Simplifying Two-Stage Detectors for On-Device Inference in Remote
  Sensing
Jaemin Kang Hoeseok Yang Hyungshin Kim
Deep learning has been successfully applied to object detection from remotely
sensed images. Images are typically processed on the ground rather than
on-board due to the computation power of the ground system. Such offloaded
processing causes delays in acquiring target mission information, which hinders
its application to real-time use cases. For on-device object detection,
researches have been conducted on designing efficient detectors or model
compression to reduce inference latency. However, highly accurate two-stage
detectors still need further exploitation for acceleration. In this paper, we
propose a model simplification method for two-stage object detectors. Instead
of constructing a general feature pyramid, we utilize only one feature
extraction in the two-stage detector. To compensate for the accuracy drop, we
apply a high pass filter to the RPN's score map. Our approach is applicable to
any two-stage detector using a feature pyramid network. In the experiments with
state-of-the-art two-stage detectors such as ReDet, Oriented-RCNN, and LSKNet,
our method reduced computation costs upto 61.2% with the accuracy loss within
2.1% on the DOTAv1.5 dataset. Source code will be released.