  Object detection in aerial images is an active yet challenging task in
computer vision because of the birdview perspective, the highly complex
backgrounds, and the variant appearances of objects. Especially when detecting
densely packed objects in aerial images, methods relying on horizontal
proposals for common object detection often introduce mismatches between the
Region of Interests (RoIs) and objects. This leads to the common misalignment
between the final object classification confidence and localization accuracy.
Although rotated anchors have been used to tackle this problem, the design of
them always multiplies the number of anchors and dramatically increases the
computational complexity. In this paper, we propose a RoI Transformer to
address these problems. More precisely, to improve the quality of region
proposals, we first designed a Rotated RoI (RRoI) learner to transform a
Horizontal Region of Interest (HRoI) into a Rotated Region of Interest (RRoI).
Based on the RRoIs, we then proposed a Rotated Position Sensitive RoI Align
(RPS-RoI-Align) module to extract rotation-invariant features from them for
boosting subsequent classification and regression. Our RoI Transformer is with
light weight and can be easily embedded into detectors for oriented object
detection. A simple implementation of the RoI Transformer has achieved
state-of-the-art performances on two common and challenging aerial datasets,
i.e., DOTA and HRSC2016, with a neglectable reduction to detection speed. Our
RoI Transformer exceeds the deformable Position Sensitive RoI pooling when
oriented bounding-box annotations are available. Extensive experiments have
also validated the flexibility and effectiveness of our RoI Transformer. The
results demonstrate that it can be easily integrated with other detector
architectures and significantly improve the performances.
