection (COD) task in complex and intricate scenarios. Inspired by human strategies in the COD task, which involve assigning uncertainties to the entire input and then focusing on highly uncertain areas with the aid of prior knowledge such as boundary information, we propose the Uncertainty-Edge Dual Guide (UEDG) architecture. UEDG effectively combines probabilistic-derived uncertainty and deterministic-derived edge information to accurately detect concealed objects. The architecture consists of two independent branches dedicated to uncertainty reasoning and edge inference, which are subsequently integrated into a feature fusion module utilizing recursion feedback and feature-reuse techniques. This novel COD framework leverages the benefits of Bayesian learning and convolution-based learning, resulting in a powerful multi-task guided approach. Extensive experiments conducted on four widely employed datasets demonstrate the superior performance of UEDG compared to 12 state-of-the-art approaches, while maintaining an acceptable level of computational complexity. Overall, UEDG presents a promising solution for addressing the challenges of COD in complex environments by combining evolutionary-inspired strategies with advanced computer vision techniques.

关键词： camouflage object detection multi-auxiliary tasks fusion uncertainty deduction

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Application of SAM-based prompting algorithm in camouflage object detection 5

Application of SAM-based prompting algorithm in camouflage o...

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5th International Conference on Computer Engineering and Application (ICCEA)

作者： Yuan, Jiatang Hubei Univ Technol Sch Comp Sci Wuhan Peoples R China

ISBN: (纸本)9798350386783;9798350386776

We propose a prompting algorithm based on SAM and apply it to the field of camouflage object detection. We extract the camouflaged object detection (COD) network features after weakly supervised learning and generate bounding box information and point prompt information to guide the SAM to segment the camouflaged objects in the image. The proposed method can effectively improve the performance of camouflage object detection network and has achieved good results on the mainstream camouflage object detection dataset.

关键词： SAM camouflage object detection Optimization algorithms

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Green fruit detection methods: Innovative application of camouflage object detection and multilevel feature mining

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2024年 225卷

作者： Zhai, Yuting Gao, Zongmei Zhou, Yang Li, Jian Zhang, Yuqi Xu, Yanlei Jilin Agr Univ Sch Informat Technol Changchun 130118 Peoples R China Washington State Univ Ctr Precis & Automated Agr Syst Dept Biol Syst Engn Prosser WA 99350 USA

Accurate detection of object fruits is essential for optimizing picking efficiency and predicting fruit yields. However, detecting early-stage ripening or green fruits, especially in complex fields, poses significant challenge due to their similarity to green leaves. This study introduces the TEAVit model, a novel camouflage object detection network specifically tailored for identifying green tomatoes in intricate agricultural environments. TEAVit incorporates a texture-edge-awareness module (TEAM) to enhance the extraction ability of texture feature by combining high-level and low-level features, an edge-guided feature module (EFM) to address background complexities and occlusions, and a context-aggregation module (CAM) to leverage contextual semantics. Experimental validation results demonstrate that the S-measure, E-measure, and F-measure performance metrics all exceed 90% on the tomato dataset, accompanied by a mean absolute error of 0.0245. These findings underpinned the effectiveness of the proposed green fruit camouflage object detection algorithm in offering new insights for agricultural target localization.

关键词： Deep learning Computer vision camouflage object detection Green tomato Transformer

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Attention Guided Multi-level Feedback Network for camouflage object detection 6th

Attention Guided Multi-level Feedback Network for Camouflage...

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6th Asian Conference on Pattern Recognition (ACPR)

作者： Tang, Qiuyan Ye, Jialin Chen, Fukang Yuan, Xia Nanjing Univ Sci & Technol Nanjing 210094 Peoples R China

ISBN: (纸本)9783031023750;9783031023743

camouflage object detection(COD)" refers to identify objects hidden in the surrounding environment, such as oriental scops owl in a tree hole. Nowadays, due to the high similarity between the camouflaged object and its dependent environment, coupled with the lack of large-scale datasets, the research on this task is still very challenging. Current COD models directly send feature maps output by the backbone into the encoding-decoding module and process them equally, which may cause information interference to a certain extent. In addition, regarding the disappearance of the underlying clues in DCNNs, these models have not been well resolved. This article carries out further research based on the existing models and proposes a novel model, AGMFNet. Specifically, we introduce channel attention and spatial attention to obtain more information we need and suppress useless information to avoid information interference. In order to make feature maps integrate better, the Inception module is utilized. Furthermore, the cascade decoding module is further expanded, and we proposed a multi-level feedback module with auxiliary edge information to refine the camouflage image, which can make full use of the high-level features while retaining the low-level clues. After a series of ablation experiments on the introduced modules on the test datasets, all the combinations can improve the performance, which will also help develop camouflage object detection. The code will be available at: https://***/baekqiu/AGMFNet-for-COD/

关键词： object detection camouflage object detection Multi-level feedback Attention mechanism Inception

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camouflage soldier object detection network based on the attention mechanism and pyramidal feature shrinking

