  The use of small and remotely controlled unmanned aerial vehicles (UAVs), or
drones, has increased in recent years. This goes in parallel with misuse
episodes, with an evident threat to the safety of people or facilities. As a
result, the detection of UAV has also emerged as a research topic. Most studies
on drone detection fail to specify the type of acquisition device, the drone
type, the detection range, or the dataset. The lack of proper UAV detection
studies employing thermal infrared cameras is also an issue, despite its
success with other targets. Besides, we have not found any previous study that
addresses the detection task as a function of distance to the target. Sensor
fusion is indicated as an open research issue as well, although research in
this direction is scarce too. To counteract the mentioned issues and allow
fundamental studies with a common public benchmark, we contribute with an
annotated multi-sensor database for drone detection that includes infrared and
visible videos and audio files. The database includes three different drones,
of different sizes and other flying objects that can be mistakenly detected as
drones, such as birds, airplanes or helicopters. In addition to using several
different sensors, the number of classes is higher than in previous studies. To
allow studies as a function of the sensor-to-target distance, the dataset is
divided into three categories (Close, Medium, Distant) according to the
industry-standard Detect, Recognize and Identify (DRI) requirements, built on
the Johnson criteria. Given that the drones must be flown within visual range
due to regulations, the largest sensor-to-target distance for a drone is 200 m,
and acquisitions are made in daylight. The data has been obtained at three
airports in Sweden: Halmstad Airport (IATA code: HAD/ICAO code: ESMT),
Gothenburg City Airport (GSE/ESGP) and Malm\"o Airport (MMX/ESMS).
