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Deep Learning Based Drone Detection and Classification

딥러닝 기반 드론 검출 및 분류

  • Yi, Keon Young (Dept. of Electrical Engineering, Kwangwoon University) ;
  • Kyeong, Deokhwan (Dept. of Electronics Engineering, Seokyeong University) ;
  • Seo, Kisung (Dept. of Electronics Engineering, Seokyeong University)
  • Received : 2018.12.07
  • Accepted : 2019.01.17
  • Published : 2019.02.01

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.

Keywords

References

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