JOURNAL ARTICLE

Comparative analysis of UAV detection and tracking performance: Evaluating YOLOv5, YOLOv8, and YOLOv8 DeepSORT for enhancing anti-UAV systems

Abstract

This article presents a comprehensive comparative analysis of the performance of three prominent object detection and tracking models, namely YOLOv5, YOLOv8, and YOLOv8 DeepSORT, in the domain of UAV detection and tracking. The study aims to assess the effectiveness of these models in enhancing anti-UAV systems. A series of experiments were conducted using diverse datasets and evaluation metrics to evaluate the detection and tracking capabilities of each model. The results provide valuable insights into the strengths and limitations of YOLOv5, YOLOv8, and YOLOv8 DeepSORT, shedding light on their potential applications in anti-UAV systems. The findings of this study contribute to the advancement of UAV detection and tracking technologies and serve as a guide for researchers and practitioners in the field of anti-UAV systems.

Keywords:
Computer science Tracking (education) Artificial intelligence Computer vision Real-time computing Psychology

Metrics

3
Cited By
3.96
FWCI (Field Weighted Citation Impact)
0
Refs
0.91
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Infrared Target Detection Methodologies
Physical Sciences →  Engineering →  Aerospace Engineering
Advanced Neural Network Applications
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
UAV Applications and Optimization
Physical Sciences →  Engineering →  Aerospace Engineering
© 2026 ScienceGate Book Chapters — All rights reserved.