  Fast and precise object detection for high-resolution aerial images has been
a challenging task over the years. Due to the sharp variations on object scale,
rotation, and aspect ratio, most existing methods are inefficient and
imprecise. In this paper, we represent the oriented objects by polar method in
polar coordinate and propose PolarDet, a fast and accurate one-stage object
detector based on that representation. Our detector introduces a sub-pixel
center semantic structure to further improve classifying veracity. PolarDet
achieves nearly all SOTA performance in aerial object detection tasks with
faster inference speed. In detail, our approach obtains the SOTA results on
DOTA, UCAS-AOD, HRSC with 76.64\% mAP, 97.01\% mAP, and 90.46\% mAP
respectively. Most noticeably, our PolarDet gets the best performance and
reaches the fastest speed(32fps) at the UCAS-AOD dataset.
