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 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.