An End-to-End Practice of Remote Sensing Object Detection with NVIDIA Embedded System

This paper aims to develop an efficient method of remote sensing object detection with deep learning in an end-to-end manner. We proposed a method of region detection followed by target detection to detect small-scale targets from large-scale remote sensing images, with a light-weighted neural network trained on our own dataset. For a simulation of practical detection algorithm deployment, which requires both accuracy and efficiency on hardware of limited compute power, the whole algorithm was implemented and tested on NVIDIA Jetson AGX Xavier embedded platform.