Maximizing object detection using sUAS
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Authors
Manore, Curtis
Manjunath, Pratheek
Larkin, Dominic
Issue Date
2021-01-04
Type
proceedings-article
Language
Keywords
small unmanned aerial system (sUAS) , Convolutional Neural Network
Alternative Title
Abstract
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.
Description
Citation
Curtis Manore, Pratheek Manjunath, Dominic Larkin, "Maximizing object detection using sUAS," Proc. SPIE 11605, Thirteenth International Conference on Machine Vision, 1160528 (4 January 2021); https://doi.org/10.1117/12.2586989
Publisher
SPIE