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

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.

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Publication Details

Journal
Edelweiss Applied Science and Technology
Published
2024-09-16
DOI
https://doi.org/10.55214/25768484.v8i5.1737
Citations
4
Primary Topic
Infrared Target Detection Methodologies
Type
article
Field-Weighted Citation Impact
5.20

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article

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

Prasatporn Wongkamchang, Khongdet Phasinam, Kamphon Suewongsuwan, Natchanun Angsuseranee
4 citations
Edelweiss Applied Science and Technology
Infrared Target Detection Methodologies
5.20
article

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

Prasatporn Wongkamchang, Khongdet Phasinam, Kamphon Suewongsuwan, Natchanun Angsuseranee
article en
4 citations

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.

Edelweiss Applied Science and TechnologyVol. 8(5)
Pibulsongkram Rajabhat University (TH), Navamindradhiraj University (TH), Rajamangala University of Technology Suvarnabhumi (TH)
Rajamangala University of Technology Suvarnabhumi
Openalex Percentile: Top 5%
Infrared Target Detection Methodologies
5.20
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Comparative analysis of UAV detection and tracking performance: Evaluating YOLOv5, YOLOv8, and YOLOv8 DeepSORT for enhancing anti-UAV systems — Prasatporn Wongkamchang, Khongdet Phasinam, et al. · Edelweiss Applied Science and Technology (2024) | TGRS Research Map | TGRS