Vega: Drone-based Multi-Altitude Target Detection for Autonomous Surveillance
Vega: Drone-based Multi-Altitude Target Detection for Autonomous Surveillance
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DOI：
10.1109/dcoss-iot58021.2023.00044
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发表时间：
2023-06
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影响因子：
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通讯作者：
Akhil Bandarupalli;Sarthak Jain;Akash Melachuri;Joseph Pappas;S. Chaterji
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作者：
Akhil Bandarupalli;Sarthak Jain;Akash Melachuri;Joseph Pappas;S. Chaterji
无人机(UAV)或无人机是基于视频的监控的很有前途的工具。空中监视的各种应用使用目标检测程序来检测目标对象。在这种应用中，有三个参数影响无人机部署策略：无人机覆盖的区域、目标(对象)检测的延迟以及对象检测器的检测输出的质量。以前的工作集中在沿着区域延迟前沿或区域质量前沿改进帕累托最优，而不是在联合区域延迟质量前沿，因此这些解决方案对于基于无人机的监视来说是次优的。在使用带有摄像头和目标检测程序的无人机对区域内的目标进行自主空中监视的背景下，我们探索了面积、延迟和质量之间的三方面权衡。我们提出了Vega，这是一个无人机部署框架，它捕捉到了这些权衡，以高效地部署无人机。我们与织女星一起做出了三项贡献。首先，我们描述了最先进的移动对象探测器EfficientDet[CPVR‘20]的能力，该探测器能够使用置信度和IOU曲线与无人机高度进行比较，从不同的无人机高度检测对象。其次，基于探测器的这些特点，我们提出了一套用于无人机机动的两种算法原语，即DroneZoom和DroneCycle。使用这两个基元，我们获得了单个无人机系统的三个目标参数-覆盖面积、检测延迟和检测质量-之间更优的帕累托边界。第三，我们将我们的发现扩展到使用高阶Voronoi镶嵌的群部署，其中我们使用Voronoi顺序控制群的空间密度，以进一步降低检测延迟，同时保持检测质量。
UAVs (unmanned aerial vehicles) or drones are promising instruments for video-based surveillance. Various applications of aerial surveillance use object detection programs to detect target objects. In such applications, three parameters influence a drone deployment strategy: the area covered by the drone, the latency of target (object) detection, and the quality of the detection output by the object detector. Previous works have focused on improving Pareto optimality along the area-latency frontier or the area-quality frontier, but not on the combined area-latency-quality frontier, because of which these solutions are sub-optimal for drone-based surveillance. We explore a three way tradeoff between area, latency, and quality in the context of autonomous aerial surveillance of targets in an area using drones with cameras and an object detection program. We propose Vega, a drone deployment framework that captures these tradeoffs to deploy drones efficiently. We make three contributions with Vega. First, we characterize the ability of the state-of-the-art mobile object detector, EfficientDet [CPVR '20], to detect objects from varying drone altitudes using confidence and IoU curves vs. drone altitude. Second, based on these characteristics of the detector, we propose a set of two algorithmic primitives for drone-based maneuvers, namely DroneZoom and DroneCycle. Using these two primitives, we obtain a more optimal Pareto frontier between our three target parameters - coverage area, detection latency, and detection quality for a single drone system. Third, we scale out our findings to a swarm deployment using higher-order Voronoi tessellations, where we control the swarm's spatial density using the Voronoi order to further lower the detection latency while maintaining detection quality.