Real-time UAV Detection based on Deep Learning Network
Syed Ali Hassan, Tariq Rahim, Soo Young Shin · 2019
This paper presents deep learning-based YOLO (You only look once), for the detection of an unmanned aerial vehicle (UAV). In common practice, the creation of own data set is an extensive and hectic task, that takes a long time because it requires proper resolution images from different angles. These issues make the data set creation an important task. Implementation of YOLOv2 and YOLOv3 is done on the own created data set for the real-time UAV's detection and to benchmark the performance of both models in terms of mean average precision (MAP) and accuracy. For the specifically created data set made, YOLOv3 is outperforming YOLOv2 both in MAP and accuracy.