Survey of High-Speed Vehicle Tracking Technology for Unmanned Aerial Vehicles Based on Neural Networks and YOLO

Kaizhuo Liu

doi:10.47297/taposatWSP2633-456919.20260702

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Survey of High-Speed Vehicle Tracking Technology for Unmanned Aerial Vehicles Based on Neural Networks and YOLO

Volume 7, Issue 2

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Survey of High-Speed Vehicle Tracking Technology for Unmanned Aerial Vehicles Based on Neural Networks and YOLO
Survey of High-Speed Vehicle Tracking Technology for Unmanned Aerial Vehicles Based on Neural Networks and YOLO
School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou, Guangdong, 511400, China
Vol. 7, Issue 2, Pages: 97-100(2026)
Published：28 February 2026
DOI：10.47297/taposatWSP2633-456919.20260702
Accepted：

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Liu Kaizhuo.Survey of High-Speed Vehicle Tracking Technology for Unmanned Aerial Vehicles Based on Neural Networks and YOLO[J].Theory and Practice of Science and Technology,2026,07(02):97-100.
DOI：

Liu Kaizhuo.Survey of High-Speed Vehicle Tracking Technology for Unmanned Aerial Vehicles Based on Neural Networks and YOLO[J].Theory and Practice of Science and Technology,2026,07(02):97-100. DOI： 10.47297/taposatWSP2633-456919.20260702.

摘要

Abstract

Due to the rapid development of unmanned aerial vehicle (UAV) technology and the increasing maturity of artificial intelligence algorithms

tracking of high-speed moving targets by UAVs has become a research hotspot in computer vision

pattern recognition

and intelligent control. Therefore

this paper provides a well-structured and logical systematic survey of UAV high-speed vehicle tracking technology based on neural networks and YOLO models: first

the background and significance of the discussed problem are presented; then

the latest methods of four core technical modules

namely target detection

state estimation

trajectory planning

and tracking control

are naturally introduced; subsequently

the application principles and implementation methods of key technologies such as YOLO target detection algorithm

adaptive neural network control

disturbance observer

finite-time control

and barrier Lyapunov functions in UAV tracking systems are analyzed in detail. Finally

existing shortcomings are objectively summarized

and future directions are reasonably proposed.

关键词

Keywords

references

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