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<p>In recent years, multi-modal salient object detection methods (e.g., RGB-D or RGB-T) have made significant progress in complex scene applications. However, existing methods often neglect edge feature enhancement and detail preservation, which are crucial for the clarity and accuracy of foreground-background separation. Additionally, effectively highlighting spatially related features, balancing local and global information, and suppressing background noise remain major challenges. To address these issues, we propose a new enhanced squeezed directional large kernel multi-scale multi-modal fusion network, named SDLK-Net, to optimize the separation of foreground and background and enhance detail capture ability. Specifically, we introduce a Squeezed Large Kernel Edge-Enhanced Fusion (SLKEF) method, which combines the squeeze operation with bidirectional large kernel convolutions to effectively enhance the separation between foreground and background, achieving the regional integrity and edge clarity of salient object segmentation results. In addition, we develop an Adaptive Extraction Multi-path Matching (AEMM) method, which enhances the fine-grained feature representation of the ROI region through path decoupling and fusion to help better capture the detailed information. Lastly, we design a Multi-Scale Filter Gate Integration (MSFGI) model, which flexibly balances local and global contextual information through dynamic filtering and gating mechanisms, ensuring the highlighting of spatially related features while effectively suppressing background noise. Experimental results demonstrate that SDLK-Net significantly improves detection accuracy in RGB-D and RGB-T salient object detection tasks, outperforming existing methods and achieving state-of-the-art performance across multiple datasets.</p>

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SDLK-Net: Enhanced squeezed directional large kernel multi-scale multi-modal fusion network for salient object detection

  • 期刊论文

  • 发表日期：

    2026年1月7日

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  • Lingyu Yan,
  • Ting Zhou,
  • Rong Gao,
  • Zengmao Wang,
  • Zhiwei Ye,
  • Xinyun Wu

摘要

In recent years, multi-modal salient object detection methods (e.g., RGB-D or RGB-T) have made significant progress in complex scene applications. However, existing methods often neglect edge feature enhancement and detail preservation, which are crucial for the clarity and accuracy of foreground-background separation. Additionally, effectively highlighting spatially related features, balancing local and global information, and suppressing background noise remain major challenges. To address these issues, we propose a new enhanced squeezed directional large kernel multi-scale multi-modal fusion network, named SDLK-Net, to optimize the separation of foreground and background and enhance detail capture ability. Specifically, we introduce a Squeezed Large Kernel Edge-Enhanced Fusion (SLKEF) method, which combines the squeeze operation with bidirectional large kernel convolutions to effectively enhance the separation between foreground and background, achieving the regional integrity and edge clarity of salient object segmentation results. In addition, we develop an Adaptive Extraction Multi-path Matching (AEMM) method, which enhances the fine-grained feature representation of the ROI region through path decoupling and fusion to help better capture the detailed information. Lastly, we design a Multi-Scale Filter Gate Integration (MSFGI) model, which flexibly balances local and global contextual information through dynamic filtering and gating mechanisms, ensuring the highlighting of spatially related features while effectively suppressing background noise. Experimental results demonstrate that SDLK-Net significantly improves detection accuracy in RGB-D and RGB-T salient object detection tasks, outperforming existing methods and achieving state-of-the-art performance across multiple datasets.