🤖 AI Summary
This work addresses the limitation of existing RGB-infrared object detection methods, which discard spectral statistical information during cross-modal fusion, thereby precluding reliable assessment of fusion quality. To overcome this, the study introduces a novel framework that explicitly extracts a parameter-free 7-dimensional spectral reliability descriptor and reuses it throughout subsequent computations. Specifically, it proposes Spectral Reliability Fusion (SRF) and Reliability-Conditioned Expert Routing (RCER) mechanisms to enable adaptive gated fusion and sparse expert selection. Evaluated on the DroneVehicle dataset under six synthetic degradation scenarios, the method achieves an average retention rate of 95.0% and improves mAP50 by 5.2 and 5.3 points in daytime and nighttime settings, respectively, significantly outperforming content-only baseline approaches.
📝 Abstract
RGB-infrared detectors typically discard the statistics generated during cross-modal fusion, leaving downstream modules unaware of whether the current interaction is reliable. We propose to extract a parameter-free, 7-dimensional spectral reliability descriptor -- summarizing band energy, amplitude ratio, phase consistency, and cross-modal correlation -- and to reuse it beyond the fusion stage. The descriptor drives both Spectral Reliability Fusion (SRF), which gates a spectral residual against a conservative spatial base, and Reliability-Conditioned Expert Routing (RCER), which combines the descriptor with pooled content to steer sparse post-fusion experts. Under matched ablations, descriptor-aware gating improves mAP50 over content-only adaptive gating; a $2{\times}2$ factorial analysis further shows that descriptor-conditioned routing provides the larger marginal gain over expert architecture alone at near-equal parameter count. Under six synthetic degradations on DroneVehicle, average retention rises to 95.0%, versus 92.0% for content-only MoE and 87.9% for concatenation, with the largest gain under modality drop; the same model also improves mAP50 by +5.2/+5.3 on the natural day/night split. These results suggest that preserving fusion-time reliability as an explicit signal benefits both adaptive fusion and post-fusion conditional computation.