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

RGB–Thermal (RGB-T) salient object detection leveragescomplementary cues from visible and thermal modalities to improve ro-bustness in challenging environments. However, in real-world scenarios,the reliability of each modality is inherently unstable: RGB images de-grade under low illumination, motion blur, and noise, while thermal im-agery often suffers from contrast compression and sensor artifacts. Suchdegradation introduces unreliable perceptual evidence that can misleadcross-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 re-liability and integrates it into feature learning and cross-modal fusion.First, we introduce a reliability-conditioned representation that adap-tively compensates degraded modality features while preserving struc-tural cues. Second, an uncertainty-guided dual-stream refinement strat-egy progressively corrects cross-modal representations while suppress-ing unreliable evidence. Finally, we propose a pixel-wise modality com-petition mechanism that dynamically selects modality cues accordingto spatial reliability for fine-grained fusion. Extensive experiments onfour benchmarks (VT821, VT1000, VT5000, and VT-IMAG) demon-strate that RA-SOD achieves state-of-the-art performance and exhibitsstrong robustness under severe modality degradation. Code and modelsare available at https://github.com/zaoxienian/RA-SOD.