Real-Time Object Detection Based on UAV Remote Sensing: A Systematic Literature Review

IF 4.4 2区 地球科学 Q1 REMOTE SENSING
Drones Pub Date : 2023-10-03 DOI:10.3390/drones7100620
Zhen Cao, Lammert Kooistra, Wensheng Wang, Leifeng Guo, João Valente
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Abstract

Real-time object detection based on UAV remote sensing is widely required in different scenarios. In the past 20 years, with the development of unmanned aerial vehicles (UAV), remote sensing technology, deep learning technology, and edge computing technology, research on UAV real-time object detection in different fields has become increasingly important. However, since real-time UAV object detection is a comprehensive task involving hardware, algorithms, and other components, the complete implementation of real-time object detection is often overlooked. Although there is a large amount of literature on real-time object detection based on UAV remote sensing, little attention has been given to its workflow. This paper aims to systematically review previous studies about UAV real-time object detection from application scenarios, hardware selection, real-time detection paradigms, detection algorithms and their optimization technologies, and evaluation metrics. Through visual and narrative analyses, the conclusions cover all proposed research questions. Real-time object detection is more in demand in scenarios such as emergency rescue and precision agriculture. Multi-rotor UAVs and RGB images are of more interest in applications, and real-time detection mainly uses edge computing with documented processing strategies. GPU-based edge computing platforms are widely used, and deep learning algorithms is preferred for real-time detection. Meanwhile, optimization algorithms need to be focused on resource-limited computing platform deployment, such as lightweight convolutional layers, etc. In addition to accuracy, speed, latency, and energy are equally important evaluation metrics. Finally, this paper thoroughly discusses the challenges of sensor-, edge computing-, and algorithm-related lightweight technologies in real-time object detection. It also discusses the prospective impact of future developments in autonomous UAVs and communications on UAV real-time target detection.
基于无人机遥感的实时目标检测:系统文献综述
基于无人机遥感的实时目标检测在不同的场景中有着广泛的需求。近20年来,随着无人机(UAV)、遥感技术、深度学习技术、边缘计算技术的发展,无人机在不同领域的实时目标检测研究变得越来越重要。然而,由于实时无人机目标检测是一项涉及硬件、算法和其他组件的综合任务,因此实时目标检测的完整实现往往被忽视。虽然关于基于无人机遥感的实时目标检测的文献很多,但对其工作流程的研究却很少。本文从应用场景、硬件选择、实时检测范式、检测算法及其优化技术、评价指标等方面系统综述了无人机实时目标检测的研究现状。通过视觉和叙事分析,结论涵盖了所有提出的研究问题。实时目标检测在紧急救援和精准农业等场景中需求更大。多旋翼无人机和RGB图像在应用中更受关注,实时检测主要使用边缘计算和记录的处理策略。基于gpu的边缘计算平台被广泛使用,深度学习算法是实时检测的首选。同时,优化算法需要关注资源有限的计算平台部署,如轻量级卷积层等。除了准确性之外,速度、延迟和能量也是同样重要的评估指标。最后,本文深入讨论了传感器、边缘计算和算法相关的轻量化技术在实时目标检测中的挑战。它还讨论了自主无人机和通信对无人机实时目标探测的未来发展的预期影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Drones
Drones Engineering-Aerospace Engineering
CiteScore
5.60
自引率
18.80%
发文量
331
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