SAR Vessel Detection and Gross Tonnage Estimation from Heterogeneous Datasets for Dark Vessel Identification
2026-07-20 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors developed a method to find ships in radar images, even when the ships turn off their tracking devices to hide. Their approach uses deep learning to detect ships, figure out their type and size, and then estimate how heavy they are. They do this by combining different types of data and a special technique that compares ship features to predict weight. Their method works well compared to other models and helps identify ships that might be trying to avoid being seen. The authors also shared their code publicly for others to use.
Deep LearningSynthetic Aperture Radar (SAR)Vessel DetectionGross Tonnage (GT)K-Nearest Neighbors (KNN)Multi-task LearningMaritime SecurityTransponderImage RegressionTabular Data
Authors
Davide Paltrinieri, Andrea Diecidue, Roberto Basla, Daniele Casciani, Piero Fraternali, Giacomo Boracchi
Abstract
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 end-to-end DL model for vessel detection and GT regression. In this work, we present a framework that leverages heterogeneous image and tabular datasets to solve this task. Our solution combines a multi-task DL framework for predicting the location, vessel type, and physical dimensions of ships, cascaded with a non-parametric model for predicting GT from vessel size and category. We perform GT regression by a KNN that measures sample similarity using a hybrid Euclidean and categorical distance. Experiments show that our solution can predict multiple outputs while remaining competitive with state-of-the-art models on individual subtasks, thus enabling the identification of dark vessels. We publish our code on GitHub https://github.com/PaltrinieriDavide/vesseldetection.