Ship Violation Behavior Detection Based on DCNN-LSTM Model
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.
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