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

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Latest papers 28

Sep 30, 2026cs.RO

Active Mapping of Underwater Litter Using Camera-Sonar Fusion

Marine litter is a growing threat to the underwater ecosystem, driving demand for autonomous survey methods that can locate debris efficiently over large areas. Existing survey methods typically follow predefined paths or operate with a single sensing modality, typically a camera (with image quality suffering in poor-visibility conditions) or sonar (usually noisy and low-resolution). We present an active mapping framework in which a forward-looking sonar and a camera both feed into a shared Bayesian occupancy map, and an optimization problem is solved at each step to decide on the next best view. Candidate viewpoints are scored by a two-term utility that balances exploration of uncertain regions via voxel entropy against exploitation of likely objects. Each sensor is characterized by range- and bearing-dependent detection and false-alarm probability tables determined from data. We evaluate the approach in a realistic underwater simulator, demonstrating that active mapping finds objects faster than a lawnmower coverage pattern, and that the dual-sensor approach works better than using either of the individual sensors.

David Rete, Patrick Boros, Lucian Busoniu

Department of Automation Technical University of Cluj-Napoca Romania

Forward-Looking Sonar Underwater Coral

Sep 29, 2026cs.RO

Multifunctional Locomotion Control of Multi-Jointed BURs with Swimming and gait Capabilities

Multimodal biomimetic underwater robots (BURs) can conduct underwater tasks suitable for the environment. Combining the characteristics of aquatic organisms enables the swimming and leggedlocomotion required for underwater exploration. Locomotion control mechanism relies on rule-based behavior selection and the designer's discretion. This limits the robot's ability to acquire new behavioral capabilities to the predetermined range of behaviors. To address these challenges, we propose a mechanism and control system that enables the expression of multimodal locomotion capabilities from the same multi-jointed structure. A mechanism equipped with four leg-fins each having four axes is used. This controller achieves nonlinear behavior based on sensor modalities, rather than relying on predefined conditional rule-based on locomotion functions. This system was validated through both physical and simulation testing based on multiple sensor data and behavioral patterns. Utilizing a potential function in multimodal locomotion control was verified to enable transitions between two or three behaviors. Implementing the control method as a multimodal controller is expected to enhance its application in underwater exploration. Our project page is at https://tasada038.github.io/multi-jointed-bur/.

Takumi Asada, Hideo Furuhashi, Kenta Tabata +2

Utsunomiya University, 7-1-2 Yoto, Utsunomiya, Tochigi, 321-8585, Japan · Aichi Institute of Technology, 1247 Yachigusa, Yakusa, Toyota, Aichi, 470-0392, Japan

Underwater Robots Agile Locomotion Underwater Gait Dynamics

Sep 29, 2026cs.CV

VesselBench-800K: A Large-scale Perception Benchmark for Multimodal Vessel Detection, Counting, and Density Estimation

Vessel perception from space is crucial for a wide range of maritime applications, from traffic monitoring to environmental protection. However, most existing datasets predominantly focus on general object detection tasks in optical remote sensing (RS) images. Relying solely on single-modality optical RS images proves inadequate for effectively perceiving vessel objects in complex maritime scenarios, where ever-changing weather conditions (e.g., clouds and rain), the need for day-and-night coverage, and the inherent limitations of a single imaging modality pose significant challenges. To fill this gap, we introduce VesselBench-800K, the largest-to-date benchmark dataset on a global scale for vessel perception in multimodal RS images. As its name suggests, VesselBench-800K comprises 800,000 images, each at a resolution of 512x512 pixels, specifically curated for vessel perception tasks such as detection, counting, and density estimation. These multimodal image pairs (i.e., optical, SAR) are collected from diverse platforms, sensors, scenes, shooting heights, and synthetic sources, spanning spatial resolutions from 4.5m to 0.1m. Furthermore, we evaluate numerous state-of-the-art detection, counting, and density estimation models on VesselBench-800K through both qualitative and quantitative comparisons. By revealing previously unrecognized cues, this dataset holds immense potential to significantly advance our understanding of marine traffic. Our VesselBench dataset will be publicly available at https://github.com/danfenghong/IEEE_TGRS_VesselBench to support and contribute to community development.

