Abstract
Ocean internal waves and eddies are typical mesoscale and submesoscale processes that play critical roles in ocean energy transfer and material transport. Their complex morphology and varying background conditions make detection from Synthetic Aperture Radar (SAR) imagery challenging. In this paper, we propose a novel multi-scale YOLO framework, termed MuLO, which designed to investigate the intrinsic connections among multi-scale ocean dynamic processes and to enable collaborative detection of oceanic eddies and internal waves in SAR imagery. This model integrates a Dynamic Grouping Cascade Module (DGCM) and a Multi-Scale Contextual Aggregation (MSCA) mechanism to enhance weak feature extraction and multi-scale feature fusion. A multi-source spaceborne SAR dataset including X-, C-, and L-band imagery acquired in the South China Sea was constructed, and a multi scale slicing inference strategy was designed for practical analysis. Experiments show that MuLO, with only 2.41M parameters, achieves 90.8% mAP50, outperforming several state-of-the-art models. Multi-source evaluation further demonstrates robust detection across radar bands, with a mean average precision (mAP) of 87.8% in simultaneous eddy and internal wave identification. These results highlight the potential of MuLO for operational monitoring of ocean dynamic processes by spaceborne SAR.
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