Motivated by the advances in deep learning techniques, the application of Unmanned Aerial Vehicle (UAV)-based object detection has proliferated across a range of fields, including vehicle counting, fire detection, and city monitoring. While most existing research studies only a subset of the challenges inherent to UAV-based object detection, there are few studies that balance various aspects to design a practical system for energy consumption reduction. In response, we present the E3-UAV, an edge-based energy-efficient object detection system for UAVs. The system is designed to dynamically support various UAV devices, edge devices, and detection algorithms, with the aim of minimizing energy consumption by deciding the most energy-efficient flight parameters (including flight altitude, flight speed, detection algorithm, and sampling rate) required to fulfill the detection requirements of the task. We first present an effective evaluation metric for actual tasks and construct a transparent energy consumption model based on hundreds of actual flight data to formalize the relationship between energy consumption and flight parameters. Then we present a lightweight energy-efficient priority decision algorithm based on a large quantity of actual flight data to assist the system in deciding flight parameters. Finally, we evaluate the performance of the system, and our experimental results demonstrate that it can significantly decrease energy consumption in real-world scenarios. Additionally, we provide four insights that can assist researchers and engineers in their efforts to study UAV-based object detection further.

翻译：受深度学习技术进步的推动，基于无人机的目标检测应用已在车辆计数、火灾检测和城市监控等领域广泛普及。尽管现有研究大多仅聚焦于无人机目标检测的部分挑战，但很少有研究能平衡多方面因素来设计实用的能耗降低系统。为此，我们提出E3-UAV——一种基于边缘的无人机节能目标检测系统。该系统旨在动态支持多种无人机设备、边缘设备和检测算法，通过决策满足任务检测需求的最节能飞行参数（包括飞行高度、飞行速度、检测算法和采样率）来最小化能耗。我们首先针对实际任务提出有效的评估指标，并基于数百次实际飞行数据构建透明能耗模型，以形式化能耗与飞行参数之间的关系。随后，我们基于大量实际飞行数据提出轻量级节能优先级决策算法，辅助系统确定飞行参数。最后，我们评估系统性能，实验结果表明该系统能在真实场景中显著降低能耗。此外，我们提供四项见解，可帮助研究者和工程师进一步探索基于无人机的目标检测研究。