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第33期83卷 79917页

作者： Peng, Yiguo Wang, Jianzhong Yu, Zibo You, Yu Sun, Yong Beijing Inst Technol Sch Mechatron Engn Beijing 100081 Peoples R China

Due to the high level of information similarity between camouflage soldier objects and their background, traditional deep learning-based object detection networks encounter distinct error detection rates and miss detection rates when attempting to detect camouflage soldiers. To address these challenges, we proposed a camouflage soldier object detection network (AFSNet) based on attention mechanism and multi-scale feature fusion strategy. We employed an attention module to enhance the network's capability for feature extraction. Furthermore, we proposed a novel strategy for multi-scale feature fusion based on pyramidal feature shrinking, aiming to mitigate interference caused by interpolation and prevent information loss resulting from pooling during the process of feature fusion. Moreover, we introduced a novel information handle module that enhances the network's capability for feature fusion by regulating the information transmission pathway. Experiments demonstrated that our network exhibits a better camouflage object detection performance than state-of-arts networks. Compared to YOLOv7, our network can achieve 93%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document} AP, which is increased by 6.7%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document} with almost no computation overhead.

关键词： camouflage object detection Attention mechanism Pyramidal feature shrinking

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IDCNet: iterative dual-channel network for camouflaged object detection

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DIGITAL SIGNAL PROCESSING 2025年 162卷

作者： Wang, Chuanjiang Liu, Baoqi Hou, Xiankai Li, Yuepeng Sun, Xiujuan Shandong Univ Sci & Technol Coll Elect Engn & Automat Qingdao 266590 Peoples R China Qingdao CCS Elect Corp Qingdao 266400 Peoples R China

Aiming at the problem that current camouflaged object detection in military operations, this paper proposes an iterative dual-channel camouflaged object detection network (IDCNet) for this task. This method employs a dual-channel architecture to explicitly assign the tasks of localization and edge refinement, which consist a robust localization channel and a global refinement channel. Position and channel attention mechanisms integrated into each high-level feature in the robust localization channel. The localization prediction map fused with the localization channel features through global attention to obtain the initial features for the global refinement channel. By incorporating a Mirror Multiplicative Attention mechanism and an attention-guided iterative zooming strategy, IDCNet achieves significant improvements in segmentation accuracy. This method only demonstrates outstanding performance on military camouflaged object datasets but also exhibits excellent performance on general camouflaged object datasets. The model's Structure-measure (S alpha) achieve 91.2% on military camouflage object dataset camouflageData. On the largest publicly disguised object dataset COD10K, S alpha reach 83.3%. These results underscore the potential of IDCNet to substantially enhance battlefield situational awareness and operational decision-making, paving the way for more robust camouflage object detection in world military applications.

关键词： Deep learning camouflage object detection Cross-layer feature fusion Iterative training

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Promoting camouflaged object detection through novel edge-target interaction and frequency-spatial fusion

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NEUROCOMPUTING 2025年 617卷

作者： Guan, Juwei Qian, Weiqi Zhu, Tongxin Fang, Xiaolin Southeast Univ Sch Comp Sci & Engn Nanjing 211189 Peoples R China Southeast Univ Key Lab Comp Network & Informat Integrat Minist Educ Nanjing Peoples R China

The realm of camouflage object detection endeavors to precisely discern concealed targets that blend seamlessly into their surroundings. Recently, numerous research endeavors have converged on unraveling the disguise tactics employed by such objects, striving for efficient and accurate detection results. While these methods have garnered notable advancements in unveiling camouflaged targets, they still grapple with challenges posed by the high similarity between objects and their backgrounds, resulting in misidentifications, detection oversights, and the loss of intricate details. In light of these challenges, this paper introduces an innovative EF Net, aimed at elevating the integrity of target recognition and the finesse of detail capture through deepened interactions between E dge-target features and F requency-spatial information. Notably, recognizing that the edges of camouflaged objects harbor a wealth of intrinsic details and serve as crucial delimiters distinguishing them from their surroundings, we have devised a dual-path architecture, where an auxiliary Edge detection (ED) branch collaborates seamlessly with the primary object Segmentation (OS) branch. Within the OS branch, we meticulously crafted the Edge-induced Segmentation Refinement module (ESR), which ingeniously harnesses the refined edge information provided by the ED branch to bolster target segmentation accuracy and augment detail fidelity. Moreover, in the supporting ED branch, we propose the Structure-induced Edge Enhancement module (SEE), designed to accentuate edge information while suppressing irrelevant noise with the assistance of the OS branch, thereby ensuring the precise extraction of edge information. To further enrich the information on dual-path, we innovatively introduce a Frequency Decomposition Module (FDM), which decomposes images into low- and high-frequency components, tailored to enhance target segmentation and edge detection, respectively. Additionally, to ensure seamless in

关键词： camouflage object detection Edge-target interaction Frequency-spatial fusion Frequency decomposition

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Double-Branch camouflaged object detection Method Based on Intra-Layer and Inter-Layer Information Integration

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IEEE ACCESS 2024年 12卷 81057-81066页

