  Unmanned aerial vehicles (UAVs) with mounted cameras have the advantage of
capturing aerial (bird-view) images. The availability of aerial visual data and
the recent advances in object detection algorithms led the computer vision
community to focus on object detection tasks on aerial images. As a result of
this, several aerial datasets have been introduced, including visual data with
object annotations. UAVs are used solely as flying-cameras in these datasets,
discarding different data types regarding the flight (e.g., time, location,
internal sensors). In this work, we propose a multi-purpose aerial dataset
(AU-AIR) that has multi-modal sensor data (i.e., visual, time, location,
altitude, IMU, velocity) collected in real-world outdoor environments. The
AU-AIR dataset includes meta-data for extracted frames (i.e., bounding box
annotations for traffic-related object category) from recorded RGB videos.
Moreover, we emphasize the differences between natural and aerial images in the
context of object detection task. For this end, we train and test mobile object
detectors (including YOLOv3-Tiny and MobileNetv2-SSDLite) on the AU-AIR
dataset, which are applicable for real-time object detection using on-board
computers with UAVs. Since our dataset has diversity in recorded data types, it
contributes to filling the gap between computer vision and robotics. The
dataset is available at https://bozcani.github.io/auairdataset.
