Revolutionary Energy-Efficient Object Detection System for UAVs
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Unmanned aerial vehicles (UAVs) are being used in various fields of our daily lives- they help detect fires or other environmental hazards, monitor natural environments and cities, locate missing persons, and find survivors of natural disasters. For all of these roles, the UAVs should be able to reliably detect targets and objects of interest in their surroundings.
Recently, researchers at Yunnan University and the Chinese Academy of Sciences introduced a new object-detection system based on edge computing that could provide UAVs with the ability to spot relevant objects and targets in their surroundings without much increase in their power consumption.
In their paper, Jiashun Suo, Xingzhou Zhang, Weisong Shi, and Wei Zhou wrote: “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.”
According to Techxplore, this new system is based on edge computing, which leverages multiple networks or nearby devices to perform computations faster while consuming less energy.
Suo, Zhang and their colleagues explained: “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.”
The researchers state that the performance of the system and the experimental results demonstrate that it can significantly decrease energy consumption in real-world scenarios.
This work could help the development of similar object detection techniques based on edge computing for robotics applications, and in the future, E3-UAV could be implemented and tested on other UAVs to further assess its potential and generalizability.
