| Title |
Deep Learning Based Drone Detection and Classification |
| Authors |
이건영(Keon Young Yi) ; 경덕환(Deokhwan Kyeong) ; 서기성(Kisung Seo) |
| DOI |
http://doi.org/10.5370/KIEE.2019.68.2.359 |
| Keywords |
Drone detection and classification ; Deep learning ; Convolution neural network ; YOLOv2 |
| Abstract |
As commercial drones have been widely used, concerns for collision accidents with people and invading secured properties are emerging. The detection of drone is a challenging problem. The deep learning based object detection techniques for detecting drones have been applied, but limited to the specific cases such as detection of drones from bird and/or background. We have tried not only detection of drones, but classification of different drones with an end-to-end model. YOLOv2 is used as an object detection model. In order to supplement insufficient data by shooting drones, data augmentation from collected images is executed. Also transfer learning from ImageNet for YOLOv2 darknet framework is performed. The experimental results for drone detection with average IoU and recall are compared and analysed. |