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<p>Camouflaged Object Detection (COD) aims to accurately segment objects hidden in their surroundings from images. Due to the limited information provided by RGB images, recent studies have begun incorporating depth information to assist detection. However, there are inherent differences between RGB and depth modalities, and how to effectively integrate information from both modes has become a key issue in RGB-D COD. Existing methods often directly fuse dual-modal features extracted by backbone networks, ignoring their differences and complementarity in feature representation. To address this issue, a novel Asymmetric Enhancement and Interaction Fusion Network (AEIFNet) for RGB-D COD is proposed. Specifically, an Asymmetric Feature Enhancement (AFE) module is designed to asymmetrically enhance RGB and depth features, effectively reducing the differences between modalities. Additionally, a Multi-modality Interaction Fusion (MIF) module is proposed to explore complementary information between modalities through interaction mechanisms and adopt an adaptive fusion strategy to mitigate the impact of low-quality depth maps. Finally, a Cross-layer Refinement Decoder (CRD) composed of multiple Cross-layer Refinement Modules (CRMs) is constructed to promote interaction and refinement of features at different levels, effectively balancing global semantic understanding and local detail representation for precise boundary and region prediction. Extensive experiments demonstrate that the proposed method achieves state-of-the-art performance compared to 13 advanced COD models on three public datasets.</p>

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AEIFNet: cross-modality asymmetric enhancement and interactive fusion network for RGB-D camouflaged object detection

  • 期刊论文

  • 发表日期：

    2026年5月6日

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  • Dongdong Zhang,
  • Huiying Wang,
  • Chunping Wang,
  • Qing Yang,
  • Zhaorui Li,
  • Qiang Fu

摘要

Camouflaged Object Detection (COD) aims to accurately segment objects hidden in their surroundings from images. Due to the limited information provided by RGB images, recent studies have begun incorporating depth information to assist detection. However, there are inherent differences between RGB and depth modalities, and how to effectively integrate information from both modes has become a key issue in RGB-D COD. Existing methods often directly fuse dual-modal features extracted by backbone networks, ignoring their differences and complementarity in feature representation. To address this issue, a novel Asymmetric Enhancement and Interaction Fusion Network (AEIFNet) for RGB-D COD is proposed. Specifically, an Asymmetric Feature Enhancement (AFE) module is designed to asymmetrically enhance RGB and depth features, effectively reducing the differences between modalities. Additionally, a Multi-modality Interaction Fusion (MIF) module is proposed to explore complementary information between modalities through interaction mechanisms and adopt an adaptive fusion strategy to mitigate the impact of low-quality depth maps. Finally, a Cross-layer Refinement Decoder (CRD) composed of multiple Cross-layer Refinement Modules (CRMs) is constructed to promote interaction and refinement of features at different levels, effectively balancing global semantic understanding and local detail representation for precise boundary and region prediction. Extensive experiments demonstrate that the proposed method achieves state-of-the-art performance compared to 13 advanced COD models on three public datasets.