Insights into deep learning-based vessel detection and characterization using SAR and AIS data

As part of the Embed2Scale project funded by the Horizon Europe programme, this study presents a deep learning-based approach to vessel detection and characterisation in support of Maritime Domain Awareness (MDA). To address the limitations of the Automatic Identification System (AIS), which is not mandatory for all vessels and may be intentionally deactivated, Sentinel-1 Synthetic Aperture Radar (SAR) imagery was combined with AIS records to generate a large-scale dataset. The dataset comprises 2,008 SAR images collected from twelve major United States ports between 2020 and 2024, yielding 23,121 vessels and over 22,770 image batches of 500 × 500 pixels. The You Only Look Once version 8 (YOLOv8) architecture was employed for vessel detection and classification. Several model variants from nano to extra-large, were trained and compared using pre-trained weights being their performance evaluated on a validation set representing 20% of the batches. The main evaluation metric was the mean Average Precision at IoU = 0.5 (mAP@50), being the medium variant model which offered the best trade-off between accuracy and computational cost. Larger models provided only marginal gains and required more training time. Nevertheless, challenges remain in detecting classes of vessels unrepresented in the dataset, what highlights the need for strategies to address data imbalance.