Redundancy-Guided Hierarchical Fusion for RGBT Object Detection

In dual-branch RGBT object detection, symmetric architectures are widely adopted without examining whether different feature levels exhibit distinct redundancy patterns. In this paper, we first analyze the weight sparsity of a dual-branch model and observe that redundancy increases with depth, and at deeper layers the RGB branch becomes substantially more redundant than the thermal branch. Based on this observation, we propose a redundancy-guided hierarchical fusion strategy (Red-HiFusion)—feature concatenation at shallow low-redundancy levels, bidirectional cross-attention at middle medium-redundancy levels, and unidirectional IR-query attention at deep asymmetric high-redundancy levels to allow the less redundant thermal features guide the more redundant RGB features. Red-HiFusion achieves 85.4% mAP50 on M3FD and 97.4% mAP50 on LLVIP with only 14.9 M parameters and 35.0 GFLOPs. Extensive ablations show that this hierarchical design consistently outperforms full-bidirectional and full-unidirectional baselines while adding negligible computation. Compared to state-of-the-art approaches, our model attains competitive detection accuracy and has an order of magnitude fewer parameters, offering an interpretable and efficient solution for lightweight RGBT detection.