End-to-end networks for detection and tracking of micro unmanned aerial vehicles

Aker, Cemal
As the number of micro unmanned aerial vehicles (mUAV) increases, several threats arise. Hence, there is a need for a system that can detect and track them. In this thesis, an object detection model based on convolutional neural networks for mUAV detection, and a novel end-to-end object tracking architecture are proposed. To solve the scarce data problem for training the detection network, an algorithm for creating an extensive artificial dataset by combining background-subtracted real images is proposed. It has been shown that the created dataset is adequate for training well performing networks and that the system can detect and track various types of mUAVs in challenging environments.