# DEPFusion: Dual-Domain Enhancement and Priority-Guided Mamba Fusion for UAV Multispectral Object Detection

Source: [DEPFusion: Dual-Domain Enhancement and Priority-Guided Mamba Fusion for UAV Multispectral Object Detection](<https://www.sota2.com/research/paper/68fa7ba41e209131cea8992a>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T05:45:26.405Z

Authors: Shucong Li, Zhenyu Liu, Zijie Hong, Zhiheng Zhou, Xianghai Cao

Published: 2025-09-09T01:51:57.000Z

## Abstract

Multispectral object detection is an important application for unmanned aerial vehicles (UAVs). However, it faces several challenges. First, low-light RGB images weaken the multispectral fusion due to details loss. Second, the interference information is introduced to local target modeling during multispectral fusion. Third, computational cost poses deployment challenge on UAV platforms, such as transformer-based methods with quadratic complexity. To address these issues, a framework named DEPFusion consisting of two designed modules, Dual-Domain Enhancement (DDE) and Priority-Guided Mamba Fusion (PGMF) , is proposed for UAV multispectral object detection. Firstly, considering the adoption of low-frequency component for global brightness enhancement and frequency spectra features for texture-details recovery, DDE module is designed with Cross-Scale Wavelet Mamba (CSWM) block and Fourier Details Recovery (FDR) block. Secondly, considering guiding the scanning of Mamba from high priority score tokens, which contain local target feature, a novel Priority-Guided Serialization is proposed with theoretical proof. Based on it, PGMF module is designed for multispectral feature fusion, which enhance local modeling and reduce interference information. Experiments on DroneVehicle and VEDAI datasets demonstrate that DEPFusion achieves good performance with state-of-the-art methods.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2509.07327>)

[PDF](<https://arxiv.org/pdf/2509.07327.pdf>)