Accelerating a two-stage object detector for high quality in-orbit remote sensing

Jaemin Kang; Jinse Kwon; Hyungshin Kim

doi:10.1117/12.2638475

26 October 2022 Accelerating a two-stage object detector for high quality in-orbit remote sensing

Jaemin Kang, Jinse Kwon, Hyungshin Kim

Proceedings Volume 12267, Image and Signal Processing for Remote Sensing XXVIII; 122670B (2022) https://doi.org/10.1117/12.2638475
Event: SPIE Remote Sensing, 2022, Berlin, Germany

Abstract

Object detection from remote sensing images has been performed on the ground. Recently, on-board object detection has been studied only to show its feasibility with single-stage detectors. However, highly accurate models such as two stage detectors are compute intensive so that they are too slow to run on power-constrained on-board computers. In this paper, we propose a speed-up method for two-stage detectors. Two-stage detectors extract features and ROIs(Region of Interest) in the first stage and then classify them at the second stage. This structure gives high accuracy but induces large inference latency. In remote sensing images from satellites, object size is small relative to the whole image. Based on this characteristic, we propose to exclude features related to the large objects in the first stage. To verify our concept, we have selected various R-CNN models as two-stage object detectors. We have implemented our methods on two NVIDIA Jetson boards. We have achieved 1.8x speed up in inference latency with 5% accuracy drop with the small object dataset.

Citation Download Citation

Jaemin Kang, Jinse Kwon, and Hyungshin Kim "Accelerating a two-stage object detector for high quality in-orbit remote sensing", Proc. SPIE 12267, Image and Signal Processing for Remote Sensing XXVIII, 122670B (26 October 2022); https://doi.org/10.1117/12.2638475

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