A Resource-Efficient Online Target Detection System With Autonomous Drone-Assisted IoT

Abstract

Mobile onboard target detection system with autonomous drone-assisted Internet of Things, due to its inherent agility and coverage-effective deployment, is beneficial in city management, ecosystem monitoring, etc. However, most advanced detection methods become inefficient or even malfunction, since the computation resources for online detection in such a high-altitude dynamic environment are far beyond the capability of the drone. To this end, we design and implement ODTDS—an online drone-based target detection system that performs online data processing and autonomous navigation simultaneously with restricted resources. Specifically, to prolong detection durations with limited energy providing for continuous processing and flying, we develop an adaptive motion planner for autonomous and energy-efficient navigation. Meanwhile, to perform online target detection from complex environments, we propose a hybrid method integrating feature pyramid feedback with the speed up robust features, to enhance the background information to achieve high accuracy on the restricted computation platform of the drone. Based on these two designs, our system can address resource-constrained challenges to accomplish detection missions autonomously. Finally, we implement an ODTDS prototype and evaluate it through outdoor high-altitude experiments. The results of various performance evaluations can demonstrate the effectiveness of our system.