# DRPFNet: Dual-domain Residual Progressive Fusion Network for RGB-Thermal Object Detection

arXiv: https://arxiv.org/abs/2608.03370 · HTML: https://mlsanity.com/paper/2608.03370/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 41 references that are in the map.
- First posted on arXiv 4 Aug 2026.

## Builds on (most cited first, 17 of 17)

- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Target-aware Dual Adversarial Learning and a Multi-scenario Multi-Modality Benchmark to Fuse Infrared and Visible for Object Detection](https://mlsanity.com/paper/2203.16220/) (2022)
- [LLVIP: A Visible-infrared Paired Dataset for Low-light Vision](https://mlsanity.com/paper/2108.10831/) (2021)
- [CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image Fusion](https://mlsanity.com/paper/2211.14461/) (2022)
- [Equivariant Multi-Modality Image Fusion](https://mlsanity.com/paper/2305.11443/) (2023)
- [Cross-Modality Fusion Transformer for Multispectral Object Detection](https://mlsanity.com/paper/2111.00273/) (2021)
- [Improving Multispectral Pedestrian Detection by Addressing Modality Imbalance Problems](https://mlsanity.com/paper/2008.03043/) (2020)
- [ICAFusion: Iterative Cross-Attention Guided Feature Fusion for Multispectral Object Detection](https://mlsanity.com/paper/2308.07504/) (2023)
- [Illumination-aware Faster R-CNN for Robust Multispectral Pedestrian Detection](https://mlsanity.com/paper/1803.05347/) (2018)
- [Fusion-Mamba for Cross-modality Object Detection](https://mlsanity.com/paper/2404.09146/) (2024)
- [Explicit Attention-Enhanced Fusion for RGB-Thermal Perception Tasks](https://mlsanity.com/paper/2303.15710/) (2023)
- [Learning a Graph Neural Network with Cross Modality Interaction for Image Fusion](https://mlsanity.com/paper/2308.03256/) (2023)
- [TFDet: Target-Aware Fusion for RGB-T Pedestrian Detection](https://mlsanity.com/paper/2305.16580/) (2023)
- [Removal then Selection: A Coarse-to-Fine Fusion Perspective for RGB-Infrared Object Detection](https://mlsanity.com/paper/2401.10731/) (2024)
- [Strip R-CNN: Large Strip Convolution for Remote Sensing Object Detection](https://mlsanity.com/paper/2501.03775/) (2025)
- [WaveMamba: Wavelet-Driven Mamba Fusion for RGB-Infrared Object Detection](https://mlsanity.com/paper/2507.18173/) (2025)
- [Spectral-Aware Global Fusion for RGB-Thermal Semantic Segmentation](https://mlsanity.com/paper/2505.15491/) (2025)

## Abstract (arXiv)

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.