基于DCNN-LSTM模型的船舶违章行为检测
Ship Violation Behavior Detection Based on DCNN-LSTM Model
郑元洲 1李鑫 1钱龙 1秦瑞朋 1李果 1李梦希1
作者信息
- 1. 武汉理工大学 航运学院,湖北 武汉 430063;内河航运技术湖北省重点实验室,湖北 武汉 430063
- 折叠
摘要
桥区水域船舶违章行为的精准检测对于预控船桥碰撞至关重要.为保障船舶航行安全,提出了一种面向桥区水域的船舶违章行为检测模型.通过实时采集长江武汉段连续桥区船舶自动识别系统(AIS)数据及预处理工作,采用卷积神经网络(CNN)提取船舶行为信息,与长短时记忆神经网络(LSTM)相结合,建立深度卷积长短时记忆模型(DCNN-LSTM)学习船舶时空行为特征,并结合船舶超速、掉头、追越三种违章行为进行实验分析.结果表明,DCNN-LSTM模型相较于CNN、LSTM和支持向量机(SVM)模型表现出较强的优势,其准确率、精确率和F1分别为88.96%、96.49%和92.87%,实现了船舶违章行为的精准检测和识别.以典型水域船舶违章行为进行实例分析,进一步论证了DCNN-LSTM的有效性和优越性.为桥区水域船舶安全监管提供了可靠的理论基础,推动了船舶智能化发展.
Abstract
Accurate detection of ship violations in bridge area waters is very important for the pre-control of ship-bridge collisions.To ensure the safety of ship navigation,this paper presents a detection model of ship violation facing bridge area waters.AIS data of continuous bridge area in the Wuhan section of the Yangtze River is real-time collected and preprocessed,and the Convolutional Neural Network(CNN)with powerful feature learning ability is used to extract ship behavior information.And combined with the Long Short Term Memory(LSTM),a deep CNN-LSTM is established to learn the spatiotemporal behavior characteristics of ships,and the experimental analysis is carried out based on three kinds of illegal behaviors on ship overspeed,turning around,and overtaking.The results show that the DCNN-LSTM model proposed has a strong advantage over the CNN,LSTM,and Support Vector Machine(SVM)models,and its accuracy rate,precision rate,and F1 are 88.96%,96.49%,and 92.87%,respectively,realizing the accurate identification of ship violation.The validity and superiority of DCNN-LSTM are further demonstrated by analyzing the violation of ships in typical waters.The research results provide a reliable theoretical basis for ship safety supervision in bridge waters and promote the development of ship intelligence.
关键词
深度学习/内河航道/CNN/LSTM/DCNN-LSTMKey words
deep learning/inland waterways/CNN/LSTM/DCNN-LSTM引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据