Research on Simulation System of Small Unmanned Air-to-Ground Object Detection Platform

Abstract

Air-to-ground object detection has important application value in many fields such as military reconnaissance, air strike, personnel rescue, and natural exploration. Object detection algorithm based on deep learning solves the problems of poor generality and low robustness of traditional manual detection algorithms. It has great advantages in air-to-ground detection tasks, but for small unmanned platforms, currently deep learning object detection algorithms have high hardware requirements and are difficult to deploy. In this paper, a deep learning object detection simulation system is built based on the TX2 embedded development board with the features of low power consumption, light weight and fast calculation speed. This object detection simulation experiment is performed for two mainstream deep learning object detection algorithms. The experimental results showed that the regression-based SSD algorithm combined with MobileNets feature extraction network could perform real-time in the embedded system, which laid the foundation for the actual construction of the next unmanned aerial platform.