Onboard Smart Surveillance for Micro-UAV Swarm: An Experimental Study
Kuang Zhao, Yong Zhou, Zhengyuan Zhou, Dengqing Tang, Yuan Chang, Qiang Fang, Han Zhou, Tianjiang Hu · 2018
In this paper, a smart approach is proposed and developed to enable micro-UAV surveillance with extremely limited onboard computation resources. Recently, a few algorithms have been proposed to detect ground vehicles from UAV aerial vision views. But most of them are processed via air-ground communication and abundant ground computing platforms. However, this study concentrates on the onboard processing scheme of detection and classification on ground moving vehicles by one flying micro UAV. A unified scheme of saliency detection and shallow convolution neural network classification makes a compatible surveillance performance with the only onboard processor. Under such circumstances, an experimental study is conducted on the developed micro-UAV onboard surveillance approach. The experimental results show that the proposed approach can definitely detect and classify at less six kinds of ground moving vehicles up to ~14 fps with only limited onboard computation. This work demonstrates practical feasibility for online vision processing of micro-UAV swarm surveillance on ground objects.