Simplifying Two-Stage Detectors for On-Device Inference in Remote Sensing

• URL: http://arxiv.org/abs/2404.07405v1
• Date: Thu, 11 Apr 2024 00:45:10 GMT
• Title: Simplifying Two-Stage Detectors for On-Device Inference in Remote Sensing
• Authors: Jaemin Kang, Hoeseok Yang, Hyungshin Kim,
• Abstract summary: We propose a model simplification method for two-stage object detectors. Our method reduces computation costs upto 61.2% with the accuracy loss within 2.1% on the DOTAv1.5 dataset.
• Score: 0.7305342793164903
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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