Real-Time Drone Detection Using Deep Learning Approach

  • Manjia WuEmail author
  • Weige Xie
  • Xiufang Shi
  • Panyu Shao
  • Zhiguo Shi
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 251)


The arbitrary use of drones poses great threat to public safety and personal privacy. It is necessary to detect the intruding drones in sensitive areas in real time. In this paper, we design a real-time drone detector using deep learning approach. Specifically, we improve a well-performed deep learning model, i.e., You Only Look Once, by modifying its structure and tuning its parameters to better accommodate drone detection. Considering that a robust detector needs to be trained using a large amount of training images, we also propose a semi-automatically dataset labelling method based on Kernelized Correlation Filters tracker to speed up the pre-processing of the training images. At last, the performance of our detector is verified via extensive experiments.


Drone detection Deep learning Visual detection 



This work was supported by NSFC under Grant 61772467, Zhejiang Provincial Natural Science Foundation of China under Grant LR16F010002, 973 Project under Grant 2015CB352503, the Fundamental Research Funds for the Central Universities (2017XZZX009-01), and China Postdoctoral Science Foundation funded project.


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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  • Manjia Wu
    • 1
    Email author
  • Weige Xie
    • 1
    • 2
  • Xiufang Shi
    • 1
    • 2
  • Panyu Shao
    • 1
    • 2
  • Zhiguo Shi
    • 2
  1. 1.State Key Laboratory of Industrial Control TechnologyZhejiang UniversityHangzhouChina
  2. 2.College of Information Science and Electronic EngineeringZhejiang UniversityHangzhouChina

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