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

• URL: http://arxiv.org/abs/2509.07327v2
• Date: Mon, 29 Sep 2025 08:14:38 GMT
• Title: DEPFusion: Dual-Domain Enhancement and Priority-Guided Mamba Fusion for UAV Multispectral Object Detection
• Authors: Shucong Li, Zhenyu Liu, Zijie Hong, Zhiheng Zhou, Xianghai Cao,
• Abstract summary: A framework named DEPFusion is proposed for UAV multispectral object detection.<n>It consists of Dual-Domain Enhancement (DDE) and Priority-Guided Mamba Fusion (PGMF)<n>Experiments on DroneVehicle and VEDAI datasets demonstrate that DEPFusion achieves good performance with state-of-the-art methods.
• Score: 6.4402018224356015
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Pyramidal Adaptive Cross-Gating for Multimodal Detection [0.0]
  PACGNet is an architecture designed to perform deep fusion within the backbone.<n>The P module reconstructs the feature hierarchy via a progressive hierarchical gating mechanism.<n>Our PACGNet sets a new state-of-the-art benchmark, with mAP50 scores reaching 81.7% and 82.1% respectively.
  arXiv  Detail & Related papers  (2025-12-20T09:32:18Z)
• MODA: The First Challenging Benchmark for Multispectral Object Detection in Aerial Images [26.48439423478357]
  We introduce the first large-scale dataset for Multispectral Object Detection in Aerial images (MODA)<n>This dataset comprises 14,041 MSIs and 330,191 annotations across diverse, challenging scenarios.<n>We also propose OSSDet, a framework that integrates spectral and spatial information with object-aware cues.
  arXiv  Detail & Related papers  (2025-12-10T10:07:06Z)
• UAVD-Mamba: Deformable Token Fusion Vision Mamba for Multimodal UAV Detection [5.89878250955707]
  UAVD-Mamba is a multimodal UAV object detection framework based on Mamba architectures.<n>To improve geometric adaptability, we propose the Deformable Token Mamba Block (DTMB)<n>To optimize the multimodal feature complementarity, we design two separate DTMBs for the RGB and infrared (IR) modalities.
  arXiv  Detail & Related papers  (2025-07-01T15:21:27Z)
• AuxDet: Auxiliary Metadata Matters for Omni-Domain Infrared Small Target Detection [49.81255045696323]
  We present the Auxiliary Metadata Driven Infrared Small Target Detector (AuxDet)<n>AuxDet integrates metadata semantics with visual features, guiding adaptive representation learning for each sample.<n>Experiments on the challenging WideIRSTD-Full benchmark demonstrate that AuxDet consistently outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-05-21T07:02:05Z)
• Lightweight RGB-D Salient Object Detection from a Speed-Accuracy Tradeoff Perspective [54.91271106816616]
  Current RGB-D methods usually leverage large-scale backbones to improve accuracy but sacrifice efficiency.<n>We propose a Speed-Accuracy Tradeoff Network (SATNet) for Lightweight RGB-D SOD from three fundamental perspectives.<n> Concerning depth quality, we introduce the Depth Anything Model to generate high-quality depth maps.<n>For modality fusion, we propose a Decoupled Attention Module (DAM) to explore the consistency within and between modalities.<n>For feature representation, we develop a Dual Information Representation Module (DIRM) with a bi-directional inverted framework.
  arXiv  Detail & Related papers  (2025-05-07T19:37:20Z)
• FUSE: Label-Free Image-Event Joint Monocular Depth Estimation via Frequency-Decoupled Alignment and Degradation-Robust Fusion [63.87313550399871]
  Image-event joint depth estimation methods leverage complementary modalities for robust perception, yet face challenges in generalizability.<n>We propose Self-supervised Transfer (PST) and FrequencyDe-coupled Fusion module (FreDF)<n>PST establishes cross-modal knowledge transfer through latent space alignment with image foundation models.<n>FreDF explicitly decouples high-frequency edge features from low-frequency structural components, resolving modality-specific frequency mismatches.
  arXiv  Detail & Related papers  (2025-03-25T15:04:53Z)
• Multimodal Transformer Using Cross-Channel attention for Object Detection in Remote Sensing Images [1.662438436885552]
  Multi-modal fusion has been determined to enhance the accuracy by fusing data from multiple modalities. We propose a novel multi-modal fusion strategy for mapping relationships between different channels at the early stage. By addressing fusion in the early stage, as opposed to mid or late-stage methods, our method achieves competitive and even superior performance compared to existing techniques.
