Simplifying Two-Stage Detectors for On-Device Inference in Remote Sensing
<|reference_start|>Simplifying Two-Stage Detectors for On-Device Inference in Remote Sensing: 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.<|reference_end|>
@article{kang2024simplifying,
    title={Simplifying Two-Stage Detectors for On-Device Inference in Remote
  Sensing},
    author={Jaemin Kang, Hoeseok Yang, Hyungshin Kim},
    journal={arXiv preprint arXiv:2404.07405},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.07405},
    primaryClass={cs.CV}
}