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Open AccessArticle

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