Maritime Surveillance

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12 papers3 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

HullProbe: Measuring Interventional Part Reliance in Maritime Vessel Classifiers
Oct 18, 2026Faisal Mohammad, Mohamed YounisImage Classification Feature Attribution
University of Maryland, Baltimore County · Johns Hopkins University Applied Physics Laboratory
Multi-Modal Graph Neural Network-Inspired Vessel Identification and Tracking for UAV-Based Maritime Surveillance
Oct 9, 2026Chenjie Zhao, Jiangtian Nie, Jiawen Kang +2Maritime Surveillance Multi-Object Tracking
Sanya Science and Education Innovation Park of Wuhan University of Technology, Sanya 572025, China · School of Navigation, Wuhan University of Technology, Wuhan 430063, China · School of Natural and Computing Sciences, University of Aberdeen, Aberdeen AB24 3UE, U.K. +2
AI-Based On-Board Maritime Object Detection for Earth Observation Payload Data Reduction on Versal Embedded Hardware
Oct 8, 2026Thomas Goudemant, Aurélien Bobey, Omar Hlimi +1Remote Sensing Maritime Surveillance
IRT Saint Exupéry
MariSat: A Maritime Dataset for Instance Segmentation of Objects in Satellite and Aerial Images
Aug 30, 2026Amir Abbes, Ines Harrabi, Lucas Justin Yirepoa Kinda +3Instance Segmentation Maritime Surveillance
University of Carthage, SUP’COM, LR11TIC05, MEDIATRON, 2083, Ariana, Tunisia · University of Gabes, Hatem Bettaher IResCoMath Laboratory, Gabes, Tunisia · University of Rouen Normandie, ESIGELEC, IRSEEM, 76000 Rouen, France
CRHT: A Continuous Regression Hybrid Transformer for Vessel Trajectory Prediction with Online Cluster Sampling
Aug 10, 2026Alexander Schiøtz, Bertram Hage, Christian Rand +2Hybrid CNN-Transformer Architectures Maritime Surveillance
Technical University of Denmark Anker Engelunds Vej 101 2800 Kongens Lyngby, Denmark
Deep Learning based Detection of Fishing Vessels and Fishing Monitoring using Nightlight Images
Aug 10, 2026Shantakar Mohanty, Prasun Kumar Gupta, Raian Vargas MarettoRemote Sensing Image Understanding Maritime Surveillance
Indian Institute of Remote Sensing, ISRO, Dehradun, India · University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC), Enschede, The Netherlands
Reinforcement Learning for Heterogeneous Sensor Selection in Maritime Surveillance
Jul 3, 2026Andrei Starodubov, Yaqub Aris Prabowo, Andreas Hadjipieris +2Maritime Surveillance Information Gain
Cyprus Marine and Maritime Institute, Larnaca, Cyprus · Technical University of Denmark, Lyngby, Denmark
Bridging the Gap Between Image Restoration and Navigational Safety in Hazy Conditions: A New Visibility Estimation Metric for Maritime Surveillance
Jun 29, 2026Wentao Feng, Guobei Peng, Wengang Mao +1Maritime Surveillance Image Quality Assessment
Sanya Science and Education Innovation Park, Wuhan University of Technology, Sanya 572000, China · School of Navigation, Wuhan University of Technology, Wuhan 430063, China · State Key Laboratory of Maritime Technology and Safety, Wuhan University of Technology, Wuhan 430063, China +1
Sea-Scan: High-Accuracy, ML-based Dark Vessel Detection and Localisation via Weakly Supervised DAS Monitoring
Jun 19, 2026Tian Tian, Agastya Raj, Lara Flanagan +2Distributed Acoustic Sensing Object Detection
School of Computer Science and Statistics, IRIS Research Group, ADAPT Research Centre, Trinity College Dublin, Ireland · School of Engineering, ADAPT Research Centre, Trinity College Dublin, Ireland
HARBOR: Heading Analysis and Reconstruction from Behavioral Observation and Radar
Jun 12, 2026Joao P. A. Dantas, Paulo F. Silva Filho, Jelton A. Cunha +1Remote Sensing Image Understanding Maritime Surveillance
Institute for Advanced Studies, São José dos Campos, São Paulo, Brazil
DarkVesselNet: Multi-Modal Remote Sensing and Trajectory Reasoning for Dark Vessel Detection
May 30, 2026Arun SharmaMultimodal Sensor Fusion Anomaly Detection
University of Minnesota, Twin Cities
Adaptive Entropy-Driven Sensor Selection in a Camera-LiDAR Particle Filter for Single-Vessel Tracking
Mar 9, 2026Andrei Starodubov, Yaqub Aris Prabowo, Andreas Hadjipieris +2Particle Filtering Maritime Surveillance
Cyprus Marine and Maritime Institute, Larnaca, Cyprus · Technical University of Denmark, Lyngby, Denmark

Oct 18, 2026J/K move · Enter open · S save

HullProbe: Measuring Interventional Part Reliance in Maritime Vessel Classifiers

Faisal Mohammad, Mohamed Younis

University of Maryland, Baltimore County · Johns Hopkins University Applied Physics Laboratory

Image Classification Feature Attribution Shortcut Learning Fine-Grained Image Classification Maritime Surveillance

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Recognizing a vessel's type from an image supports maritime surveillance and sanctions monitoring, but a classifier can reach high accuracy by leaning on the generic hull instead of the more distinctive superstructure, and is then fooled when a vessel is re-hulled or occluded. De-biasing methods claim to remove such reliance, yet the field certifies them with worst-group accuracy, a proxy that needs group labels and only infers reliance from accuracy. We measure the reliance directly. HullProbe swaps a registered anatomical part of a vessel for another instance's and reads how much that part drives the decision, using a same-class swap to control for compositing artifacts. Because a surface vessel is a hull and a superstructure arranged along one axis, its parts form a 2x3 grid whose 2^6 = 64 swaps we evaluate exhaustively, and a single Walsh transform returns each part's interventional influence and its interaction spectrum. On planted shortcuts, where the true reliance is known, the measure tracks worst-group accuracy (r = -0.94 over 45 configurations), and replacing its learned segmenter with human annotations changes the hull-share by less than the audit's resolution. On the 26 vessel types of MARVEL, the classifier places about 46% of its corrected sensitivity on the generic hull yet follows the superstructure when the two conflict, and the group-label-free de-biasers available there do not detectably reduce the hull's share. The findings hold across three architectures and a second maritime dataset, and the method extends to fine-grained vehicle recognition on Stanford Cars.