SSRLM: A self-supervised representation learning method for identifying one ship with multi-MMSI codes

Abstract

It is of great practical significance for maritime safety and shipping development to achieve “One Ship with One Maritime Mobile Service Identity (MMSI) Code” in accordance with regulations. However, the current “One Ship with Multiple MMSI Codes” violation identification method lacks practicability and generalization. Aiming at the these problems, this paper proposes a self-supervised representation learning model for the “One Ship with Multiple MMSI Codes” abnormal behavior recognition task, named SSRLM. Firstly, we construct a new dataset about “One Ship with Multiple MMSI Codes”, named HN_MulMI. Secondly, the SSRLM model learns the contextual interdependencies among different trajectory features in the HN_MulMI dataset through an unsupervised pre-training scheme, and extracts a dense vectorial representation of the ship’s motion trajectories. Finally, by using the unique characteristics of the “One Ship with Multiple MMSI Codes” trajectory sequence, the SSRLM model captures its feature dependencies through a fine-tuning scheme to accomplish the abnormal behavior detection task. Experimental results in the two scenarios of HN_MulMI test set demonstrate that the proposed model outperform the state-of-the-art models in recent years, the average precision, recall and F1-score rates is up to about 99.26% and 93.5%. Meanwhile, we also conducted comparative experiments on three public datasets to verify the generalization performance, achieving 90.31%, 93.2% and 95.14% results in the F1 score evaluation indicators, further confirming the good recognition performance of the SSLLM model. Finally, our model has been applied to the actual scene and achieved good results.