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Detecting camouflaged objects via Multi-Stage Coarse-to-Fine Refinement

IEEE ACCESS 2024年 12卷 44055-44068页

作者： Wang, Yuye Chen, Tianyou Hu, Xiaoguang Shi, Jiaqi Jia, Zichong Minnan Normal Univ Coll Phys & Informat Engn Zhangzhou 363000 Fujian Peoples R China Cent China Normal Univ Fac Artificial Intelligence Educ Wuhan 430079 Hubei Peoples R China Beihang Univ State Key Lab Virtual Real Technol & Syst Beijing 100191 Peoples R China

camouflaged objects are typically assimilated into their surroundings. Consequently, in contrast to generic object detection/segmentation, camouflaged object detection proves to be considerably more intricate due to the indistinct boundaries and heightened intrinsic similarities between foreground targets and the surrounding environment. Despite the proposition of numerous algorithms that have demonstrated commendable performance across various scenarios, these approaches may still grapple with blurred boundaries, leading to the inadvertent omission of camouflaged targets in challenging scenes. In this paper, we introduce a multi-stage framework tailored for segmenting camouflaged objects through a process of coarse-to-fine refinement. Specifically, our network encompasses three distinct decoders, each fulfilling a unique role in the model. In the initial decoder, we introduce the Bi-directional Locating Module to excavate foreground and background cues, enhancing target localization. The second decoder focuses on leveraging boundary information to augment overall performance, incorporating the Multi-level Feature Fusion Module to generate prediction maps with finer boundaries. Subsequently, the third decoder introduces the Mask-guided Fusion Module, designed to process high-resolution features under the guidance of the second decoder's results. This approach enables the preservation of structural details and the generation of fine-grained prediction maps. Through the integration of the three decoders, our model effectively identifies and segments camouflaged targets. Extensive experiments are conducted on three commonly used benchmark datasets. The results of these experiments demonstrate that, even without the application of pre-processing or post-processing techniques, our model outperforms 14 state-of-the-art algorithms.

关键词： Decoding Feature extraction object detection object recognition Semantics Benchmark testing Background noise Convolutional neural networks Image analysis camouflaged object detection coarse-to-fine refinement convolutional neural network multi-stage detection

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TCU-Net: Transformer and Convolutional Neural Network-Based Advanced U-Net for Concealed object detection

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IEEE ACCESS 2022年 10卷 122347-122360页

作者： Park, Kyeong-Beom Lee, Jae Yeol Chonnam Natl Univ Dept Ind Engn Gwangju 61186 South Korea

camouflaged object detection (COD) seeks to find concealed objects hidden in natural surroundings. COD is challenging since it has to distinguish intrinsic similarities between foreground objects and background surroundings, unlike salient object detection. Convolutional neural network (CNN)-based approaches are proposed to overcome this challenge. However, they have inherent limitations in modeling and extracting global contexts. Although Transformer-based approaches are proposed to tackle this problem, which can maintain the semantic features of input images, they have limitations in learning localized spatial features in the limited receptive field. Therefore, one of the main challenges is to conduct accurate and robust COD while maintaining global contexts without sacrificing low-level contexts. This study proposes a novel concealed object detection and segmentation method using Transformer and CNN-based advanced U-Net (TCU-Net). TCU-Net can extract globalized semantic features using the Swin Transformer-based encoder and localized spatial features using the attentive Inception decoder. In particular, multi-dilated residual (MDR) blocks connecting the encoder and decoder generate refined multi-level features to improve discriminability. Finally, the attentive Inception decoder generates the final camouflaged object mask by maintaining the localized spatial information. Instead of simple up-sampling of the feature map, the attentive Inception decoder conducts cascaded deconvolution through Inception and attention modules. A weighted hybrid loss function is used for optimizing the model, consisting of the binary cross entropy (BCE) and intersection over union (IoU) losses. We comprehensively compared the proposed TCU-Net with previous studies by analyzing different metrics based on four public datasets, such as CAMO, CHAMELEON, COD10K, and NC4K. An ablation study was also conducted to evaluate network architectures and loss functions to verify advantages of the pr

