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Sensors 2018, 18(11), 3825; https://doi.org/10.3390/s18113825

V-RBNN Based Small Drone Detection in Augmented Datasets for 3D LADAR System

1
School of Electronics Engineering, Kyungpook National University, Daegu 41566, Korea
2
Hanwha Systems Corporation, Optronics Team, Gumi 39376, Korea
3
Faculty of Computer and Information Science, University of Ljubljana, SI-1000 Ljubljana, Slovenia
4
Agency for Defense Development, Yuseong, Daejeon 34186, Korea
5
Research Center for Neurosurgical Robotic System, Kyungpook National University, Daegu 41566, Korea
*
Author to whom correspondence should be addressed.
Received: 10 October 2018 / Revised: 3 November 2018 / Accepted: 5 November 2018 / Published: 8 November 2018
(This article belongs to the Special Issue Laser Sensors for Displacement, Distance and Position)
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Abstract

A common countermeasure to detect threatening drones is the electro-optical infrared (EO/IR) system. However, its performance is drastically reduced in conditions of complex background, saturation and light reflection. 3D laser sensor LiDAR is used to overcome the problems of 2D sensors like EO/IR, but it is not enough to detect small drones at a very long distance because of low laser energy and resolution. To solve this problem, A 3D LADAR sensor is under development. In this work, we study the detection methodology adequate to the LADAR sensor which can detect small drones at up to 2 km. First, a data augmentation method is proposed to generate a virtual target considering the laser beam and scanning characteristics, and to augment it with the actual LADAR sensor data for various kinds of tests before full hardware system developed. Second, a detection algorithm is proposed to detect drones using voxel-based background subtraction and variable radially bounded nearest neighbor (V-RBNN) method. The results show that 0.2 m L2 distance and 60% expected average overlap (EAO) indexes are satisfied for the required specification to detect 0.3 m size of small drones. View Full-Text
Keywords: drone detection; clustering; 3D sensor; LiDAR; fusion data; 3D LADAR drone detection; clustering; 3D sensor; LiDAR; fusion data; 3D LADAR
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Kim, B.H.; Khan, D.; Bohak, C.; Choi, W.; Lee, H.J.; Kim, M.Y. V-RBNN Based Small Drone Detection in Augmented Datasets for 3D LADAR System. Sensors 2018, 18, 3825.

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