Drone Detection and Classification using Machine Learning and Sensor Fusion

Abstract: This thesis explores the process of designing an automatic multisensordrone detection system using machine learning and sensorfusion. Besides the more common video and audio sensors, the systemalso includes a thermal infrared camera. The results show thatutilizing an infrared sensor is a feasible solution to the drone detectiontask, and even with slightly lower resolution, the performance isjust as good as a video sensor. The detector performance as a functionof the sensor-to-target distance is also investigated. Using sensor fusion, the system is made more robust than the individualsensors. It is observed that when using the proposed sensorfusion approach, the output system results are more stable, and thenumber of false detections is mitigated. A video dataset containing 650 annotated infrared and visible videosof drones, birds, airplanes and helicopters is published. Additionally,an audio dataset with the classes drones, helicopters and backgroundsis also published.