发表机构
Tennessee Technological University（田纳西理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对高空无人机在部分可观测条件下搜索稀疏地面目标的问题，提出基于PTZ相机和POMCP的在线规划方法，通过选择性集成检测提升感知可靠性，在仿真中实现高效目标检测。
AI 中文摘要
高空无人机搜索地面目标时面临一个独特的挑战，尤其是当目标已经位于视野内但由于其视在尺度较小而实际上无法观测时。标准目标检测器在此类场景中往往表现不佳，原因是分辨率降低和上下文有限。相比之下，主动目标搜索框架通过引导智能体到达合适的位置以获取更丰富的视觉信息来应对这一挑战。然而，城市空域的飞行法规通常限制无人机进行此类物理移动。作为一种替代的主动感知方法，无人机可以利用机载相机的云台变焦（PTZ）机制动态调整其视野，并顺序地从感兴趣的区域收集增强的视觉信息。一旦识别出目标的候选位置，它可以部署更昂贵的检测方案，例如多模型集成，以在固定尺度上获得更好的推理。在这项工作中，我们将使用PTZ操作的顺序探索建模为部分可观测马尔可夫决策过程（POMDP），其中智能体维护关于目标真实位置的信念状态。为解决该POMDP，我们部署了部分可观测蒙特卡洛规划（POMCP），其中我们将感知可靠性条件化于目标物体尺度，并部署选择性集成检测作为额外的推理步骤。我们在不同环境条件和车辆状态下的逼真模拟器中验证了我们的方法，结果显示与基线方法相比，能够以显著更少的步骤和最小的传感器分辨率依赖检测不同尺度的地面目标。
英文摘要
Unmanned aerial vehicles (UAVs) searching for ground targets from a high altitude face a unique challenge, particularly when the target is already within the field of view but effectively unobservable because of its small apparent scale. Standard object detectors often underperform in such scenarios because of resolution downscaling and limited context. In contrast, active object search frameworks address this challenge by directing the agent to a suitable pose to gather richer visual information. However, flight regulations in urban airspace often restrict such physical movements for UAVs. As an alternative active sensing approach, the UAV can leverage the pan-tilt-zoom (PTZ) mechanism of the onboard camera to dynamically adjust its field of view and sequentially gather enhanced visual information from specific regions of interest. Once the candidate locations of the target are identified, it can deploy more expensive object detection schemes, such as an ensemble of multiple models, to get better reasoning at a fixed scale. In this work, we formulate the sequential exploration with PTZ operation as a partially observable Markov decision process (POMDP), in which the agent maintains a belief state over the target's true location. To solve the POMDP, we deploy partially observable Monte Carlo planning (POMCP), where we condition the sensing reliability on target object scale and deploy selective ensemble detection as an additional reasoning step. We validate our methodology with experiments in a photorealistic simulator under different environmental conditions and vehicle states, showing detection of ground targets at variable scales with significantly fewer steps and minimal dependence on sensor resolution compared to baseline methods.
