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arXiv · 2411.05225

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

Corwin Grant Jeon MacMillan·K. Andrea Scott·Matthew Garvin·Zhao Pan
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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.

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Corwin Grant Jeon MacMillan, K. Andrea Scott, Matthew Garvin, Zhao Pan. 2024-11-07. Breaking The Ice: Video Segmentation for Close-Range Ice-Covered Waters. https://arxiv.org/abs/2411.05225

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