Performance Evaluation of Deep Learning-based Quadrotor UAV Detection and Tracking Methods

TL;DR

This paper evaluates deep learning methods for quadrotor UAV detection and tracking, comparing YOLOv5 and YOLOv8 models with BoT-SORT and Byte Track trackers, highlighting their relative strengths in accuracy and robustness.

Contribution

It provides a comprehensive performance comparison of state-of-the-art deep learning models and tracking algorithms for UAV detection and tracking tasks.

Findings

YOLOv5 outperforms YOLOv8 in detection accuracy

YOLOv8 better recognizes less distinct objects

BoT-SORT achieves more accurate and stable tracking

Abstract

Unmanned Aerial Vehicles (UAVs) are becoming more popular in various sectors, offering many benefits, yet introducing significant challenges to privacy and safety. This paper investigates state-of-the-art solutions for detecting and tracking quadrotor UAVs to address these concerns. Cutting-edge deep learning models, specifically the YOLOv5 and YOLOv8 series, are evaluated for their performance in identifying UAVs accurately and quickly. Additionally, robust tracking systems, BoT-SORT and Byte Track, are integrated to ensure reliable monitoring even under challenging conditions. Our tests on the DUT dataset reveal that while YOLOv5 models generally outperform YOLOv8 in detection accuracy, the YOLOv8 models excel in recognizing less distinct objects, demonstrating their adaptability and advanced capabilities. Furthermore, BoT-SORT demonstrated superior performance over Byte Track, achieving higher IoU and lower center error in most cases, indicating more accurate and stable tracking. Code: https://github.com/zmanaa/UAV_detection_and_tracking Tracking demo: https://drive.google.com/file/d/1pe6HC5kQrgTbA2QrjvMN-yjaZyWeAvDT/view?usp=sharing