Breaking The Ice: Video Segmentation for Close-Range Ice-Covered Waters
It addresses the need for reliable navigation solutions in ice-covered waters, which is an incremental improvement over existing methods.
This study tackled the problem of automated ice condition assessment for Arctic navigation by developing a video segmentation model called UPerFlow, which outperformed baseline image segmentation networks by an average 38% in occluded regions.
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.