  Rotated object detection in aerial images has received increasing attention
for a wide range of applications. However, it is also a challenging task due to
the huge variations of scale, rotation, aspect ratio, and densely arranged
targets. Most existing methods heavily rely on a large number of pre-defined
anchors with different scales, angles, and aspect ratios, and are optimized
with a distance loss. Therefore, these methods are sensitive to anchor
hyper-parameters and easily suffer from performance degradation caused by
boundary discontinuity. To handle this problem, in this paper, we propose a
dense anchor-free rotated object detector (DARDet) for rotated object detection
in aerial images. Our DARDet directly predicts five parameters of rotated boxes
at each foreground pixel of feature maps. We design a new alignment convolution
module to extracts aligned features and introduce a PIoU loss for precise and
stable regression. Our method achieves state-of-the-art performance on three
commonly used aerial objects datasets (i.e., DOTA, HRSC2016, and UCAS-AOD)
while keeping high efficiency. Code is available at
https://github.com/zf020114/DARDet.
