舰船电子工程2023，Vol.43Issue(10)：120-125,6.DOI:10.3969/j.issn.1672-9730.2023.10.026
基于LSTM-YOLOv5的"黑飞"无人机异常行为辨识方法研究
Research on Abnormal Behavior Recognition Method of"Black Fly"Drones Based on LSTM-YOLOv5
袁江 1兰增武 1熊鹏1
作者信息
- 1. 中国长江电力股份有限公司 宜昌 443000
- 折叠
摘要
Abstract
In view of the problem that the"black fly"drone has a low flying trajectory,small size,slow speed,and produces extremely low signal echo data volume and time domain signal resolution,and the feature feedback information is difficult to be cap-tured by traditional radar detection equipment,this paper proposes a method for identifying abnormal behaviors of"black fly"drones based on LSTM-YOLOv5.The YOLOv5 algorithm is used to track and identify the drone targets,and the advantages of LSTM in processing time series are combined to analyze the flight trend changes in the time slice to predict and identify the abnor-mal behavior of"black fly"drones.The results show that the proposed method has high accuracy and robustness in the identification and classification of abnormal behaviors of drones,and can effectively deal with the threats caused by"black fly"drones.关键词
"黑飞"无人机/LSTM-YOLOv5/异常行为辨识/跟踪识别Key words
"black fly"drone/LSTM-YOLOv5/abnormal behavior identification/tracking and recognition分类
航空航天引用本文复制引用
袁江,兰增武,熊鹏..基于LSTM-YOLOv5的"黑飞"无人机异常行为辨识方法研究[J].舰船电子工程,2023,43(10):120-125,6.