关键词： Advanced U-Net camouflaged object detection attentive Inception decoder multi-dilatedresidual block Swin Transformer

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Finding camouflaged objects Along the Camouflage Mechanisms

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年第4期34卷 2346-2360页

作者： Yang, Yang Zhang, Qiang Xidian Univ Sch Mechanoelect Engn Xian 710071 Shaanxi Peoples R China

Common mechanisms for achieving object camouflage include reducing differences and increasing distractions. Such camouflage mechanisms hinder the object detectors to accurately distinguish the camouflaged objects from their surroundings. Considering that, we reexamine the camouflaged object detection (COD) task from the perspective of camouflage mechanisms and make the first attempt to discover the target objects in a de-camouflaging manner. We argue that this process can not only lead to a better understanding of camouflage, but also provide a new perspective for detecting camouflaged objects. For that, we first analyze some existing camouflage mechanisms together with their induced problems. Afterwards, considering the inner relationships between SOD and COD, we resort to the SOD task to synergistically achieve de-camouflaging for COD. Specifically, we incorporate the SOD task into the COD model and present a multi-task learning framework for COD, which models the intrinsic relationships between the two tasks from different perspectives, i.e., task-conflicting attribute and task-consistent attribute, to destroy the camouflage conditions for highlighting those inconspicuous yet valuable cues of camouflaged objects. In more detail, modeling the task-conflicting attribute is to well identify camouflaged objects by alleviating such interfering information from salient ones, and is achieved by a Gate Classification (GC) strategy and a Region Distraction Module (RDM). While, modeling the task-consistent attribute, which is achieved by an adversarial learning (AL) scheme and a Boundary Injection Module (BIM), is intended to enhance the boundary differences between the camouflaged objects and their backgrounds for fully segmenting the camouflaged objects. Extensive results demonstrate the superiorities of our proposed model over existing ones in camouflaged object detection.

关键词： Task analysis object detection Feature extraction Logic gates Multitasking Image segmentation Search problems camouflaged object detection salient object detection de-camouflaging task-consistent attribute task-conflicting attribute

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Confusing object detection:A Survey

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Computers, Materials & Continua 2024年第9期80卷 3421-3461页

作者： Kunkun Tong Guchu Zou Xin Tan Jingyu Gong Zhenyi Qi Zhizhong Zhang Yuan Xie Lizhuang Ma School of Computer Science and Technology East China Normal UniversityShanghai200062China Shanghai Institute of Ceramics Chinese Academy of SciencesShanghai200050China

Confusing object detection(COD),such as glass,mirrors,and camouflaged objects,represents a burgeoning visual detection task centered on pinpointing and distinguishing concealed targets within intricate backgrounds,leveraging deep learning *** garnering increasing attention in computer vision,the focus of most existing works leans toward formulating task-specific solutions rather than delving into in-depth analyses of methodological *** of now,there is a notable absence of a comprehensive systematic review that focuses on recently proposed deep learning-based models for these specific *** fill this gap,our study presents a pioneering review that covers both themodels and the publicly available benchmark datasets,while also identifying potential directions for future research in this *** current dataset primarily focuses on single confusing object detection at the image level,with some studies extending to video-level *** conduct an in-depth analysis of deep learning architectures,revealing that the current state-of-the-art(SOTA)COD methods demonstrate promising performance in single object *** also compile and provide detailed descriptions ofwidely used datasets relevant to these detection *** endeavor extends to discussing the limitations observed in current methodologies,alongside proposed solutions aimed at enhancing detection ***,we deliberate on relevant applications and outline future research trajectories,aiming to catalyze advancements in the field of glass,mirror,and camouflaged object detection.

