  Deep convolutional neural network with increased number of parameters has
achieved improved precision in task of object detection on natural images,
where objects of interests are annotated with horizontal boundary boxes. On
aerial images captured from the bird-view perspective, these improvements on
model architecture and deeper convolutional layers can also boost the
performance on oriented object detection task. However, it is hard to directly
apply those state-of-the-art object detectors on the devices with limited
computation resources, which necessitates lightweight models through model
compression. In order to address this issue, we present a model compression
method for rotated object detection on aerial images by knowledge distillation,
namely KD-RNet. With a well-trained teacher oriented object detector with a
large number of parameters, the obtained object category and location
information are both transferred to a compact student network in KD-RNet by
collaborative training strategy. Transferring the category information is
achieved by knowledge distillation on predicted probability distribution, and a
soft regression loss is adopted for handling displacement in location
information transfer. The experimental result on a large-scale aerial object
detection dataset (DOTA) demonstrates that the proposed KD-RNet model can
achieve improved mean-average precision (mAP) with reduced number of
parameters, at the same time, KD-RNet boost the performance on providing high
quality detections with higher overlap with groundtruth annotations.
