RA-SOD: Reliability-Aware RGB-T Salient Object Detection under Modality Degradation

2026-09-11

AI Summary

为解决RGB-T图像在复杂环境下的模态退化问题，提出RA-SOD框架，通过建模模态可靠性并优化特征学习和跨模态融合来提高检测性能。

Abstract

RGB-Thermal (RGB-T) salient object detection leverages complementary cues from visible and thermal modalities to improve robustness in challenging environments. However, in real-world scenarios, the reliability of each modality is inherently unstable: RGB images degrade under low illumination, motion blur, and noise, while thermal imagery often suffers from contrast compression and sensor artifacts. Such degradation introduces unreliable perceptual evidence that can mislead cross-modal fusion and significantly deteriorate detection performance. To address this challenge, we propose RA-SOD, a reliability-aware RGB-T salient object detection framework that explicitly models modality reliability and integrates it into feature learning and cross-modal fusion. First, we introduce a reliability-conditioned representation that adaptively compensates degraded modality features while preserving structural cues. Second, an uncertainty-guided dual-stream refinement strategy progressively corrects cross-modal representations while suppressing unreliable evidence. Finally, we propose a pixel-wise modality competition mechanism that dynamically selects modality cues according to spatial reliability for fine-grained fusion. Extensive experiments on four benchmarks (VT821, VT1000, VT5000, and VT-IMAG) demonstrate that RA-SOD achieves state-of-the-art performance and exhibits strong robustness under severe modality degradation. Code and models are available at https://github.com/zaoxienian/RA-SOD.

Problem

• modality degradation
• reliable perception
• cross-modal fusion
• RGB-Thermal salient object detection

Innovation

• reliability-aware
• cross-modal fusion
• uncertainty-guided refinement
• pixel-wise modality competition