关键词： Confusing object detection mirror detection glass detection camouflaged object detection deep learning

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Bidirectional Collaborative Mentoring Network for Marine Organism detection and Beyond

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2023年第11期33卷 6595-6608页

作者： Cheng, Jinguang Wu, Zongwei Wang, Shuo Demonceaux, Cedric Jiang, Qiuping Ningbo Univ Fac Informat Sci & Engn Ningbo 315211 Peoples R China Univ Bourgogne Franche Comt ImViA F-21000 Dijon France Swiss Fed Inst Technol Comp Vis Lab CH-8092 Zurich Switzerland Beijing Normal Univ Sch Syst Sci Beijing 100875 Peoples R China

Organism detection plays a vital role in marine resource exploitation and marine economy. How to accurately locate the target organism object within the camouflaged and dark light oceanic scene has recently drawn great attention in the research community. Existing learning-based works usually leverage local texture details within a neighboring area, with few methods explicitly exploring the usage of contextualized awareness for accurate object detection. From a novel perspective, we in this work present a Bidirectional Collaborative Mentoring Network (BCMNet) which fully explores both texture and context clues during the encoding and decoding stages, making the cross-paradigm interaction bidirectional and improving the scene understanding at all stages. Specifically, we first extract texture and context features through a dual-branch encoder and attentively fuse them through our adjacent feature fusion (AFF) block. Then, we propose a structure-aware module (SAM) and a detail-enhanced module (DEM) to form our two-stage decoding pipeline. On the one hand, our SAM leverages both local and global clues to preserve morphological integrity and generate an initial prediction of the target object. On the other hand, the DEM explicitly explores long-range dependencies to refine the initially predicted object mask further. The combination of SAM and DEM enables better extracting, preserving, and enhancing the object morphology, making it easier to segment the target object from the camouflaged background with sharp contour. Extensive experiments on three benchmark datasets show that our proposed BCMNet performs favorably over state-of-the-art models. The code will be made available at https://***/chasecjg/BCMNet.

关键词： Marine organism detection camouflaged object detection deep learning morphology

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Detecting camouflaged objects via cross-level context supplement

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APPLIED INTELLIGENCE 2024年第20期54卷 9685-9705页

作者： Zhang, Qing Yan, Weiqi Zhao, Rui Shi, Yanjiao Zeng, Jian Shanghai Inst Technol Sch Comp Sci & Informat Engn Shanghai 101418 Peoples R China

camouflaged object detection (COD) is to distinguish the target objects with varied sizes and shapes from the low-contrast real-world scenarios. Although deep learning-based methods have made great progress, it is still challenging to accurately detect and segment the complete and edge-preserving camouflaged objects. In this paper, we propose a novel cross-level context supplement network (CCSNet) to effectively utilize cross-level features to provide additional information, which can compensate for the deficiencies of the current level potentials. Specifically, we develop a selective cross-level aggregation (SCA) module to fully explore the cross-level different but complementary cues to detect camouflaged objects with different scales. It makes each level of the network adaptively focus on the informative features with the assist of the channel dependencies and spatial relationship provided by the adjacent levels. Furthermore, considering that the camouflaged objects are hardly distinguishable from the backgrounds, we design a location and boundary supplement (LBS) module to directly incorporate the global and edge information to different levels, thus enhancing the spatial coherence of interior regions and reducing the uncertainty of boundary regions. Comprehensive experiments are conducted on four public datasets to demonstrate the effectiveness of our CCSNet. In addition, we apply our model to two other dense pixel prediction tasks to further demonstrate the effectiveness and generalization ability of our network. The code, trained model and predicted maps will be available upon acceptance of this paper for publication.

关键词： camouflaged object detection Cross-level feature fusion Boundary cues Global context

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camouflaged objects detection network via contradiction detection and feature aggregation

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第3期83卷 9161-9179页

作者： Bi, Hongbo Tong, Jinghui Zhang, Cong Mo, Disen Wang, Xiufang Northeast Petr Univ Sch Elect Informat Engn Daqing 163000 Hei Longjiang Peoples R China

camouflaged object detection(COD) aims to segment objects with a similar appearance to the background. There are some problems in existing algorithms, such as blurred edges and incomplete detection. To address the above issues, we propose a novel COD framework termed CFNet. Our network consists of the Contradiction Area detection Module(CADM) and Feature Aggregation Module(FAM). In the CADM, we propose an improved receptive field mechanism, which utilizes max pooling operation and convolution block to highlight the contradictory areas and refine the edge of the hidden object. Besides, the FAM is designed to connect two adjacent layers via attention and anti-attention strategy, leading to cross-layer feature enhancement and information fusion. More specifically, the self-attention mechanism helps supplement semantic information, and the anti-attention mechanism contributes to removing redundant information. Extensive experiments conducted on the four public COD datasets show the comparable performance of the proposed CFNet with SOTAs, and the ablation experiments demonstrate the effectiveness of the proposed modules.

