Survey of Object Detection Methods in Camouflaged Image

Singh, Shuchi; Dhawale, Chitra A.; Misra, Sanjay

doi:10.1016/j.ieri.2013.11.050

Cited by 51 publications

(11 citation statements)

References 11 publications

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“…In the early stage, traditional methods usually employ handcrafted features to detect camouflage objects or perform the work related to camouflage objects [25]. Xue et al [26] proposed a nonlinear feature fusion method to evaluate the camouflage degree of the target object.…”

Section: A Camouflaged Object Detectionmentioning

confidence: 99%

D$^{2}$C-Net: A Dual-Branch, Dual-Guidance and Cross-Refine Network for Camouflaged Object Detection

Wang

Zhang

et al. 2022

IEEE Trans. Ind. Electron.

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In this work, we propose a novel framework for camouflaged object detection (COD), named D 2 C-Net, which contains two new modules: dual-branch features extraction (DFE) and gradually refined cross fusion (GRCF). Specifically, the DFE simulates the two-stage detection process of human visual mechanisms in observing camouflage scenes. For the first stage, a dense concatenation is employed to aggregate multi-level features and expand the receptive field. The first stage feature maps are then utilized to extract two-direction guidance information, which benefits the second stage. The GRCF consists of a selfrefine attention unit and a cross-refinement unit, with the aim of combining the peer layer features and DFE features for an improved COD performance. The proposed framework outperforms 13 state-of-the-art deep learning-based methods upon three public datasets in terms of five widely used metrics. Finally, we show evidence for the successful applications of the proposed method in the fields of surface defect detection, medical image segmentation.

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Section: A Camouflaged Object Detectionmentioning

confidence: 99%

D$^{2}$C-Net: A Dual-Branch, Dual-Guidance and Cross-Refine Network for Camouflaged Object Detection

Wang

Zhang

et al. 2022

IEEE Trans. Ind. Electron.

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“…The term "camouflage" was originally used to describe the behavior of an animal trying to hide itself from its surroundings to hunt or avoid being hunted [1], namely naturally camouflaged objects [2]. Human adopted this and began to apply it widely on the battlefield.…”

Section: Introductionmentioning

confidence: 99%

Camouflaged Instance Segmentation In-The-Wild: Dataset, Method, and Benchmark Suite

Le,

Cao,

Nguyen

et al. 2021

Preprint

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This paper pushes the envelope on camouflaged regions to decompose them into meaningful components, namely, camouflaged instances. To promote the new task of camouflaged instance segmentation, we introduce a new large-scale dataset, namely CAMO++, by extending our preliminary CAMO dataset (camouflaged object segmentation) in terms of quantity and diversity. The new dataset substantially increases the number of images with hierarchical pixel-wise ground-truths. We also provide a benchmark suite for the task of camouflaged instance segmentation. In particular, we conduct extensive evaluation of state-of-the-art instance segmentation detectors on our newly constructed CAMO++ dataset in various scenarios. The dataset, evaluation suite, and benchmark will be publicly available at our project page. 1

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“…The term "camouflage" was originally used to describe the behavior of an animal or insect trying to hide itself from its surroundings to hunt or avoid being hunted [1], Figure 1: A few examples of camouflaged objects in CAMO dataset [2]. From left to right, camouflaged objects (e.g., fish, chameleon, insect) are followed by artificial camouflaged objects (e.g., solider, body painting).…”

Section: Introductionmentioning

confidence: 99%

MirrorNet: Bio-Inspired Camouflaged Object Segmentation

Yan,

Le,

Nguyen

et al. 2020

Preprint

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Camouflaged objects are generally difficult to be detected in their natural environment even for human beings. In this paper, we propose a novel bio-inspired network, named the MirrorNet, that leverages both instance segmentation and adversarial attack for the camouflaged object segmentation. Differently from existing networks for segmentation, our proposed network possesses two segmentation streams: the main stream and the adversarial stream corresponding with the original image and its flipped image, respectively. The output from the adversarial stream is then fused into the main stream's result for the final camouflage map to boost up the segmentation accuracy. Extensive experiments conducted on the public CAMO dataset demonstrate the effectiveness of our proposed network. Our proposed method achieves 89% in accuracy, outperforming the state-of-the-arts.

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Survey of Object Detection Methods in Camouflaged Image

Cited by 51 publications

References 11 publications

D$^{2}$C-Net: A Dual-Branch, Dual-Guidance and Cross-Refine Network for Camouflaged Object Detection

D$^{2}$C-Net: A Dual-Branch, Dual-Guidance and Cross-Refine Network for Camouflaged Object Detection

Camouflaged Instance Segmentation In-The-Wild: Dataset, Method, and Benchmark Suite

MirrorNet: Bio-Inspired Camouflaged Object Segmentation

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