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<p>Aiming at the challenges of insufficient multi-modal feature interaction, spatial-spectral misalignment, and poor robustness to target deformation and occlusion in RGB-T salient object detection, this paper proposes a detection framework (CMFGP-Net) based on cross-modal global perception and multi-scale cross-fusion. The framework adopts Swin Transformer with a hierarchical window attention mechanism as the backbone network, which significantly enhances the global context modeling capability. A cross-modal feature global perception (CMFGP) module is designed; through temporal correlation modeling and dynamic feature calibration mechanisms, it effectively alleviates the spatial and spectral misalignment between visible light and thermal infrared modalities. Furthermore, a multi-scale deformable convolutional cross-fusion (MSDCIF) module is introduced, which uses deformable convolution kernels to adaptively adjust the receptive field, enhancing the model’s adaptability to target deformation, occlusion, and complex backgrounds. Experiments on three mainstream RGB-T datasets (VT821, VT1000 and VT5000) demonstrate that CMFGP-Net outperforms existing state-of-the-art methods in key metrics such as E-measure, weighted F-measure and MAE. Ablation experiments further verify the effectiveness of each module and its contribution to performance improvement, and the model exhibits stronger robustness and generalization ability especially in complex scenarios.</p>

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CMFGP-Net: RGBT salient object detection based on cross-modal feature global perception and multi-scale deformable convolution fusion

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月22日

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  • Lijuan Shi,
  • Haitang Li,
  • Qiuju Liu

摘要

Aiming at the challenges of insufficient multi-modal feature interaction, spatial-spectral misalignment, and poor robustness to target deformation and occlusion in RGB-T salient object detection, this paper proposes a detection framework (CMFGP-Net) based on cross-modal global perception and multi-scale cross-fusion. The framework adopts Swin Transformer with a hierarchical window attention mechanism as the backbone network, which significantly enhances the global context modeling capability. A cross-modal feature global perception (CMFGP) module is designed; through temporal correlation modeling and dynamic feature calibration mechanisms, it effectively alleviates the spatial and spectral misalignment between visible light and thermal infrared modalities. Furthermore, a multi-scale deformable convolutional cross-fusion (MSDCIF) module is introduced, which uses deformable convolution kernels to adaptively adjust the receptive field, enhancing the model’s adaptability to target deformation, occlusion, and complex backgrounds. Experiments on three mainstream RGB-T datasets (VT821, VT1000 and VT5000) demonstrate that CMFGP-Net outperforms existing state-of-the-art methods in key metrics such as E-measure, weighted F-measure and MAE. Ablation experiments further verify the effectiveness of each module and its contribution to performance improvement, and the model exhibits stronger robustness and generalization ability especially in complex scenarios.