关键词： camouflaged object detection Contradiction detection Feature aggregation Attention mechanism

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Utilizing Grounded SAM for self-supervised frugal camouflaged human detection 34

Utilizing Grounded SAM for self-supervised frugal camouflage...

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Conference on Automatic Target Recognition XXXIV

作者： Pijarowski, Matthias Wolpert, Alexander Heckmann, Martin Teutsch, Michael Hensoldt Optron GmbH Oberkochen Germany Aalen Univ Appl Sci Aalen Germany

ISBN: (纸本)9781510673977;9781510673960

Visually detecting camouflaged objects is a hard problem for both humans and computer vision algorithms. Strong similarities between object and background appearance make the task significantly more challenging than traditional object detection or segmentation tasks. Current state-of-the-art models use either convolutional neural networks or vision transformers as feature extractors. They are trained in a fully supervised manner and thus need a large amount of labeled training data. In this paper, both self-supervised and frugal learning methods are introduced to the task of camouflaged object detection (COD). The overall goal is to fine-tune two COD reference methods, namely SINet-V2 and HitNet, pre-trained for camouflaged animal detection to the task of camouflaged human detection. Therefore, we use the public dataset CPD1K that contains camouflaged humans in a forest environment. We create a strong baseline using supervised frugal transfer learning for the fine-tuning task. Then, we analyze three pseudo-labeling approaches to perform the fine-tuning task in a self-supervised manner. Our experiments show that we achieve similar performance by pure self-supervision compared to fully supervised frugal learning.

关键词： camouflaged object detection frugality self-supervision foundation models object segmentation

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FINDING camouflaged object GUIDED BY CONTOUR AND ATTENTION 30

FINDING CAMOUFLAGED OBJECT GUIDED BY CONTOUR AND ATTENTION

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30th IEEE International Conference on Image Processing (ICIP)

作者： Cui, Junjie Sun, Fengming Yuan, Xia Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Peoples R China

ISBN: (纸本)9781728198354

camouflaged object detection aims to detect objects closely blended into the background. Inspired by visual mechanism of human being, we define detection process as an end-to-end task consisted of locating and refining. To this end, we propose Contour Supervision and Initial Locating Guidance Network (CSIGNet) to effectively segment camouflaged objects from background. Specifically, our method fully explores the contribution of semantic contour to the binary segmentation task. In addition, attention mechanism is used for our final prediction. Experiments show that it has achieved excellent results on public datasets and the model can accurately segment camouflage objects. Codes will be made available at https://***/RecKono/CSIGNet.

关键词： camouflaged object detection

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camouflaged object Segmentation Based on Fractional Edge Perception 6th

Camouflaged Object Segmentation Based on Fractional Edge Per...

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

作者： Yuan, Xia Cui, Junjie Liu, Zhengyu Yang, Shuting Zhang, Xuejian Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China Ningxia Acad Agr & Forestry Sci Inst Agr Econ & Informat Technol Yinchuan 750002 Ningxia Peoples R China

ISBN: (纸本)9789819985548;9789819985555

camouflaged object detection is a challenging task because of intrinsic similarity between background and foreground. In the existing models, many confusing edge features eventually lead to bad predictions. In this paper, we propose an Interactive Task Learning Network, in which an improved fractional-order differential operator is used to calculate the gradient intensity of the image. By calculating average gradient and spatial frequency of image, it can adaptively learn the fractional-order v and extract features from different directions. Experiments on three camouflaged datasets indicate that the proposed method is effective and progressiveness.

关键词： camouflaged object detection Fractional edge Interactive task learning

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