Simplifying Two-Stage Detectors for On-Device Inference in Remote Sensing
arxiv(2024)
摘要
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
2.1
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