Drone Detection and Tracking in Real-Time by Fusion of Different Sensing Modalities
This work addresses security risks from unauthorized drones, but it is incremental as it builds on existing multi-sensor approaches with minor novelties like a new dataset.
The authors tackled the problem of real-time drone detection by developing a multi-sensor system that integrates video, thermal infrared, and other sensors, showing that thermal cameras perform comparably to video cameras despite lower resolution and that sensor fusion reduces false detections.
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.