AI 中文总结
针对现有RGB-T目标检测方法的跨尺度知识继承不足、噪声抑制弱及信息退化问题，提出DRPFNet，通过结构、特征、增强三级协同优化，在公开数据集上实现了兼具竞争力与高效性的检测性能。
AI 中文摘要
RGB-热成像（RGB-T）目标检测旨在融合可见光和热成像模态的互补信息，以在不同光照和天气条件下实现鲁棒检测。当前方法通常采用注意力机制或Transformer在每个特征尺度独立执行跨模态融合，直接在空间域结合RGB和热成像特征，但仍存在显著局限：各尺度独立融合导致跨层级知识继承不足，缺乏双向优化难以持续抑制噪声，缺少频域-空间协作引发信息退化。为解决这些问题，我们提出DRPFNet，即双域残差渐进融合网络，其从结构、特征、增强三个协同层级构建统一信息流优化系统：结构层级通过自底向上的知识积累与双向增强建立跨尺度传播，确保信息流顺畅；特征层级通过频带分离与边缘引导协同提取RGB高频边缘和热成像低频结构，保证表征质量；增强层级通过边缘引导的双域细化增强前景-背景区分度，实现精确目标定位。在两个公开RGB-T数据集上的实验表明，我们的方法在达到竞争力性能的同时具备高效性，验证了该层级协同策略的有效性。
英文摘要
RGB-thermal (RGB-T) object detection aims to fuse complementary information from visible and thermal modalities to achieve robust detection under varying illumination and weather conditions. Current methods typically employ attention mechanisms or transformers to perform cross-modal fusion independently at each feature scale, directly combining RGB and thermal features in the spatial domain. However, they still face significant limitations: cross-level knowledge inheritance caused by independent fusion at each scale,suppressing noise continuously due to the lack of bidirectional optimization, and information degradation induced by the absence of frequency-spatial collaboration. To address these issues, we propose DRPFNet, a Dual-domain Residual Progressive Fusion Network that constructs a unified information flow optimization system from three synergistic levels:structure, feature, and enhancement. At the structural level, we establish cross-scale propagation through bottom-up knowledge accumulation and bidirectional enhancement,ensuring smooth information flow. At the feature level, we collaboratively extract RGB high-frequency edges and thermal low-frequency structures via frequency band separation and edge guidance, guaranteeing representation quality. At the enhancement level, we enhance foreground-background discrimination through edge-guided dual-domain refinement,achieving precise object localization.Extensive experiments on two public RGB-T datasets demonstrate that our method achieves competitive performance with competitive efficiency, validating the effectiveness of this hierarchical collaborative strategy.
CommentsAccepted at ICME 2026
