Exploitation of data augmentation strategies for improved UAV detection
The increased availability of UAVs poses possibilities for novel applications but also safety issues regarding mass events or safety-sensitive infrastructure. Thus, the demand for automated UAV detection systems to allow for an early alert generation is increasing. Such systems often rely on electro-optical imagery and deep learning to detect UAVs. However, the absence of a large and diverse dataset for training may result in an error-prone learning process. In this work, we investigate how far these issues can be mitigated without relying on extra data, which is often costly to obtain and annotate. We thus evaluate and demonstrate the impact of different data augmentation strategies to enhance our available training data. We evaluate how the different methods increase the robustness of several state of the art deep learning based detectors. Particularly, we focus our evaluation on the aspects of false alarms caused by distractor objects or by complex background.