Danfeng Hong, Chenyu Li, Jocelyn Chanussot

School of Automation, Southeast University, Nanjing 211189, China · Univ. Grenoble Alpes, INRAE, LESSEM, 38000 Grenoble, France · Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, Grenoble 38000, France

Ship Detection Maritime Navigation Remote Sensing Vessel Segmentation Multimodal Dataset Underwater+1

Sep 24, 2026cs.RO

Underwater C3-JEPA: An Object-Centric Cross-View World Model for ROV Salvage

We present Underwater C

^{3}

-JEPA (cross-view, control-conditioned, context-extended), an object-centric multi-view predictive world model for near-field heavy-load underwater ROV salvage. Without contact sensors, it predicts in latent space how the task-object state evolves through contact interaction and under the hydrodynamic lag of the vehicle, from synchronized multi-view RGB observations and vehicle control signals. C

^{3}

-JEPA encodes multi-camera observations into task-object and context tokens, fuses cross-camera evidence through held-out-view attention, and directly predicts future states conditioned on control. Weak binding anchors the target and gripper at low annotation cost, while SIGReg sharpens the geometric representation. Experiments show that the learned representation transfers substantially more task-relevant information to downstream probes than a reconstruction-free latent baseline, while keeping the predictor lightweight. The resulting predictive interface supports model-predictive-control (MPC) candidate evaluation and imagined-rollout behavior-agent training. Validation on real underwater video shows the same architecture recovering a withheld camera's object state and staying ahead of persistence, so the recipe transfers beyond simulation.

Yuncong Yang, Jinlong Li, Yulong Xue +4

Underwater Engineering Institute, Shanghai Jiao Tong University, Shanghai 200240, China

Underwater Underwater Object Detection Video Joint Embedding Predictive Architecture Object-Centric Representations Cross-View Video World Models+2

Sep 20, 2026cs.RO

Towards Reliable Underwater Diver-Robot Interaction: Gesture Design, Interaction Logic, and Real-World Evaluation

Underwater human--robot interaction requires gesture commands that are both easy for divers to use and reliable for robots to recognize. We investigate these aspects through a closed-loop diver--robot interaction framework integrating a compact seven-gesture vocabulary, lightweight landmark-based recognition, and command-level interaction logic. We evaluate the framework through a user study and underwater robot experiments in a laboratory tank and a swimming pool. The user study supported the reproducibility of the gestures after brief learning. Recognition analysis further showed that visual similarity was associated with gesture confusion, while intermediate poses during gesture formation introduced temporal ambiguity. Command-level processing mitigated the effects of transient recognition errors on robot execution, reducing unintended triggers and premature task interruptions. These findings show that reliable underwater gesture interaction depends on human usability, gesture recognizability, and execution reliability in underwater interaction.

Yingqi Liu, Kanzhong Yao, Zimeng Peng +4

University of Science and Technology of China, Hefei, China. · Institute of Artificial Intelligence (TeleAI), China Telecom, Shanghai 200232, China.

Underwater Robots Underwater Human-Robot Interaction Hand Gesture Recognition Co-Speech Gesture Generation Interaction Data+2

Aug 8, 2026cs.CV

SCTD 3.0: Sonar Common Target Detection in the Wild - A Large-Scale, Multi-Scene Dataset from Real Marine Surveys

Synthetic Aperture Sonar (SAS) is core for wide-area detection of small underwater targets. However, large-scale, high-quality SAS datasets are scarce, hindering data-driven recognition. Existing benchmarks are small and limited to single scenarios, failing to reproduce complex acoustic scattering, diverse seabeds, and multi-pose imaging in real detection. To fill this gap, we introduce SCTD 3.0 - a large-scale real-measured dataset for Sonar Common Target Detection in the Wild in natural waters. It contains over 10,000 high-quality real SAS image snippets from multi-frequency systems (240 kHz, 450 kHz, and others), covering ten typical target categories across varied seabed geomorphologies, with multiple observation angles, detection ranges, and frequency bands. We establish a rigorous hierarchical annotation protocol that decouples labeling of intrinsic physical properties, deployment characteristics, and scattering phenomena - covering material, geometry, internal structure, burial state, shadow integrity, specular highlights, edge diffraction, and resonance effects. This enables fine-grained target characterization. We also construct a multi-task benchmark for object detection, fine-grained classification, and attribute prediction, evaluating mainstream deep learning models under cross-domain, cross-scene, cross-frequency, and cross-view generalization. SCTD 3.0 is expected to provide a critical data cornerstone for robust underwater target perception in open-water environments. SCTD 3.0 is available at https://github.com/automlresearch/SCTD-3.0.

Peng Zhang

Forward-Looking Sonar Underwater Acoustic Target Recognition Underwater Unsupervised Detection Fine-Grained Perception

Aug 2, 2026cs.CV

Swimm3R: Splatting with Medium-aware SfM for Underwater 3D Reconstruction

We propose Swimm3R, a unified framework that combines medium-aware structure-from-motion (SfM) with Underwater Beta Splatting to address scattering- and attenuation-induced failures in underwater 3D reconstruction. Swimm3R distills in-air geometric priors into a feed-forward backbone and uses a physics head to regress underwater image-formation parameters, camera poses, and restored point clouds. Additionally, we introduce Underwater Beta Splatting, which extends Gaussian splatting with Beta primitives and scattering-aware geometric gradients for stable underwater geometry representation. We further establish the Barbados underwater video dataset to demonstrate the effectiveness of our method in challenging underwater environments. On this dataset, Swimm3R robustly recovers underwater scene structure under challenging scattering conditions, yielding coherent seafloor geometry. Using these predicted point clouds, the proposed Underwater Beta Splatting improves average PSNR by

1.47

dB over WaterSplatting while increasing downstream localization performance by

2.0

and

2.4

percentage points in RRA@15 and RTA@15, respectively.

