Enhancing UAV Search under Occlusion using Next Best View Planning

A new dataset named MM-UAV has been introduced, designed for tracking unmanned aerial vehicles (UAVs) using a multi-modal approach that includes RGB, infrared, and event signals. This dataset features over 30 challenging scenarios with 1,321 synchronized sequences and more than 2.8 million annotated frames, addressing the limitations of single-modality tracking in difficult conditions.

MambaRefine-YOLO has been introduced as a dual-modality small object detector specifically designed for Unmanned Aerial Vehicle (UAV) imagery, addressing the challenges of low resolution and background clutter in small object detection. The model incorporates a Dual-Gated Complementary Mamba fusion module (DGC-MFM) and a Hierarchical Feature Aggregation Neck (HFAN), achieving a state-of-the-art mean Average Precision (mAP) of 83.2% on the DroneVehicle dataset.

A new framework for 3D dynamic radio map prediction using Vision Transformers has been proposed to enhance connectivity in low-altitude wireless networks, particularly with the increasing use of unmanned aerial vehicles (UAVs). This framework addresses the challenges posed by fluctuating user density and power budgets in a three-dimensional environment, allowing for real-time adaptation to changing conditions.

A new framework for autonomous UAV navigation has been proposed, focusing on meta-policy switching to enhance resilience against adversarial attacks that manipulate sensor inputs. This approach utilizes a discounted Thompson sampling mechanism to dynamically select robust policies, addressing the limitations of traditional reinforcement learning methods in adversarial airspace.