Real-Time Drone Detection and Tracking With Visible, Thermal and Acoustic Sensors

TL;DR

This paper presents a multi-sensor drone detection system combining visible, thermal, and acoustic sensors, demonstrating improved robustness and introducing a new annotated dataset for drone detection research.

Contribution

The paper introduces a novel multi-sensor fusion approach for drone detection and provides a new annotated dataset for the community.

Findings

Thermal infrared cameras are effective for drone detection despite lower resolution.

Sensor fusion reduces false detections and enhances robustness.

Performance varies with sensor-to-target distance, informing system design.

Abstract

This paper explores the process of designing an automatic multi-sensor drone detection system. Besides the common video and audio sensors, the system also includes a thermal infrared camera, which is shown to be a feasible solution to the drone detection task. Even with slightly lower resolution, the performance is just as good as a camera in visible range. The detector performance as a function of the sensor-to-target distance is also investigated. In addition, using sensor fusion, the system is made more robust than the individual sensors, helping to reduce false detections. To counteract the lack of public datasets, a novel video dataset containing 650 annotated infrared and visible videos of drones, birds, airplanes and helicopters is also presented (https://github.com/DroneDetectionThesis/Drone-detection-dataset). The database is complemented with an audio dataset of the classes drones, helicopters and background noise.