A vision-based drone-to-drone detection system is crucial for various
applications like collision avoidance, countering hostile drones, and
search-and-rescue operations. However, detecting drones presents unique
challenges, including small object sizes, distortion, occlusion, and real-time
processing requirements. Current methods integrating multi-scale feature fusion
and temporal information have limitations in handling extreme blur and
minuscule objects. To address this, we propose a novel coarse-to-fine detection
strategy based on vision transformers. We evaluate our approach on three
challenging drone-to-drone detection datasets, achieving F1 score enhancements
of 7%, 3%, and 1% on the FL-Drones, AOT, and NPS-Drones datasets, respectively.
Additionally, we demonstrate real-time processing capabilities by deploying our
model on an edge-computing device. Our code will be made publicly available.

基于视觉转换器的粗细检测策略用于无人机检测，提升 F1 得分，并在边缘计算设备上实现实时处理能力。

C2FDrone: 基于视觉 Transformer 网络的粗 - 精无人机间检测

C2FDrone: Coarse-to-Fine Drone-to-Drone Detection using Vision  Transformer Networks

Climate-induced disasters are and will continue to be on the rise, and thus
search-and-rescue (SAR) operations, where the task is to localize and assist
one or several people who are missing, become increasingly relevant. In many
cases the rough location may be known and a UAV can be deployed to explore a
given, confined area to precisely localize the missing people. Due to time and
battery constraints it is often critical that localization is performed as
efficiently as possible. In this work we approach this type of problem by
abstracting it as an aerial view goal localization task in a framework that
emulates a SAR-like setup without requiring access to actual UAVs. In this
framework, an agent operates on top of an aerial image (proxy for a search
area) and is tasked with localizing a goal that is described in terms of visual
cues. To further mimic the situation on an actual UAV, the agent is not able to
observe the search area in its entirety, not even at low resolution, and thus
it has to operate solely based on partial glimpses when navigating towards the
goal. To tackle this task, we propose AiRLoc, a reinforcement learning
(RL)-based model that decouples exploration (searching for distant goals) and
exploitation (localizing nearby goals). Extensive evaluations show that AiRLoc
outperforms heuristic search methods as well as alternative learnable
approaches, and that it generalizes across datasets, e.g. to disaster-hit areas
without seeing a single disaster scenario during training. We also conduct a
proof-of-concept study which indicates that the learnable methods outperform
humans on average. Code and models have been made publicly available at
this https URL

本文提出了一种基于强化学习的模型，名叫 AiRLoc，用于解决搜索和救援行动中的目标定位问题，该模型可以在局部图像信息下自主完成目标搜索和定位，并且在灾难等环境下也表现出良好的泛化能力。