SAR Vessel Detection and Gross Tonnage Estimation from Heterogeneous Datasets for Dark Vessel Identification
Detecting vessels engaging in illegal activities is of paramount importance for maritime security. One of the major goals is to detect dark vessels, ships that disable their transponders to evade surveillance. Deep Learning (DL) models can detect vessels in Synthetic Aperture Radar (SAR) images, enabling maritime traffic analysis regardless of weather or visibility conditions. However, to detect potential dark vessels, a DL model must select only those that are required to carry a transponder based on their Gross Tonnage (GT). Unfortunately, no public SAR dataset is available for training an e
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- FuzzySimilar title/name (fuzzy) · 84%huggingface/datasets →
“Fuzzy title match (0.92): “SAR Vessel Detection and Gross Tonnage Estimation from Heter” ≈ “huggingface/datasets””
- LinkedLinked via arxiv author · 85%Davide Paltrinieri →
“SAR Vessel Detection and Gross Tonnage Estimation from Heterogeneous Datasets for Dark Vessel Identification”
- LinkedLinked via arxiv author · 85%Andrea Diecidue →
“SAR Vessel Detection and Gross Tonnage Estimation from Heterogeneous Datasets for Dark Vessel Identification”
- LinkedLinked via arxiv author · 85%Roberto Basla →
“SAR Vessel Detection and Gross Tonnage Estimation from Heterogeneous Datasets for Dark Vessel Identification”
- LinkedLinked via arxiv author · 85%Daniele Casciani →
“SAR Vessel Detection and Gross Tonnage Estimation from Heterogeneous Datasets for Dark Vessel Identification”