Ship trajectory anomaly detection method based on encoder-decoder architecture composed of Transformer_LSTM modules
[Objective]In order to improve the accuracy and efficiency of ship trajectory anomaly detection,and solve the problems of traditional anomaly detection methods such as limited feature characterization abil-ity,insufficient compensation accuracy,gradient disappearance and overfitting,an unsupervised ship traject-ory anomaly detection method based on the Transformer_LSTM codec module is proposed.[Method]Based on the encoder decoder architecture,the Transformer_LSTM module replaces the traditional neural network to achieve track feature extraction and track reconstruction.By embedding the transformer into the recursive mechanism of LSTM,combined with the cyclic unit and attention mechanism,self-attention and cross-attention can be used to calculate the state vector of the cyclic unit and effectively construct the long sequence model.By minimizing the difference between the reconstructed output and original input,the model learns the characteristics and motion mode of the general trajectory,and trajectories with a reconstruction error greater than the abnormal threshold are judged as abnormal trajectories.[Results]AIS data collected in January 2021 is adopted.The results show that the accuracy,precesion and recall rate of the model are significantly im-proved compared with those of LOF,DBSCAN,VAE,LSTM,etc.The F1 score is improved by 8.11%com-pared with that of the VAE_LSTM model.[Conclusion]The anomaly detection performance of the pro-posed method is significantly superior to the traditional algorithm in various indexes,and the model can be ef-fectively and reliably applied to the trajectory anomaly detection of ships at sea.
万方数据
维普
