The Transactions of The Korean Institute of Electrical Engineers (전기학회논문지)

The Korean Institute of Electrical Engineers (대한전기학회)
권호 표지
DOI QR코드

DOI QR Code

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
⟨ Previous Next ⟩

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

  1. D. R. Lee, W. G. La, H. Kim, "Drone Detection and Identification System using Artificial Intelligence", IEEE Int. Conf. on Information and Communication Technology Convergence (ICTC), pp. 1131-1133, 2018.
  2. J. Park, D. Kim, Y. Shin, S. Lee, "A Comparison of Convolutional Object Detectors for Real-time Drone Tracking Using a PTZ Camera," 2017 17th International Conference on Control, Automation and Systems (ICCAS 2017), pp. 696-699. 2017.
  3. Y. Chen, P. Aggarwal, J. Choi, and C. Kuo, "A Deep Learning Approach to Drone Monitoring," Proceedings of APSIPA Annual Summit and Conference 2017, pp. 686-691, 2017.
  4. M. Saqib, S. D. Khan, N. Sharma, M. Blumenstein, "A Study on Detecting Drones Using Deep Convolutional Neural Networks", IEEE Int. Conf. on Advanced Video and Signal Based Surveillance (AVSS), pp 1-5, 2017.
  5. C. Aker, S. Kalkan, "Using deep networks for drone detection", IEEE Int. Conf. on Advanced Video and Signal Based Surveillance (AVSS), pp 1-6, 2017.
  6. M. Lee, K, Seo, "Comparison of Region-based CNN Methods for Defects Detection on Metal Surface," The Transactions of the Korean Institute of Electrical Engineers, Vol. 67, No. 7, pp. 865~870, 2018. https://doi.org/10.5370/KIEE.2018.67.7.865
  7. Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun, "Faster r-cnn: Towards real-time object detection with region proposal networks," Advances in neural information processing systems, pp. 91-99, 2015.
  8. Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi, "You Only Look Once: Unified, Real-Time Object Detection," Proceedings of the IEEE conference on computer vision and pattern recognition, 2016.
  9. Joseph Redmon, Ali Farhadi, "YOLO9000: better, faster, stronger," arXiv:1612.08242, 2017.
  10. S. Pan, Q. Yang, "A survey on transfer learning", IEEE Transactions on knowledge and data engineering, Vol. 22, No. 10, pp. 1345-1359, 2010. https://doi.org/10.1109/TKDE.2009.191