An attention-enhanced deep learning model for detecting vessel anomalous behavior

Intelligent surveillance is critical for ensuring the safety and efficiency of maritime traffic, especially in busy waterways where the volume of vessel traffic presents substantial monitoring challenges. To effectively detect vessel anomalies and minimize potential risks, we propose a novel deep learning model called Seq2Seq-Attention for Vessel Anomalous Behavior Detection (SA-VABD). Specifically, the speed-aware Douglas-Peucker algorithm is employed to compress trajectory data to reduce data complexity and improve processing efficiency. This compression allows us to extract high-level attributes of vessel trajectories more effectively. Building on these attributes, we present a seq2seq-driven model designed to capture both static and dynamic characteristics of vessels. To further improve the model’s detection capability, an attention mechanism is embedded into the Seq2Seq network. This allows the model to …

Recommended citation: C Xu, S Zhou, M Liang, Y Zhang, M Zhang, RW Liu. (2026). An attention-enhanced deep learning model for detecting vessel anomalous behavior. Ocean Engineering, 343, 123239. https://www.sciencedirect.com/science/article/pii/S0029801825029221