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

Sensors

HyunJeong Lee¹  Ciril Bohak²  Wonju Choi³  MinYoung Kim⁴  ByeongHak Kim⁴  Danish Khan⁴ 

[1] Agency for Defense Development, Yuseong, Daejeon 34186, Korea;Faculty of Computer and Information Science, University of Ljubljana, SI-1000 Ljubljana, Slovenia;Hanwha Systems Corporation, Optronics Team, Gumi 39376, Korea;School of Electronics Engineering, Kyungpook National University, Daegu 41566, Korea;

关键词: drone detection;    clustering;    3D sensor;    LiDAR;    fusion data;    3D LADAR;   

DOI  :  10.3390/s18113825

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown