A Computer Vision Approach to Estimate the Localized Sea State

This research presents a novel application of computer vision (CV) and deep learning methods for real-time sea state recognition aiming to contribute to improving the operational safety and energy efficiency of seagoing vessels, key factors in meeting the International Maritime Organization's carbon reduction targets. In particular, our work focuses on utilizing sea images in operational envelope captured by a single stationary camera mounted on the ship bridge, which are used to train deep learning algorithms for automatic sea state estimation based on the Beaufort scale. To recognize the sea state, we used 4 state-of-the-art neural networks with different characteristics that proved useful in various computer vision tasks: Resnet-101, NASNet, MobileNet_v2 and Transformer Vit-b32. Furthermore, we have defined a unique large-scale dataset, collected over a broad range of sea conditions from an ocean-going vessel prepared for machine learning. We used transfer learning approach to fine-tune the models on our dataset. The obtained results suggest promising potential for this approach to complement traditional methods, particularly where in-situ measurements are unfeasible or interpolated weather buoy data is insufficiently accurate. This study sets the groundwork for further development of machine learning-based sea state classification models to address recognized gaps in maritime research and enable safer and more efficient maritime operations.