  Unmanned Aerial Vehicles (UAV) can pose a major risk for aviation safety, due
to both negligent and malicious use. For this reason, the automated detection
and tracking of UAV is a fundamental task in aerial security systems. Common
technologies for UAV detection include visible-band and thermal infrared
imaging, radio frequency and radar. Recent advances in deep neural networks
(DNNs) for image-based object detection open the possibility to use visual
information for this detection and tracking task. Furthermore, these detection
architectures can be implemented as backbones for visual tracking systems,
thereby enabling persistent tracking of UAV incursions. To date, no
comprehensive performance benchmark exists that applies DNNs to visible-band
imagery for UAV detection and tracking. To this end, three datasets with varied
environmental conditions for UAV detection and tracking, comprising a total of
241 videos (331,486 images), are assessed using four detection architectures
and three tracking frameworks. The best performing detector architecture
obtains an mAP of 98.6% and the best performing tracking framework obtains a
MOTA of 96.3%. Cross-modality evaluation is carried out between visible and
infrared spectrums, achieving a maximal 82.8% mAP on visible images when
training in the infrared modality. These results provide the first public
multi-approach benchmark for state-of-the-art deep learning-based methods and
give insight into which detection and tracking architectures are effective in
the UAV domain.
