用视频分割技术自动识别极地近海冰情，提升航行安全
Breaking The Ice: Video Segmentation for Close-Range Ice-Covered Waters
- 基于船载光学影像，设计双分支视频分割模型
- 在遮挡区域平均提升38%精度，优于传统图像分割
- 适合极地航行导航与自动化冰况评估场景
北极海冰快速消退，预计2060年夏季将无冰，催生新航道但亟需可靠导航方案。现有方法高度依赖主观专家判断，亟需数据驱动的自动化手段。本研究利用机器学习分析船载光学数据，构建了包含946张图像的精细标注数据集，并提出半自动区域标注方法。所提视频分割模型UPerFlow在SegFlow基础上改进：采用六通道ResNet编码器、两个基于UPerNet的分割解码器（每帧各一）、PWCNet作为光流编码器，以及跨连接结构，在不丢失潜在信息前提下融合双向光流特征。该架构在遮挡区域平均比基线图像分割网络提升38%，验证了视频分割在复杂极地条件下的鲁棒性。
原文摘要 · Abstract (English)
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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