Research on Multi Machine Cooperative Autonomous Inspection Strategy for UHV Dense Transmission Channel Based on 5G Technology

Li, Long; Cheng, Liang; Li, Xiang; Yue, Lingping; Zhang, Hongbin

doi:10.1155/2022/8524817

Cited by 2 publications

(1 citation statement)

References 26 publications

(30 reference statements)

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“…Humans need to use real-time detection results to reduce losses and improve efficiency. To achieve this, some technologies, including multi-machine collaborative work [143,144], knowledge graphs [145], natural language processing [146], and blockchain [147], are needed.…”

Section: Autonomous Uav Real-time Object Detectionmentioning

confidence: 99%

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

Cao,

Kooistra,

Wang

et al. 2023

Drones

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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.

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