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DMM: Disparity-guided Multispectral Mamba for Oriented Object Detection in Remote Sensing

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arxiv 2407.08132 v1 pith:IHRSFCS5 submitted 2024-07-11 cs.CV

Minghang Zhou , Tianyu Li , Chaofan Qiao , Dongyu Xie , Guoqing Wang , Ningjuan Ruan , Lin Mei , Yang Yang This is my paper · ORCID
classification cs.CV
keywords moduledetectionmambamultispectraldisparity-guidedobjectorientedauxiliary
verification ladder T0 review T1 audit T2 compute T3 formal
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Multispectral oriented object detection faces challenges due to both inter-modal and intra-modal discrepancies. Recent studies often rely on transformer-based models to address these issues and achieve cross-modal fusion detection. However, the quadratic computational complexity of transformers limits their performance. Inspired by the efficiency and lower complexity of Mamba in long sequence tasks, we propose Disparity-guided Multispectral Mamba (DMM), a multispectral oriented object detection framework comprised of a Disparity-guided Cross-modal Fusion Mamba (DCFM) module, a Multi-scale Target-aware Attention (MTA) module, and a Target-Prior Aware (TPA) auxiliary task. The DCFM module leverages disparity information between modalities to adaptively merge features from RGB and IR images, mitigating inter-modal conflicts. The MTA module aims to enhance feature representation by focusing on relevant target regions within the RGB modality, addressing intra-modal variations. The TPA auxiliary task utilizes single-modal labels to guide the optimization of the MTA module, ensuring it focuses on targets and their local context. Extensive experiments on the DroneVehicle and VEDAI datasets demonstrate the effectiveness of our method, which outperforms state-of-the-art methods while maintaining computational efficiency. Code will be available at https://github.com/Another-0/DMM.

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Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. RegisterBridgeMM: A Register-Centric Framework for RGB-Infrared Object Detection

    cs.CV 2026-08 conditional novelty 6.0 of 10

    A frozen-backbone framework that uses pretrained DINOv3 register tokens as a bidirectional cross-modal bottleneck reports the highest mAP50-95 on LLVIP, M3FD, DroneVehicle, and FLIR-Aligned among the compared methods.

  2. InfraNet: Quality-Aware RGB Guidance for Efficient Infrared Object Detection

    cs.CV 2026-07 conditional novelty 6.0 of 10

    QualGate-regulated RGB guidance during training produces efficient IR-only and dual-modal detectors that match or beat equal-fusion baselines under low light and adverse weather.

  3. WaveMamba: Wavelet-Driven Mamba Fusion for RGB-Infrared Object Detection

    cs.CV 2025-07 conditional novelty 6.0 of 10

    WaveMamba fuses RGB and infrared features in the wavelet domain and reports an average mAP gain of about 4.5 points over prior methods on four public benchmarks.

  4. LDFE: Laplacian Decoupled Feature Enhancement Block for Dual-Stream CNN-based RGB-IR Object Detection

    cs.CV 2026-07 unverdicted novelty 5.0 of 10

    A Laplacian Pyramid-based feature enhancement block with state-space and convolutional modules improves RGB-IR object detection by 2–6% mAP over prior methods on six datasets.

  5. Selective Structured State Space for Multispectral-fused Small Target Detection

    cs.CV 2025-05 conditional novelty 5.0 of 10

    A Mamba-based multispectral detector with three new modules reports state-of-the-art accuracy on VEDAI at real-time speed and with 17 MB size, though it trails some methods on larger objects.

  6. LASFNet: A Lightweight Attention-Guided Self-Modulation Feature Fusion Network for Multimodal Object Detection

    cs.CV 2025-06 conditional novelty 4.0 of 10

    LASFNet fuses RGB and infrared features in one lightweight stage with attention modules, reporting similar or better detection accuracy than heavier multimodal detectors.

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