针对无人机多光谱检测的三重挑战，提出双域增强与优先级引导融合新框架。
DEPFusion: Dual-Domain Enhancement and Priority-Guided Mamba Fusion for UAV Multispectral Object Detection
- 设计双域增强模块，结合小波与傅里叶变换恢复光照与纹理细节。
- 提出优先级引导序列化，提升目标特征建模效率并抑制干扰信息。
- 轻量化结构适合无人机部署，实测在两个数据集上达顶尖性能。
多光谱目标检测在无人机应用中至关重要，但面临三大挑战：低照度RGB图像导致细节丢失，影响多光谱融合；融合过程中引入干扰信息，损害局部目标建模；基于Transformer的方法计算复杂度高（二次方），难以部署于无人机平台。为此，本文提出DEPFusion框架，包含双域增强（DDE）和优先级引导马尔可夫融合（PGMF）两个模块。DDE模块采用跨尺度小波马尔可夫（CSWM）块增强全局亮度，利用傅里叶细节恢复（FDR）块恢复纹理特征。PGMF模块通过理论证明的优先级引导序列化机制，引导马尔可夫扫描高优先级目标特征令牌，强化局部建模并减少干扰。在DroneVehicle和VEDAI数据集上的实验表明，DEPFusion在保持高效的同时达到当前最优性能。
原文摘要 · Abstract (English)
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.
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