Breaking The Ice: Video Segmentation for Close-Range Ice-Covered Waters

TL;DR

This paper presents UPerFlow, a novel video segmentation model that improves ice condition assessment in Arctic waters using ship-borne optical data, aiding safer navigation amid rapidly changing ice conditions.

Contribution

It introduces a new multi-channel architecture with enhanced flow and segmentation modules, significantly outperforming existing models in challenging Arctic environments.

Findings

UPerFlow outperforms baseline models by 38% in occluded regions.

The model demonstrates robustness in challenging Arctic conditions.

A new annotated dataset of 946 images supports future research.

Abstract

Rapid ice recession in the Arctic Ocean, with predictions of ice-free summers by 2060, opens new maritime routes but requires reliable navigation solutions. Current approaches rely heavily on subjective expert judgment, underscoring the need for automated, data-driven solutions. This study leverages machine learning to assess ice conditions using ship-borne optical data, introducing a finely annotated dataset of 946 images, and a semi-manual, region-based annotation technique. The proposed video segmentation model, UPerFlow, advances the SegFlow architecture by incorporating a six-channel ResNet encoder, two UPerNet-based segmentation decoders for each image, PWCNet as the optical flow encoder, and cross-connections that integrate bi-directional flow features without loss of latent information. The proposed architecture outperforms baseline image segmentation networks by an average 38% in occluded regions, demonstrating the robustness of video segmentation in addressing challenging Arctic conditions.