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<p>Camouflaged object detection (COD) is a challenging task in computer vision, aiming to segment objects that seamlessly blend into their backgrounds. In the spatial domain, pixel-level information effectively captures salient regions in an image but is highly sensitive to small variations or noise. In contrast, the frequency domain, by separating high- and low-frequency components—especially utilizing the smoothing effect of low-frequency components—provides stronger robustness against noise. Therefore, this paper aims to explore a comprehensive fusion approach to enhance the model’s inference capabilities. We propose a novel cross-domain refinement network (CDRNet), which enhances information by establishing correlations and differences between the frequency and spatial domains, followed by iterative refinement of the prediction results. In the first stage of coarse segmentation, we introduce a domain correlation fusion (DCF) module, which uses spatial domain information to guide the frequency domain. Additionally, we design a domain difference convolution (DDC) to exploit the differences between the two domains to enhance spatial domain information. In the second stage of fine-grained optimization, we adopt a newly developed iterative refinement masking (IRM) to restore details. Experimental results across four COD datasets demonstrate that CDRNet achieves state-of-the-art performance. The source code is available at <a href="https://anonymous.4open.science/r/CDRNet-E4B0/">https://anonymous.4open.science/r/CDRNet-E4B0/</a>.</p>

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Unified cross-domain refinement network for camouflaged object detection

  • 期刊论文

  • 发表日期：

    2025年5月26日

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  • Qingzheng Wang,
  • Ning Li,
  • Jiazhi Xie,
  • Wenhui Liu,
  • Xingqin Wang,
  • Zengwei Mai

摘要

Camouflaged object detection (COD) is a challenging task in computer vision, aiming to segment objects that seamlessly blend into their backgrounds. In the spatial domain, pixel-level information effectively captures salient regions in an image but is highly sensitive to small variations or noise. In contrast, the frequency domain, by separating high- and low-frequency components—especially utilizing the smoothing effect of low-frequency components—provides stronger robustness against noise. Therefore, this paper aims to explore a comprehensive fusion approach to enhance the model’s inference capabilities. We propose a novel cross-domain refinement network (CDRNet), which enhances information by establishing correlations and differences between the frequency and spatial domains, followed by iterative refinement of the prediction results. In the first stage of coarse segmentation, we introduce a domain correlation fusion (DCF) module, which uses spatial domain information to guide the frequency domain. Additionally, we design a domain difference convolution (DDC) to exploit the differences between the two domains to enhance spatial domain information. In the second stage of fine-grained optimization, we adopt a newly developed iterative refinement masking (IRM) to restore details. Experimental results across four COD datasets demonstrate that CDRNet achieves state-of-the-art performance. The source code is available at https://anonymous.4open.science/r/CDRNet-E4B0/.