标题： UAVD-Mamba：用于多模态无人机检测的可变形标记融合视觉Mamba 显示英文标题

标题： UAVD-Mamba: Deformable Token Fusion Vision Mamba for Multimodal UAV Detection

摘要： 无人机（UAV）目标检测已广泛应用于交通管理、农业、应急救援等领域。 然而，它面临着显著的挑战，包括遮挡、小目标尺寸和不规则形状。 这些挑战突显了对一种强大且高效的多模态无人机目标检测方法的必要性。 Mamba在多模态图像融合中表现出巨大的潜力。 利用这一点，我们提出了UAVD-Mamba，一种基于Mamba架构的多模态无人机目标检测框架。 为了提高几何适应性，我们提出了可变形标记Mamba块（DTMB），通过结合来自可变形卷积的自适应补丁和来自常规卷积的常规补丁来生成可变形标记，这些标记作为Mamba块的输入。 为了优化多模态特征互补性，我们为RGB和红外（IR）模态设计了两个独立的DTMB，两个DTMB的输出被整合到Mamba块中进行特征提取，并整合到融合Mamba块中进行特征融合。 此外，为了提高多尺度目标检测，特别是小目标的检测，我们在不同尺度上堆叠四个DTMB以生成多尺度特征表示，然后将这些特征发送到检测颈部进行Mamba（DNM）。 DNM模块受YOLO系列的启发，对YOLOv11中的SPPF和C3K2进行了修改，以更好地处理多尺度特征。 特别是，在DTMB之前我们采用了跨增强空间注意力，在融合Mamba块之后采用跨通道注意力，以提取更具区分性的特征。 在DroneVehicle数据集上的实验结果表明，我们的方法在mAP指标上比基线OAFA方法提高了3.6%。 代码将在https://github.com/GreatPlum-hnu/UAVD-Mamba.git发布。 显示英文摘要

摘要： Unmanned Aerial Vehicle (UAV) object detection has been widely used in traffic management, agriculture, emergency rescue, etc. However, it faces significant challenges, including occlusions, small object sizes, and irregular shapes. These challenges highlight the necessity for a robust and efficient multimodal UAV object detection method. Mamba has demonstrated considerable potential in multimodal image fusion. Leveraging this, we propose UAVD-Mamba, a multimodal UAV object detection framework based on Mamba architectures. To improve geometric adaptability, we propose the Deformable Token Mamba Block (DTMB) to generate deformable tokens by incorporating adaptive patches from deformable convolutions alongside normal patches from normal convolutions, which serve as the inputs to the Mamba Block. To optimize the multimodal feature complementarity, we design two separate DTMBs for the RGB and infrared (IR) modalities, with the outputs from both DTMBs integrated into the Mamba Block for feature extraction and into the Fusion Mamba Block for feature fusion. Additionally, to improve multiscale object detection, especially for small objects, we stack four DTMBs at different scales to produce multiscale feature representations, which are then sent to the Detection Neck for Mamba (DNM). The DNM module, inspired by the YOLO series, includes modifications to the SPPF and C3K2 of YOLOv11 to better handle the multiscale features. In particular, we employ cross-enhanced spatial attention before the DTMB and cross-channel attention after the Fusion Mamba Block to extract more discriminative features. Experimental results on the DroneVehicle dataset show that our method outperforms the baseline OAFA method by 3.6% in the mAP metric. Codes will be released at https://github.com/GreatPlum-hnu/UAVD-Mamba.git.