  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 legislative carbon reduction targets. Our work focuses
on utilizing sea images in operational envelopes captured by a single
stationary camera mounted on the ship bridge. The collected images are used to
train a deep learning model to automatically recognize the state of the sea
based on the Beaufort scale. To recognize the sea state, we used 4
state-of-the-art deep 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 the transfer learning
approach to fine-tune the models on our dataset. The obtained results
demonstrate the 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 sea state classification models to address recognized
gaps in maritime research and enable safer and more efficient maritime
operations.
