# Drone Detection and Tracking in Real-Time by Fusion of Different Sensing Modalities

arXiv: https://arxiv.org/abs/2207.01927 · HTML: https://mlsanity.com/paper/2207.01927/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 65 references that are in the map.
- First posted on arXiv 5 Jul 2022.

## Builds on (most cited first, 6 of 6)

- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [Real-Time Drone Detection and Tracking With Visible, Thermal and Acoustic Sensors](https://mlsanity.com/paper/2007.07396/) (2020)
- [A dataset for multi-sensor drone detection](https://mlsanity.com/paper/2111.01888/) (2021)
- [Using Deep Networks for Drone Detection](https://mlsanity.com/paper/1706.05726/) (2017)
- [Towards Visible and Thermal Drone Monitoring with Convolutional Neural Networks](https://mlsanity.com/paper/1812.08333/) (2018)
- [Empirical Study of Drone Sound Detection in Real-Life Environment with Deep Neural Networks](https://mlsanity.com/paper/1701.05779/) (2017)

## Built on by (most cited first, 8 of 8)

- [MMAUD: A Comprehensive Multi-Modal Anti-UAV Dataset for Modern Miniature Drone Threats](https://mlsanity.com/paper/2402.03706/) (2024)
- [YOLO-FEDER FusionNet: A Novel Deep Learning Architecture for Drone Detection](https://mlsanity.com/paper/2406.11641/) (2024)
- [Vision-Based Anti Unmanned Aerial Technology: Opportunities and Challenges](https://mlsanity.com/paper/2507.10006/) (2025)
- [Performance Optimization of YOLO-FEDER FusionNet for Robust Drone Detection in Visually Complex Environments](https://mlsanity.com/paper/2509.14012/) (2025)
- [Detector-Augmented SAMURAI for Long-Duration Drone Tracking](https://mlsanity.com/paper/2601.04798/) (2026)
- [Detection and Tracking of MAVs Using a Rosette Scanning Pattern LiDAR](https://mlsanity.com/paper/2408.08555/) (2024)
- [Multi-Sensor Fusion for UAV Classification Based on Feature Maps of Image and Radar Data](https://mlsanity.com/paper/2410.16089/) (2024)
- [LER-YOLO: Reliability-Aware Expert Routing for Misaligned RGB-Infrared UAV Detection](https://mlsanity.com/paper/2605.20667/) (2026)

## Abstract (arXiv)

Automatic detection of flying drones is a key issue where its presence, especially if unauthorized, can create risky situations or compromise security. Here, we design and evaluate a multi-sensor drone detection system. In conjunction with standard video cameras and microphone sensors, we explore the use of thermal infrared cameras, pointed out as a feasible and promising solution that is scarcely addressed in the related literature. Our solution integrates a fish-eye camera as well to monitor a wider part of the sky and steer the other cameras towards objects of interest. The sensing solutions are complemented with an ADS-B receiver, a GPS receiver, and a radar module. However, our final deployment has not included the latter due to its limited detection range. The thermal camera is shown to be a feasible solution as good as the video camera, even if the camera employed here has a lower resolution. Two other novelties of our work are the creation of a new public dataset of multi-sensor annotated data that expands the number of classes compared to existing ones, as well as the study of the detector performance as a function of the sensor-to-target distance. Sensor fusion is also explored, showing that the system can be made more robust in this way, mitigating false detections of the individual sensors.