作者： Sun, Hang Zhang, Cong Northeast Petr Univ Sch Elect & Informat Engn Daqing 163318 Peoples R China

The color and texture of camouflaged objects closely resemble their backgrounds, making it difficult to distinguish between the target and the background. Additionally, camouflaged environments are complex, with some camouflaged objects overlapping significantly with the background, further complicating the identification of camouflaged target areas. To address these challenges, we present a novel double-branch camouflaged object detection algorithm, termed TDNet, inspired by biological vision and based on the classical convolutional neural network ResNet50. TDNet employs a two-stage target detection structure that utilizes location segmentation to accurately identify detection targets. Specifically, we introduce an internal feature interaction mechanism (IFE) that enhances camouflaged target feature representations through multi-dimensional spatial feature enhancement and multi-channel feature interaction, enabling the rough segmentation of camouflaged regions. To mitigate the issue of blurred boundaries of camouflaged targets, we devise a cross-layer feature fusion mechanism (CFU) to refine the borders of camouflaged regions. Moreover, we employ a multi-supervised approach to constrain the model's degradation process, ensuring the accuracy of camouflaged object detection. Experimental results demonstrate that our proposed method outperforms nine classical camouflaged object detection algorithms, achieving state-of-the-art performance. We will release the code and the test maps at https://***/zc199823/TDNet.

关键词： Feature extraction object detection Interference Image edge detection Task analysis Image color analysis Convolutional neural networks Spatial resolution camouflage object detection two-stage object detection multi-dimensional spatial feature enhancement cross-layer feature joint mechanism

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Polyp Segmentation Based on Multilevel Information Correction Transformer

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IEEE ACCESS 2024年 12卷 91619-91633页

作者： Yuan, Ju Liu, Guoqi Nam, Haewoon Hanyang Univ Dept Elect & Elect Engn Ansan 15588 South Korea Henan Normal Univ Coll Comp & Informat Engn Key Lab Artificial Intelligence & Personalized Le Xinxiang 453000 Peoples R China

This paper proposes a precise and reliable polyp segmentation method using multilevel Information Correction transformer. Unlike the conventional direct fusion approach, it harnesses the strengths of transformers in modeling global information and convolutional neural networks (CNN) in capturing local information. Deep learning methods allow doctors to achieve effective and reliable results, greatly reducing manual costs and improving accuracy. However, previous approaches have not achieved a good balance between incorporating local and global information, leading to segmentation results that are not entirely satisfactory. The proposed method uses an enriched multi-scale feature extractor module(EMFE) that generates reliable boundary attention maps through richer scales. Considering that feature maps at different stages of the encoder have different sizes, the proposed method applies a pyramid hybrid attention module(PHA) to filter out background noise information using different scales. We also devise a multilevel lesion correction module(MLC), utilizing Transformer blocks for target localization, followed by convolutional correction of uncertain regions, effectively harnessing the strengths of both methods to achieve accurate segmentation from coarse to fine. Finally, we introduce a feature selection fusion module(FSF), enabling the network to adaptively select relevant contextual information for further correction, thus achieving an effective balance between global and local information. Experimental results demonstrate significant superiority of our method over the top 17 state-of-the-art approaches on five publicly available datasets. In particular, the proposed method achieves an improvement of 3.36% in dice score and 3.17% in mIoU score compared to the most advanced methods on the ETIS dataset.

关键词： Transformers Task analysis Convolutional neural networks Feature extraction Image segmentation Accuracy Decoding Polyp segmentation camouflage object detection long-range dependencies local details correction

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YOLO-FEDER FUSIONNET: A NOVEL DEEP LEARNING ARCHITECTURE FOR DRONE detection 31

YOLO-FEDER FUSIONNET: A NOVEL DEEP LEARNING ARCHITECTURE FOR...

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2024 International Conference on Image Processing

作者： Lenhard, Tamara R. Weinmann, Andreas Jaeger, Stefan Koch, Tobias Inst Protect Terr Infrastructures German Aerosp Ctr DLR St Augustin Germany Univ Appl Sci Darmstadt Working Grp Algorithms Comp Vis Imaging & Data An Darmstadt Germany

ISBN: (纸本)9798350349405;9798350349399

Predominant methods for image-based drone detection frequently rely on employing generic object detection algorithms like YOLOv5. While proficient in identifying drones against homogeneous backgrounds, these algorithms often struggle in complex, highly textured environments. In such scenarios, drones seamlessly integrate into the background, creating camouflage effects that adversely affect the detection quality. To address this issue, we introduce a novel deep learning architecture called YOLO-FEDER FusionNet. Unlike conventional approaches, YOLO-FEDER FusionNet combines generic object detection methods with the specialized strength of camouflage object detection techniques to enhance drone detection capabilities. Comprehensive evaluations of YOLO-FEDER FusionNet show the efficiency of the proposed model and demonstrate substantial improvements in both reducing missed detections and false alarms.

关键词： Drone detection camouflage object detection feature fusion synthetic data

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