  Recent advances in camera equipped drone applications and their widespread
use increased the demand on vision based object detection algorithms for aerial
images. Object detection process is inherently a challenging task as a generic
computer vision problem, however, since the use of object detection algorithms
on UAVs (or on drones) is relatively a new area, it remains as a more
challenging problem to detect objects in aerial images. There are several
reasons for that including: (i) the lack of large drone datasets including
large object variance, (ii) the large orientation and scale variance in drone
images when compared to the ground images, and (iii) the difference in texture
and shape features between the ground and the aerial images. Deep learning
based object detection algorithms can be classified under two main categories:
(a) single-stage detectors and (b) multi-stage detectors. Both single-stage and
multi-stage solutions have their advantages and disadvantages over each other.
However, a technique to combine the good sides of each of those solutions could
yield even a stronger solution than each of those solutions individually. In
this paper, we propose an ensemble network, SyNet, that combines a multi-stage
method with a single-stage one with the motivation of decreasing the high false
negative rate of multi-stage detectors and increasing the quality of the
single-stage detector proposals. As building blocks, CenterNet and Cascade
R-CNN with pretrained feature extractors are utilized along with an ensembling
strategy. We report the state of the art results obtained by our proposed
solution on two different datasets: namely MS-COCO and visDrone with \%52.1
$mAP_{IoU = 0.75}$ is obtained on MS-COCO $val2017$ dataset and \%26.2
$mAP_{IoU = 0.75}$ is obtained on VisDrone $test-set$.
