SAR Vessel Detection and Gross Tonnage Estimation from Heterogeneous Datasets for Dark Vessel Identification

• URL: http://arxiv.org/abs/2607.18051v1
• Date: Mon, 20 Jul 2026 15:23:18 GMT
• Title: SAR Vessel Detection and Gross Tonnage Estimation from Heterogeneous Datasets for Dark Vessel Identification
• Abstract summary: We present a framework for training an end-to-end Deep Learning model for vessel detection and GT regression.<n>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.<n> Experiments show that our solution can predict multiple outputs while remaining competitive with state-of-the-art models on individual subtasks.
• Score: 8.506766443396224
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• OSSDD - a New Open Dataset for Sentinel-1 Ship Detection [2.6346032678787807]
  OpenSARShip-Ship Detection dataset (OSSDD) is a new dataset based on the well-known OpenSARShip 1.0 dataset.<n>It contains 15,197 Sentinel-1 amplitude patches in VV and VH polarization, binary ship masks, axis-aligned bounding box and rotated bounding box annotations for a total of 55,759 ships.<n>The construction of the dataset, the contents and structure of the downloadable data and experiments with three common detector models are shown and discussed.
  arXiv  Detail & Related papers  (2026-08-03T09:31:49Z)
• HARBOR: Heading Analysis and Reconstruction from Behavioral Observation and Radar [0.29165586612027233]
  This work introduces a complete pipeline for transforming a single SAR image into predictive motion information.<n> AIS data are used exclusively during an offline calibration phase to derive vessel-type-dependent motion parameters.<n>A case study using real COSMO-SkyMed SAR imagery demonstrates the pipeline's ability to extract motion tendencies.
  arXiv  Detail & Related papers  (2026-06-12T00:57:26Z)
• WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects [68.44670476527155]
  WUTDet is a large-scale ship detection dataset with 100,576 images and 381,378 annotated ship instances.<n>We systematically evaluate 20 baseline models from three mainstream detection architectures, namely CNN, Transformer, and Mamba.<n>Results on Ship-GEN show that models trained on WUTDet exhibit stronger generalization under different data distributions.
  arXiv  Detail & Related papers  (2026-04-09T03:28:05Z)
• Predicting Barge Tow Size on Inland Waterways Using Vessel Trajectory Derived Features: Proof of Concept [0.0]
  This study introduces a novel method to use Automatic Identification System (AIS) vessel tracking data to predict the number of barges in tow using Machine Learning (ML)<n>To train and test the model, barge instances were annotated from satellite scenes across the Lower Mississippi River.<n>The proposed approach provides a scalable, readily implementable method for enhancing Maritime Domain Awareness (MDA) with strong potential applications in lock scheduling, port management, and freight planning.
  arXiv  Detail & Related papers  (2025-10-28T01:51:23Z)
• Neptune-X: Active X-to-Maritime Generation for Universal Maritime Object Detection [54.1960918379255]
  Neptune-X is a data-centric generative-selection framework for maritime object detection.<n>X-to-Maritime is a multi-modality-conditioned generative model that synthesizes diverse and realistic maritime scenes.<n>Our approach sets a new benchmark in maritime scene synthesis, significantly improving detection accuracy.
  arXiv  Detail & Related papers  (2025-09-25T04:59:02Z)
• Lightweight CNNs for Embedded SAR Ship Target Detection and Classification [0.0]
  On-board processing to generate higher-level products could reduce the data volume that needs to be downlinked.<n>This work proposes and evaluates neural networks designed for real-time inference on unfocused SAR data.
  arXiv  Detail & Related papers  (2025-08-14T14:55:19Z)
• CBW: Towards Dataset Ownership Verification for Speaker Verification via Clustering-based Backdoor Watermarking [85.68235482145091]
  Large-scale speech datasets have become valuable intellectual property.<n>We propose a novel dataset ownership verification method.<n>Our approach introduces a clustering-based backdoor watermark (CBW)<n>We conduct extensive experiments on benchmark datasets, verifying the effectiveness and robustness of our method against potential adaptive attacks.
  arXiv  Detail & Related papers  (2025-03-02T02:02:57Z)
• Lazy Layers to Make Fine-Tuned Diffusion Models More Traceable [70.77600345240867]
  A novel arbitrary-in-arbitrary-out (AIAO) strategy makes watermarks resilient to fine-tuning-based removal. Unlike the existing methods of designing a backdoor for the input/output space of diffusion models, in our method, we propose to embed the backdoor into the feature space of sampled subpaths. Our empirical studies on the MS-COCO, AFHQ, LSUN, CUB-200, and DreamBooth datasets confirm the robustness of AIAO.
  arXiv  Detail & Related papers  (2024-05-01T12:03:39Z)
• LiDAR View Synthesis for Robust Vehicle Navigation Without Expert Labels [50.40632021583213]
  We propose synthesizing additional LiDAR point clouds from novel viewpoints without physically driving at dangerous positions. We train a deep learning model, which takes a LiDAR scan as input and predicts the future trajectory as output. A waypoint controller is then applied to this predicted trajectory to determine the throttle and steering labels of the ego-vehicle.
  arXiv  Detail & Related papers  (2023-08-02T20:46:43Z)
• Anomalous NO2 emitting ship detection with TROPOMI satellite data and machine learning [0.08602553195689512]
  Starting from 2021, more demanding $textNO_text2$ emission restrictions were introduced for ships operating in the North and Baltic Sea waters. In this study, we present a method for the automated selection of potentially non-compliant ships using a combination of machine learning models on TROPOMI satellite data.
  arXiv  Detail & Related papers  (2023-02-24T16:54:47Z)
• DAE : Discriminatory Auto-Encoder for multivariate time-series anomaly detection in air transportation [68.8204255655161]
  We propose a novel anomaly detection model called Discriminatory Auto-Encoder (DAE) It uses the baseline of a regular LSTM-based auto-encoder but with several decoders, each getting data of a specific flight phase. Results show that the DAE achieves better results in both accuracy and speed of detection.
  arXiv  Detail & Related papers  (2021-09-08T14:07:55Z)
• DAAIN: Detection of Anomalous and Adversarial Input using Normalizing Flows [52.31831255787147]
  We introduce a novel technique, DAAIN, to detect out-of-distribution (OOD) inputs and adversarial attacks (AA) Our approach monitors the inner workings of a neural network and learns a density estimator of the activation distribution. Our model can be trained on a single GPU making it compute efficient and deployable without requiring specialized accelerators.
  arXiv  Detail & Related papers  (2021-05-30T22:07:13Z)
• ABOShips -- An Inshore and Offshore Maritime Vessel Detection Dataset with Precise Annotations [0.17205106391379021]
  Maritime vessel detection of inshore and offshore datasets is no exception. We collected a dataset of images of maritime vessels taking into account different factors. Vessel instances (including 9 types of vessels), seamarks and miscellaneous floaters were precisely annotated. We evaluated the the out-of-the-box performance of four prevalent object detection algorithms.
  arXiv  Detail & Related papers  (2021-02-11T07:05:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.