对比YOLOv5/v8与BoT-SORT/ByteTrack在无人机检测跟踪中的表现。
Performance Evaluation of Deep Learning-based Quadrotor UAV Detection and Tracking Methods
- 采用YOLOv5/v8检测，结合BoT-SORT与ByteTrack跟踪
- YOLOv8更擅长识别模糊目标，BoT-SORT精度与稳定性更高
- 适合关注无人机安防与智能监控的开发者与研究人员
无人机在多个领域应用日益广泛，但对隐私与安全带来挑战。本文评估了当前先进的深度学习方法在四轴无人机检测与跟踪中的表现。选用YOLOv5与YOLOv8系列模型进行检测，集成BoT-SORT与Byte Track跟踪系统，以应对复杂场景。在DUT数据集上的测试显示，尽管YOLOv5在检测准确率上普遍优于YOLOv8，但后者在识别低可见度目标方面更具优势，表现出更强的适应性与先进能力。此外，BoT-SORT在多数情况下实现更高交并比（IoU）与更低中心误差，表明其跟踪更为精确稳定。代码与演示视频已公开。
原文摘要 · Abstract (English)
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
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