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Boundary-Guided Camouflaged Object Detection

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arxiv 2207.00794 v1 pith:KU343SU6 submitted 2022-07-02 cs.CV

Yujia Sun , Shuo Wang , Chenglizhao Chen , Tian-Zhu Xiang This is my paper · ORCID
classification cs.CV
keywords objectcamouflageddetectionbgnetboundary-guidedchallengingexistingmethods
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Camouflaged object detection (COD), segmenting objects that are elegantly blended into their surroundings, is a valuable yet challenging task. Existing deep-learning methods often fall into the difficulty of accurately identifying the camouflaged object with complete and fine object structure. To this end, in this paper, we propose a novel boundary-guided network (BGNet) for camouflaged object detection. Our method explores valuable and extra object-related edge semantics to guide representation learning of COD, which forces the model to generate features that highlight object structure, thereby promoting camouflaged object detection of accurate boundary localization. Extensive experiments on three challenging benchmark datasets demonstrate that our BGNet significantly outperforms the existing 18 state-of-the-art methods under four widely-used evaluation metrics. Our code is publicly available at: https://github.com/thograce/BGNet.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Rethinking Detecting Salient and Camouflaged Objects in Unconstrained Scenes

    cs.CV 2024-12 conditional novelty 7.0 of 10

    A new dataset, model, and metric extend salient and camouflaged object detection to unconstrained scenes where the two object types can coexist.

  2. MSRNet: A Multi-Scale Recursive Network for Camouflaged Object Detection

    cs.CV 2025-11 conditional novelty 5.0 of 10

    MSRNet, a multi-scale recursive network with attention-based scale integration and recursive-feedback decoding, reports state-of-the-art or runner-up camouflaged object detection on four standard benchmarks.

  3. DRRNet: Macro-Micro Feature Fusion and Dual Reverse Refinement for Camouflaged Object Detection

    cs.CV 2025-05 conditional novelty 5.0 of 10

    DRRNet is a four-stage camouflaged object detection network that fuses global and local features and then applies two rounds of reverse refinement to sharpen object boundaries.

  4. Promoting SAM for Camouflaged Object Detection via Selective Key Point-based Guidance

    cs.CV 2025-05 conditional novelty 5.0 of 10

    A lightweight point-prompt generator enables frozen SAM to reach state-of-the-art camouflaged object detection on several benchmarks.

  5. B2Net: Camouflaged Object Detection via Boundary Aware and Boundary Fusion

    cs.CV 2024-12 conditional novelty 4.0 of 10

    B2Net, a boundary-aware network that reuses a boundary module and fuses boundary cues across scales, reports state-of-the-art camouflaged object detection results on three public benchmarks.

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