  Drones or UAVs, equipped with different sensors, have been deployed in many
places especially for urban traffic monitoring or last-mile delivery. It
provides the ability to control the different aspects of traffic given
real-time obeservations, an important pillar for the future of transportation
and smart cities. With the increasing use of such machines, many previous
state-of-the-art object detectors, who have achieved high performance on front
facing cameras, are being used on UAV datasets. When applied to high-resolution
aerial images captured from such datasets, they fail to generalize to the wide
range of objects' scales. In order to address this limitation, we propose an
object detection method called Butterfly Detector that is tailored to detect
objects in aerial images. We extend the concept of fields and introduce
butterfly fields, a type of composite field that describes the spatial
information of output features as well as the scale of the detected object. To
overcome occlusion and viewing angle variations that can hinder the
localization process, we employ a voting mechanism between related butterfly
vectors pointing to the object center. We evaluate our Butterfly Detector on
two publicly available UAV datasets (UAVDT and VisDrone2019) and show that it
outperforms previous state-of-the-art methods while remaining real-time.
