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

arXiv: https://arxiv.org/abs/2410.00285 · HTML: https://mlsanity.com/paper/2410.00285/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 83 references that are in the map.
- First posted on arXiv 30 Sep 2024.

## Builds on (most cited first, 26 of 26)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [ByteTrack: Multi-Object Tracking by Associating Every Detection Box](https://mlsanity.com/paper/2110.06864/) (2021)
- [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://mlsanity.com/paper/1406.4729/) (2014)
- [Fully-Convolutional Siamese Networks for Object Tracking](https://mlsanity.com/paper/1606.09549/) (2016)
- [LaSOT: A High-quality Benchmark for Large-scale Single Object Tracking](https://mlsanity.com/paper/1809.07845/) (2018)
- [Transformer Tracking](https://mlsanity.com/paper/2103.15436/) (2021)
- [CSPNet: A New Backbone that can Enhance Learning Capability of CNN](https://mlsanity.com/paper/1911.11929/) (2019)
- [Learning Discriminative Model Prediction for Tracking](https://mlsanity.com/paper/1904.07220/) (2019)
- [BoT-SORT: Robust Associations Multi-Pedestrian Tracking](https://mlsanity.com/paper/2206.14651/) (2022)
- [Drone-based Object Counting by Spatially Regularized Regional Proposal Network](https://mlsanity.com/paper/1707.05972/) (2017)
- [RGB-T Object Tracking:Benchmark and Baseline](https://mlsanity.com/paper/1805.08982/) (2018)
- [Global Tracking Transformers](https://mlsanity.com/paper/2203.13250/) (2022)
- [DepthTrack : Unveiling the Power of RGBD Tracking](https://mlsanity.com/paper/2108.13962/) (2021)
- [Learning Spatially Regularized Correlation Filters for Visual Tracking](https://mlsanity.com/paper/1608.05571/) (2016)
- [Learning Background-Aware Correlation Filters for Visual Tracking](https://mlsanity.com/paper/1703.04590/) (2017)
- [Multi-Modal Fusion for End-to-End RGB-T Tracking](https://mlsanity.com/paper/1908.11714/) (2019)
- [Vision-based Anti-UAV Detection and Tracking](https://mlsanity.com/paper/2205.10851/) (2022)
- [PTB-TIR: A Thermal Infrared Pedestrian Tracking Benchmark](https://mlsanity.com/paper/1801.05944/) (2018)
- [Object Tracking by Reconstruction with View-Specific Discriminative Correlation Filters](https://mlsanity.com/paper/1811.10863/) (2018)
- [LSOTB-TIR:A Large-Scale High-Diversity Thermal Infrared Object Tracking Benchmark](https://mlsanity.com/paper/2008.00836/) (2020)
- [Learning Deep Multi-Level Similarity for Thermal Infrared Object Tracking](https://mlsanity.com/paper/1906.03568/) (2019)
- [Siamese Transformer Pyramid Networks for Real-Time UAV Tracking](https://mlsanity.com/paper/2110.08822/) (2021)
- [Large Margin Structured Convolution Operator for Thermal Infrared Object Tracking](https://mlsanity.com/paper/1804.07006/) (2018)
- [Hierarchical Deep CNN Feature Set-Based Representation Learning for Robust Cross-Resolution Face Recognition](https://mlsanity.com/paper/2103.13851/) (2021)

## Abstract (arXiv)

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