基于轨迹和形态识别的无人机检测方法

Translated title of the contribution: Unmanned Aerial Vehicle Detection Based on Trajectory and Pattern Recognition

Yicheng LIU, Luchuan LIAO, Jing ZHANG, Hao WU, Ling HE, 戴弘宁, Han ZHANG, Gang YANG

Research output: Journal PublicationsJournal Article (refereed)peer-review

Abstract

无人机因具有体型小以及受环境干扰大等因素导致其在可见光图像序列下的检测准确率较低。为此,提出一种新的无人机检测方法。通过转台相机捕获飞行物形态变化,使用轨迹聚类算法获得运动小目标轨迹,提取并融合目标的轨迹特征和形态特征,进而通过人工神经网络识别目标,并采用小范围搜索算法进行追踪,同时运用干扰无线电定向压制无人机。实验结果表明,该方法对无人机和飞鸟的识别准确率达到99.53%,且能够实时检测、识别和追踪。

Many factors such as the interference and the small fuselage of Unmanned Aerial Vehicle(UAV) pose challenges to the high precision detection of the UAV in visible image sequences. Therefore, this paper proposes a new UAV detection method. The shape change of the flying object is captured by the turntable camera. Then the trajectory of the small moving target is obtained by using the trajectory clustering algorithm. The trajectory characteristics and morphological characteristics of the target are extracted and fused, and on this basis the target is identified through the Artificial Neural Network(ANN).At the same time, the small-range search algorithm is used to track the target and the jamming radio is used to suppress the UAV. Experimental results show that the method increases its UAV and bird detection accuracy to 99.53%,and can provide real-time detection, recognition and tracking of these targets.
Translated title of the contributionUnmanned Aerial Vehicle Detection Based on Trajectory and Pattern Recognition
Original languageChinese (Simplified)
Pages (from-to)283-289
Journal计算机工程
Volume46
Issue number12
DOIs
Publication statusPublished - Dec 2020
Externally publishedYes

Keywords

  • 无人机检测
  • 轨迹聚类
  • 特征提取
  • 轨迹识别
  • 人工神经网络
  • 目标追踪
  • Unmanned Aerial Vehicle(UAV) detection
  • trajectory clustering
  • feature extraction
  • trajectory recognition
  • Artificial Neural Network (ANN)
  • object tracking

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