  Object detection has been a building block in computer vision. Though
considerable progress has been made, there still exist challenges for objects
with small size, arbitrary direction, and dense distribution. Apart from
natural images, such issues are especially pronounced for aerial images of
great importance. This paper presents a novel multi-category rotation detector
for small, cluttered and rotated objects, namely SCRDet. Specifically, a
sampling fusion network is devised which fuses multi-layer feature with
effective anchor sampling, to improve the sensitivity to small objects.
Meanwhile, the supervised pixel attention network and the channel attention
network are jointly explored for small and cluttered object detection by
suppressing the noise and highlighting the objects feature. For more accurate
rotation estimation, the IoU constant factor is added to the smooth L1 loss to
address the boundary problem for the rotating bounding box. Extensive
experiments on two remote sensing public datasets DOTA, NWPU VHR-10 as well as
natural image datasets COCO, VOC2007 and scene text data ICDAR2015 show the
state-of-the-art performance of our detector. The code and models will be
available at https://github.com/DetectionTeamUCAS.
