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.

提出了一种基于边缘计算的无人机能效高的目标检测系统，旨在通过决策最节能的飞行参数来降低能源消耗，实验结果表明在实际情景中能够显著降低能源消耗，并提供了对无人机目标检测进一步研究的四个见解。

E3-UAV：无人机边缘能效目标检测系统

E3-UAV: An Edge-based Energy-Efficient Object Detection System for  Unmanned Aerial Vehicles

Historically, the rotorcraft community has experienced a higher fatal
accident rate than other aviation segments, including commercial and general
aviation. Recent advancements in artificial intelligence (AI) and the
application of these technologies in different areas of our lives are both
intriguing and encouraging. When developed appropriately for the aviation
domain, AI techniques provide an opportunity to help design systems that can
address rotorcraft safety challenges. Our recent work demonstrated that AI
algorithms could use video data from onboard cameras and correctly identify
different flight parameters from cockpit gauges, e.g., indicated airspeed.
These AI-based techniques provide a potentially cost-effective solution,
especially for small helicopter operators, to record the flight state
information and perform post-flight analyses. We also showed that carefully
designed and trained AI systems could accurately predict rotorcraft attitude
(i.e., pitch and yaw) from outside scenes (images or video data). Ordinary
off-the-shelf video cameras were installed inside the rotorcraft cockpit to
record the outside scene, including the horizon. The AI algorithm could
correctly identify rotorcraft attitude at an accuracy in the range of 80\%. In
this work, we combined five different onboard camera viewpoints to improve
attitude prediction accuracy to 94\%. In this paper, five onboard camera views
included the pilot windshield, co-pilot windshield, pilot Electronic Flight
Instrument System (EFIS) display, co-pilot EFIS display, and the attitude
indicator gauge. Using video data from each camera view, we trained various
convolutional neural networks (CNNs), which achieved prediction accuracy in the
range of 79\% % to 90\% %. We subsequently ensembled the learned knowledge from
all CNNs and achieved an ensembled accuracy of 93.3\%.

应用人工智能和无人机的相机技术，可以有效地提高直升机的安全性能，并通过将多种卷积神经网络进行组合，对其姿态进行准确预测。