A Multi-Task Network With Dynamic Segmentation for Sea Ice Classification in Arctic Shipping Route Optimization

Ice floes and icebergs pose serious risks to ship navigation in the Arctic. It is important to identify the sea ice distribution around ships, and subsequently generate a sea ice map and optimize an efficient and safe navigation route. Existing sea ice classification methods that use the shipboard camera images have low accuracy for the distal ice field. This study establishes a multi-task sea ice classification method with dynamic segmentation, which can extract unique features of different regions and intra-region feature correlation to improve the overall classification accuracy. First, a segmentation network based on U-Net is established to divide the image into two parts: an upper part containing the sky and the distal ice field, and a lower part containing the proximal ice field. Second, a ResU-Net is utilized in each part to learn local features, and a multi-task network for sea ice classification is built to improve the feature extraction quality, especially in the distal region. Meanwhile, the spatial attention and channel attention modules are introduced to eliminate the least significant features and enhance the learning efficiency in the feature aggregation regions. Last, the case analysis and performance comparison are carried out using actual data collected by the Chinese icebreaker Xuelong. The results show that the classification accuracy, weighted mean dice coefficient, F1-score and mean intersection over union of the proposed model are 94.7%, 0.937, 0.947 and 0.904, respectively, which outperform the frequently-used models such as PSPNet and Deeplab v3+ while using a significantly lower number of parameters.