湖南大学学报（自然科学版）2024，Vol.51Issue(12)：119-128,10.DOI:10.16339/j.cnki.hdxbzkb.2024289
基于DCNN-LSTM模型的船舶违章行为检测
Ship Violation Behavior Detection Based on DCNN-LSTM Model
摘要
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分类
交通工程引用本文复制引用
郑元洲,李鑫,钱龙,秦瑞朋,李果,李梦希..基于DCNN-LSTM模型的船舶违章行为检测[J].湖南大学学报（自然科学版）,2024,51(12):119-128,10.基金项目
国家自然科学基金资助项目(52171350),National Natural Science Foundation of China(52171350). （52171350）
