Class-Guided Network with Rare-Class Amplification for Sea State Estimation Based on Ship Motion Data

Abstract

Accurate, real-time Sea State Estimation (SSE) is crucial for the safety and operational efficiency of Autonomous Surface Vessels (ASVs). However, existing deep learning methods for this task commonly face three major challenges: the inherent class imbalance of marine environments, the ambiguous boundaries between discrete sea state levels, and the difficulty of extracting multi-scale temporal features from vessel motion. To address these challenges, this paper proposes a novel framework named the Class-guided Rare-boosted Multi-Scale Net (CRUISE). The framework is built upon a multi-scale encoder-decoder architecture and integrates two key innovations: a Rare-Boosted Class Embedding (RBCE) module at the network's bottleneck and a class-guided decoding mechanism. The RBCE module first generates a preliminary class prediction and then dynamically enhances the representation of rare sea state classes to create a class-balanced conditional vector. This vector subsequently provides top-down guidance to the decoder, injecting class-aware information by modulating the feature reconstruction process. This synergistic design fundamentally addresses the data imbalance problem at the feature level and effectively sharpens the decision boundaries between easily confused transitional sea states. Extensive experiments on multiple public benchmarks, simulated ship motion, and real-world datasets demonstrate that CRUISE significantly outperforms existing state-of-the-art methods, showing a pronounced advantage in improving the recognition accuracy of rare and high-risk sea states. Furthermore, real-time inference tests on a physical model vessel validate the model's performance on edge computing devices, further confirming its feasibility and robustness for deployment in real-world marine environments.