A Comparative Study on Ocean Front Detection in the Northwestern Pacific Using U-Net and Mask R-CNN

Ocean fronts play a vital role in modulating climate variability, driving material transport, and maintaining the stability of marine ecosystems. Therefore, accurate identification of ocean fronts is of great significance for marine environmental monitoring and resource management. This study focuses on the Northwestern Pacific region and conducts a systematic comparison between two representative deep learning models—U-Net and Mask R-CNN—for automated ocean front detection. The objective is to evaluate the adaptability and strengths of different network architectures in handling multi-scale features, complex background conditions, and boundary delineation, thereby providing a theoretical basis for model selection and application-specific deployment. Experimental results show that U-Net achieves superior spatial consistency in large-scale frontal segmentation, with an IoU of 0.81 and a Dice coefficient of 0.76, while maintaining relatively high computational efficiency. In contrast, Mask R-CNN demonstrates stronger boundary modeling capabilities in detecting small-scale fronts and handling heterogeneous backgrounds, achieving an IoU of 0.78 and a Dice score of 0.73, though at the cost of increased computational demand. Overall, U-Net is more suitable for broad-scale automatic detection of ocean fronts, whereas Mask R-CNN exhibits greater potential in complex scene recognition. Integrating the structural advantages of both models holds promise for further enhancing the stability and accuracy of frontal detection, thereby offering robust technical support for ocean remote sensing analysis and environmental forecasting.