面向RGB-D显著目标检测的全局融合和递进解码网络

doi:10.16451/j.cnki.issn1003-6059.202607006

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Abstract Existing RGB-D salient object detection methods predominantly employ homogenized fusion strategies and neglect the inherent discrepancies across spatial and semantic dimensions. To address these issues, a global fusion and progressive decoding network for RGB-D salient object detection is proposed in this paper. An asymmetric dual-stream encoder architecture is utilized, and differentiated backbone networks tailored to the distinct information densities of RGB and depth modalities are designed. First, a detail-aware module is developed to replace traditional symmetric fusion. Global RGB texture features are employed as spatial guidance for the asymmetric filtering of low-quality depth features with fine boundaries preserved and sensor noise suppressed. Then, a semantic aggregation module is developed to construct parallel multi-scale receptive fields. A channel attention mechanism is introduced for cross-modal semantic purification, optimizating precise localization and semantic alignment for multi-scale targets. Finally, a refined decoding module is constructed to cooperate with large receptive fields to extract channel attention through a dual-branch residual structure, effectively compensating for information loss and achieving a progressive reconstruction of salient objects from high-level semantics to low-level details. Experiments on six public datasets demonstrate that the proposed network achieves superior detection performance.

Key words： RGB-D Salient Object Detection Attention Mechanism Cross-Modal Fusion Feature Fusion

Received: 02 April 2026

ZTFLH:

TP 3

Fund:Supported by the Open Project Program of Yunnan Province Key Laboratory of Low Light Detection and Intelligent Visual Navigation(No.2026-LLDIVN-GD-02-06), State Key Laboratory of Space Intelligent Control(No.HTKJ2025KL502006)

Corresponding Authors: WEI Longsheng, Ph.D., associate professor. His research interests include computer vision and pattern recognition.

About author:: MI Yichen, Master student. Her research interests include deep learning and RGB-D salient object detection.
ZHOU Zhanxiang, Master student. His research interests include co-salient object detection and EEG signal decoding.

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Cite this article:

WEI Longsheng,MI Yichen,ZHOU Zhanxiang. Global Fusion and Progressive Decoding Network for RGB-D Salient Object Detection[J]. Pattern Recognition and Artificial Intelligence, 2026, 39(7): 650-664.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202607006 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2026/V39/I7/650

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