Trajectory and image-based detection and identification of UAV

Yicheng Liu, Luchuan Liao, Hao Wu, Jing Qin, Ling He, Gang Yang, Han Zhang, Jing Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

10 Citations (Scopus)

Abstract

Much more attentions have been attracted to the inspection and prevention of unmanned aerial vehicle (UAV) in the wake of increasing high frequency of security accident. Many factors like the interferences and the small fuselage of UAV pose challenges to the timely detection of the UAV. In our work, we present a system that is capable of detecting, recognizing, and tracking an UAV using single camera automatically. For our method, a single pan–tilt–zoom (PTZ) camera detects flying objects and gets their trajectories; then, the trajectory identified as a UAV guides the camera and PTZ to capture the detailed region image of the target. Therefore, the images can be classified into the UAV and interference classes (such as birds) by the convolution neural network classifier trained with our image dataset. For the target recognized as a UAV with the double verification, the radio jammer emits the interferential radio to disturb its control radio and GPS. This system could be applied in some complex environment where many birds and UAV appear simultaneously.

Original languageEnglish
Pages (from-to)1769-1780
Number of pages12
JournalVisual Computer
Volume37
Issue number7
DOIs
Publication statusPublished - Jul 2021

Keywords

  • Deep learning
  • Drone detection
  • Object recognition
  • Tracking
  • Trajectory identification
  • UAV

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design

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