LAUSR

Abstract. Remotely piloted aircraft systems (RPAS) have introduced a new ability to quickly deploy low-cost, fully or partially autonomous aerial sensor platforms, which has created new intelligence, surveillance, and reconnaissance capabilities in various domains using cameras that are ubiquitous in most RPAS. The mounted cameras acquire images or full-motion video (FMV) which can be analyzed using object detection algorithms for locating and classifying one or more specified targets. To date, there has not been much published work regarding the effect of the RPAS flight parameters on the performance of object detection algorithms. To explore the use of object detection on aerial FMV acquired at various RPAS flight parameter settings, a dataset acquisition campaign was launched resulting in 8.5 h of RPAS-acquired FMV. Analysis and interpretation of the acquired dataset revealed that state-of-the-art performance was achieved using a modified you only look once object detection algorithm when the RPAS was deployed under an altitude of 30 m, at a velocity of under 7 m/s, and at pitch angles ranging from 25 deg to 65 deg while acquiring FMV at a resolution of 4.16 MP. The experimental results show that, when flown under specific conditions, RPAS are an effective and reliable platform for acquiring aerial FMV for the purpose of object detection which has a variety of different applications, such as peace support, public safety, and aerial monitoring.