Maximizing object detection using sUAS

Curtis Manore; Pratheek Manjunath; Dominic Larkin

doi:10.1117/12.2586989

4 January 2021 Maximizing object detection using sUAS

Curtis Manore, Pratheek Manjunath, Dominic Larkin

Proceedings Volume 11605, Thirteenth International Conference on Machine Vision; 1160528 (2021) https://doi.org/10.1117/12.2586989
Event: Thirteenth International Conference on Machine Vision, 2020, Rome, Italy

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.

Citation Download Citation

Curtis Manore, Pratheek Manjunath, and 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

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