Accurate and efficient vehicle detection from unmanned aerial vehicle (UAV) imagery is essential for intelligent transportation, urban monitoring, and public safety, yet this task remains challenging due to high target density, extreme scale variation, complex backgrounds, and stringent onboard computational constraints. Existing DETR-based detectors model global context through self-attention but incur quadratic $\mathcal{O}\left(N^2\right)$ complexity that is prohibitive for high-resolution UAV images, while CNN-based methods lack the long-range contextual awareness needed for dense small-object scenarios. We propose RSMamDet, an efficient end-to-end detection framework built upon RT-DETR that replaces quadratic self-attention with linear $\mathcal{O}\left(N\right)$ State Space Model scanning. The framework integrates a MobileMamba backbone with a Selective Feature Scanning module for efficient global context modeling, a Dimension-Aware Selective Integration module for adaptive cross-scale feature fusion, a Poly Kernel Inception Network encoder for multi-receptive-field feature enrichment, and an Adaptive Multi-Level Feature Fusion module for content-aware dynamic upsampling, complemented by an Uncertainty-Minimal Composite loss for stable query selection in cluttered aerial scenes. Experiments on DroneVehicle and VisDrone2019 demonstrate that RSMamDet achieves mAP₅₀ of 72.6% and 40.2%, surpassing state-of-the-art methods by 4.1% and 2.2%, respectively, while maintaining real-time inference at 186.2 FPS with only 19.8M parameters and 42.3 GFLOPs, representing a $6.14\times$ reduction in computational cost and a $3.86\times$ reduction in model parameters compared to the strongest baseline.

Multimodal object detection leveraging RGB and infrared imagery has become essential for robust all-weather perception in unmanned aerial vehicle (UAV) applications. However, existing methods still struggle with effective cross-modal feature fusion, spatial misalignment between modalities, and scale variation of objects in aerial views. In this paper, we propose CMGFDet, a Cross-Modal Gated Fusion Network with Multi-Receptive Field Aggregation designed for RGB–infrared aerial object detection. Our framework introduces three coordinated modules: (1) a Cross-Modal Feature Fusion Network (CMFFN) that employs a gated attention mechanism to selectively aggregate complementary information from both modalities during encoding; (2) a Global–Local Attention Module (GLAM) that performs hierarchical cross-modal feature alignment by jointly modelling global channel statistics and local spatial correlations in the decoder; and (3) a Multi-Receptive Field Aggregation Network (MRFAN) that captures multi-scale contextual information through parallel depthwise convolutions with diverse kernel sizes. Additionally, we incorporate a deep supervision strategy and a composite loss function to enhance training efficiency. Extensive experiments on four public benchmarks (DroneVehicle, RGBTDronePerson, VEDAI, and VTUAV) show that CMGFDet improves the previous best mAP@0.5 by 1.6%, 2.2%, 1.9%, and 2.2%, respectively. The implementation code will be released upon acceptance to support reproducibility.