  arXiv  Detail & Related papers  (2023-10-21T00:56:11Z)
• Multimodal Industrial Anomaly Detection via Hybrid Fusion [59.16333340582885]
  We propose a novel multimodal anomaly detection method with hybrid fusion scheme. Our model outperforms the state-of-the-art (SOTA) methods on both detection and segmentation precision on MVTecD-3 AD dataset.
  arXiv  Detail & Related papers  (2023-03-01T15:48:27Z)
• CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image Fusion [138.40422469153145]
  We propose a novel Correlation-Driven feature Decomposition Fusion (CDDFuse) network. We show that CDDFuse achieves promising results in multiple fusion tasks, including infrared-visible image fusion and medical image fusion.
  arXiv  Detail & Related papers  (2022-11-26T02:40:28Z)
• Bridging the View Disparity of Radar and Camera Features for Multi-modal Fusion 3D Object Detection [6.959556180268547]
  This paper focuses on how to utilize millimeter-wave (MMW) radar and camera sensor fusion for 3D object detection. A novel method which realizes the feature-level fusion under bird-eye view (BEV) for a better feature representation is proposed.
  arXiv  Detail & Related papers  (2022-08-25T13:21:37Z)
• Target-aware Dual Adversarial Learning and a Multi-scenario Multi-Modality Benchmark to Fuse Infrared and Visible for Object Detection [65.30079184700755]
  This study addresses the issue of fusing infrared and visible images that appear differently for object detection. Previous approaches discover commons underlying the two modalities and fuse upon the common space either by iterative optimization or deep networks. This paper proposes a bilevel optimization formulation for the joint problem of fusion and detection, and then unrolls to a target-aware Dual Adversarial Learning (TarDAL) network for fusion and a commonly used detection network.
  arXiv  Detail & Related papers  (2022-03-30T11:44:56Z)
• TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers [49.689566246504356]
  We propose TransFusion, a robust solution to LiDAR-camera fusion with a soft-association mechanism to handle inferior image conditions. TransFusion achieves state-of-the-art performance on large-scale datasets. We extend the proposed method to the 3D tracking task and achieve the 1st place in the leaderboard of nuScenes tracking.
  arXiv  Detail & Related papers  (2022-03-22T07:15:13Z)
• MBDF-Net: Multi-Branch Deep Fusion Network for 3D Object Detection [17.295359521427073]
  We propose a Multi-Branch Deep Fusion Network (MBDF-Net) for 3D object detection. In the first stage, our multi-branch feature extraction network utilizes Adaptive Attention Fusion modules to produce cross-modal fusion features from single-modal semantic features. In the second stage, we use a region of interest (RoI) -pooled fusion module to generate enhanced local features for refinement.
  arXiv  Detail & Related papers  (2021-08-29T15:40:15Z)
• EPMF: Efficient Perception-aware Multi-sensor Fusion for 3D Semantic Segmentation [62.210091681352914]
  We study multi-sensor fusion for 3D semantic segmentation for many applications, such as autonomous driving and robotics. In this work, we investigate a collaborative fusion scheme called perception-aware multi-sensor fusion (PMF) We propose a two-stream network to extract features from the two modalities separately. The extracted features are fused by effective residual-based fusion modules.
  arXiv  Detail & Related papers  (2021-06-21T10:47:26Z)
• Middle-level Fusion for Lightweight RGB-D Salient Object Detection [81.43951906434175]
  A novel lightweight RGB-D SOD model is presented in this paper. With IMFF and L modules incorporated in the middle-level fusion structure, our proposed model has only 3.9M parameters and runs at 33 FPS. The experimental results on several benchmark datasets verify the effectiveness and superiority of the proposed method over some state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-04-23T11:37:15Z)
• Multi-View Adaptive Fusion Network for 3D Object Detection [14.506796247331584]
  3D object detection based on LiDAR-camera fusion is becoming an emerging research theme for autonomous driving. We propose a single-stage multi-view fusion framework that takes LiDAR bird's-eye view, LiDAR range view and camera view images as inputs for 3D object detection. We design an end-to-end learnable network named MVAF-Net to integrate these two components.
  arXiv  Detail & Related papers  (2020-11-02T00:06:01Z)
• A Single Stream Network for Robust and Real-time RGB-D Salient Object Detection [89.88222217065858]
  We design a single stream network to use the depth map to guide early fusion and middle fusion between RGB and depth. This model is 55.5% lighter than the current lightest model and runs at a real-time speed of 32 FPS when processing a $384 times 384$ image.
  arXiv  Detail & Related papers  (2020-07-14T04:40:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.