Maximizing object detection using sUAS

Contributing USMA Research Unit(s)

Robotics Research Center

Publication Date


Publication Title

Thirteenth International Conference on Machine Vision, 2020, Rome, Italy

Document Type

Conference Proceeding


This paper examines optimal look-angles for a camera which is mounted on a small unmanned aerial system (sUAS), that provides for maximized object detection on the ground. Using a generic convolutional neural network (CNN), this research identifies the best angle for detecting a ground target from an aerial perspective. The study involves altering camera angles on an sUAS that is flown along a fixed trajectory and then determining the angle which provides the highest detection rate of predefined objects, which are emplaced at known locations on the ground. The experiment is conducted in simulation and validated on a physical quadcopter. The results of this paper directly influence the U.S. Army’s research efforts on training neural networks and developing object detection algorithms.

First Page


1160528.pdf (1000 kB)
Paper downloaded from SPIE Digital Library

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