Comparison of Object Detectors for Fully Autonomous Aerial Systems Performance

Abstract

Unmanned aerial vehicles (UAVs) are gaining popularity in many governmental and civilian sectors. The combination of aerial mobility and data sensing capabilities facilitates previously impossible workloads. UAVs are normally piloted by remote operators who determine where to fly and when to sense data, but operations over large areas put a heavy burden on human pilots. Fully autonomous aerial systems (FAAS) have emerged as an alternative to human piloting by using software combined with edge and cloud hardware to execute autonomous UAV missions. The compute and networking infrastructure required for autonomy has significant power and performance demands. FAAS deployed in remote environments, such as crop fields, must manage limited power and networking capabilities. To facilitate widespread adoption of FAAS, middleware must support heterogeneous compute and networking resources at the edge while ensuring that the workloads quickly produce effective and efficient autonomous flight paths. Object detectors are a vital component of FAAS. FAAS flight mission goals and flight path generation are often focused on locating and photographing phenomena identified using object detectors. Given the importance of object detection to FAAS, it is paramount that object detectors produce accurate results as quickly and efficiently as possible to elongate FAAS missions and save precious energy. In this poster, we analyze the performance of different object detection techniques for facial recognition, a core FAAS workload. We analyzed the accuracy and performance of three facial recognition techniques provided in SoftwarePilot, an FAAS middleware, on two architectural configurations for FAAS edge systems. These findings can be used when selecting an object detector for any FAAS mission type and hardware configuration.