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This paper introduces DRPFNet, a Dual-domain Residual Progressive Fusion Network designed to enhance RGB-thermal object detection by addressing limitations in existing methods related to cross-modal fusion and information degradation. By implementing a unified information flow optimization system across structural, feature, and enhancement levels, DRPFNet ensures effective knowledge propagation and improved representation quality through frequency band separation and edge guidance. Experimental results on two public RGB-T datasets show that DRPFNet achieves competitive performance and efficiency, highlighting the effectiveness of its hierarchical collaborative strategy.
Bidirectional optimization in DRPFNet leads to superior object localization by effectively merging RGB high-frequency edges with thermal low-frequency structures.
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.
