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

Kamphon Suewongsuwan (), Natchanun Angsuseranee (), Prasatporn Wongkamchang () and Khongdet Phasinam ()

Edelweiss Applied Science and Technology, 2024, vol. 8, issue 5, 708-726

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: Convolutional neural network; Deep learning; Object detection; Object tracking; Real-time Tracking with a deep association metric; Simple online Unmanned aerial vehicle. (search for similar items in EconPapers)
Date: 2024
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