Integrating Attention Mechanisms in Graph Neural Networks for Marine Oil Spill Detection

The increasing frequency of offshore engineering activities, particularly the expansion of offshore oil transport and the rise in the number of oil platforms, has greatly increased the potential risk of marine oil spill incidents. Historically, several large oil spills have had long-term adverse effects on marine ecosystems and economic development, highlighting the importance of accurately delineating and monitoring oil spill areas. In this study, graph neural network technology is introduced to implement semantic segmentation of SAR images, and two graph neural network models based on Graph-FCN and Graph-DeepLabV3+ with the introduction of an attention mechanism are constructed and evaluated to improve the accuracy and efficiency of oil spill detection. By comparing the Swin-Unet model, the Graph-DeepLabV3+ model performs better in complex scenarios, especially in edge detail recognition. This not only provides strong technical support for marine oil spill monitoring but also provides an effective solution to deal with the potential risks brought by the increase of marine engineering activities, which is of great practical significance as it helps to safeguard the health and sustainable development of marine ecosystems and reduce the economic losses.