  Multi-class vehicle detection from airborne imagery with orientation
estimation is an important task in the near and remote vision domains with
applications in traffic monitoring and disaster management. In the last decade,
we have witnessed significant progress in object detection in ground imagery,
but it is still in its infancy in airborne imagery, mostly due to the scarcity
of diverse and large-scale datasets. Despite being a useful tool for different
applications, current airborne datasets only partially reflect the challenges
of real-world scenarios. To address this issue, we introduce EAGLE (oriEnted
vehicle detection using Aerial imaGery in real-worLd scEnarios), a large-scale
dataset for multi-class vehicle detection with object orientation information
in aerial imagery. It features high-resolution aerial images composed of
different real-world situations with a wide variety of camera sensor,
resolution, flight altitude, weather, illumination, haze, shadow, time, city,
country, occlusion, and camera angle. The annotation was done by airborne
imagery experts with small- and large-vehicle classes. EAGLE contains 215,986
instances annotated with oriented bounding boxes defined by four points and
orientation, making it by far the largest dataset to date in this task. It also
supports researches on the haze and shadow removal as well as super-resolution
and in-painting applications. We define three tasks: detection by (1)
horizontal bounding boxes, (2) rotated bounding boxes, and (3) oriented
bounding boxes. We carried out several experiments to evaluate several
state-of-the-art methods in object detection on our dataset to form a baseline.
Experiments show that the EAGLE dataset accurately reflects real-world
situations and correspondingly challenging applications.
