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标题: "Is There Really a Camouflaged Object? Towards Realistic Camouflaged Object Detection"
原文链接: "https://arxiv.org/abs/2608.11135"
发布日期: "2026-08-11"
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## Computer Science > Computer Vision and Pattern Recognition

## Title:Is There Really a Camouflaged Object? Towards Realistic Camouflaged Object Detection

Authors:[Huafeng Chen](https://arxiv.org/search/cs?searchtype=author&query=Chen,+H), [Yueming Lyu](https://arxiv.org/search/cs?searchtype=author&query=Lyu,+Y), [Chenyang Si](https://arxiv.org/search/cs?searchtype=author&query=Si,+C), [Wende Tan](https://arxiv.org/search/cs?searchtype=author&query=Tan,+W), [Liucheng Guo](https://arxiv.org/search/cs?searchtype=author&query=Guo,+L), [Caifeng Shan](https://arxiv.org/search/cs?searchtype=author&query=Shan,+C)

[View PDF](https://arxiv.org/pdf/2608.11135) [HTML (experimental)](https://arxiv.org/html/2608.11135v1)

> Abstract:Camouflaged object detection (COD) aims to segment objects that are visually concealed in their surroundings and has attracted increasing attention in recent years. However, most existing COD methods are developed under a closed-world assumption, where each input image is assumed to contain a camouflaged object. This assumption ignores realistic scenarios with pure backgrounds or non-camouflaged objects, causing existing models to produce severe false positives when deployed in open-world environments. To address this limitation, we propose OPC16K, a large-scale benchmark for realistic COD. OPC16K contains 16,245 images from 14 sources and is carefully organized into camouflaged-object images, pure background images, and non-camouflaged-object images, enabling comprehensive evaluation of both segmentation quality and negative-sample rejection. Based on this benchmark, we further propose OPCNet, a presence-aware camouflage network that reformulates COD from a pure segmentation task into a joint problem of object localization and camouflage existence reasoning. Specifically, OPCNet introduces hierarchical existence reasoning to distinguish CO, BG, and NOCOD scenarios, similarity-aware camouflage relation modeling to capture foreground-background camouflage cues, and existence-aware feature refinement to regulate segmentation features with existence predictions. Extensive experiments on OPC16K demonstrate that OPCNet achieves superior performance under the proposed realistic COD evaluation protocol, significantly reducing false positives on negative samples while maintaining accurate camouflaged-object segmentation. Code and dataset will be released at [this https URL](https://github.com/2231122/OPCOD).

| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| --- | --- |
| Cite as: | [arXiv:2608.11135](https://arxiv.org/abs/2608.11135) \[cs.CV\] |
|  | (or [arXiv:2608.11135v1](https://arxiv.org/abs/2608.11135v1) \[cs.CV\] for this version) |
|  | [https://doi.org/10.48550/arXiv.2608.11135](https://doi.org/10.48550/arXiv.2608.11135) |

## Submission history

From: Huafeng Chen \[[view email](https://arxiv.org/show-email/ba943d97/2608.11135)\]  
**\[v1\]** Tue, 11 Aug 2026 16:57:40 UTC (2,789 KB)

[Which authors of this paper are endorsers?](https://arxiv.org/auth/show-endorsers/2608.11135) | Disable MathJax ([What is MathJax?](https://info.arxiv.org/help/mathjax.html))
