  Rotating object detection has wide applications in aerial photographs, remote
sensing images, UAVs, etc. At present, most of the rotating object detection
datasets focus on the field of remote sensing, and these images are usually
shot in high-altitude scenes. However, image datasets captured at low-altitude
areas also should be concerned, such as drone-based datasets. So we present a
low-altitude dronebased dataset, named UAV-ROD, aiming to promote the research
and development in rotating object detection and UAV applications. The UAV-ROD
consists of 1577 images and 30,090 instances of car category annotated by
oriented bounding boxes. In particular, The UAV-ROD can be utilized for the
rotating object detection, vehicle orientation recognition and object counting
tasks. Compared with horizontal object detection, the regression stage of the
rotation detection is a tricky problem. In this paper, we propose a rotating
object detector TS4Net, which contains anchor refinement module (ARM) and
two-stage sample selective strategy (TS4). The ARM can convert preseted
horizontal anchors into high-quality rotated anchors through twostage anchor
refinement. The TS4 module utilizes different constrained sample selective
strategies to allocate positive and negative samples, which is adaptive to the
regression task in different stages. Benefiting from the ARM and TS4, the
TS4Net can achieve superior performance for rotating object detection solely
with one preseted horizontal anchor. Extensive experimental results on UAV-ROD
dataset and three remote sensing datasets DOTA, HRSC2016 and UCAS-AOD
demonstrate that our method achieves competitive performance against most
state-of-the-art methods.
