  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.