Minseong Kweon, Junaed Sattar

Department of Computer Science & Engineering and the Minnesota Robotics Institute (MnRI), University of Minnesota–Twin Cities, Minneapolis, MN 55455 USA

Underwater Image Enhancement Methods Underwater Object Detection Underwater Structure-From-Motion

Jul 27, 2026cs.CV

MarineEVT: Advancing Event-Centric Marine Video Understanding via Visual Tool Reasoning

Recent Vision-Language Models (VLMs) have achieved remarkable success in visual understanding, driven by the growing availability of high-quality image-text pairs. However, the performance of VLMs often degrades in the video domain due to the essential need for temporal understanding and the scarcity of large-scale annotated video data. In this work, we focus on marine video understanding, which brings further challenges: first, it requires substantial domain expertise; and video VLMs usually struggle with localizing and interpreting critical information from marine videos, as the informative events are typically sparse, unpredictable, and unevenly distributed. To address these challenges, we carefully curate the first event-centric marine video understanding dataset called MarineEVT, which features 20K multi-task, video-level visual question-answering pairs spanning multiple dimensions of marine understanding and analysis. Meanwhile, based on MarineEVT, we decompose marine video understanding as an Event-centric Visual Tool-integrated Reasoning process EVT-R1 for short, where we leverage powerful visual tools to drive the model to localize and interpret critical information aligned with visual questions and human intent. To demonstrate its effectiveness, we compare EVT-R1 against 11 SOTA VLMs in different settings. EVT-R1 outperforms the top open-source and top commercial models by 5.22 and 11.09, respectively. MarineEVT and EVT-R1 lay the foundation for ecological discovery and marine education, fostering the development of VLMs capable of interpreting marine dynamics, reasoning about ecological interactions, and supporting sustainable ocean video understanding and analysis.

Tuan-An To, Yuk-Kwan Wong, Tuan-Anh Vu +2

The Hong Kong University of Science and Technology, Hong Kong, China · University of California, Los Angeles CA, USA · University of Electronic Science and Technology of China, China

Recent Vision-Language Models Underwater Visual Reasoning Maritime Navigation

Jul 16, 2026cs.RO

Risk-Aware Belief Control Barrier Functions over Random Finite Sets

Ensuring robot safety in unknown, dynamic environments is a fundamental requirement. It involves inferring the states of an unknown and time-varying number of moving objects from noisy, incomplete measurements. We address safe control under the induced multi-object state uncertainty with a risk-aware belief control barrier function (BCBF) framework. The uncertainty is captured by a random finite set (RFS) belief, estimated by a sequential Monte Carlo probability hypothesis density (SMC-PHD) filter that represents it with a set of particles. Building directly on these particles, we construct a nonsmooth BCBF, establish forward invariance of the safe set under continuous prediction, and derive an explicit condition under which discrete updates preserve safety. Simulation and real-world underwater experiments demonstrate the effectiveness and efficiency of the proposed approach.

Shaohang Han, Gang Chen, Yixi Cai +5

Department of Robotics, Perception and Learning, KTH Royal Institute of Technology, Stockholm, Sweden · Department of Cognitive Robotics, Delft University of Technology, Delft, The Netherlands · Division of Aerospace, Moveability and Naval Architecture, KTH Royal Institute of Technology, Stockholm, Sweden

Control Barrier Functions Obstacle Avoidance Barriers Underwater

Jul 12, 2026cs.RO

Underwater Dead Reckoning with Deployable Situation-Triggered Covariance Scheduling

Underwater dead reckoning estimates vehicle position when vision is unavailable and external positioning cannot be assumed. A single set of filter parameters can work well in many situations, but fixed tuning may be poorly matched during turns, motion transitions, or periods when sensor measurements are less reliable. This paper presents the Situation-Triggered Calibrated Adaptive Robust Extended Kalman Filter for a BlueROV2. An onboard probabilistic trigger identifies the current motion situation while one error-state filter runs continuously. When the trigger is confident, the filter changes only to the corresponding pre-calibrated process- and measurement-noise matrices; the state estimate, covariance history, dynamics, and measurement models are not reset or replaced. The trigger, noise profiles, and a one-time Doppler velocity log yaw-alignment correction are calibrated offline using sparse AprilTag-supervised pool runs. A separate validation set selects the scheduling policy, which is then fixed before held-out testing. Across four held-out pool runs, the method reduces label-weighted mean per-run translation root-mean-square error from 0.488 m to 0.471 m relative to the same filter backbone with one global noise profile, and every held-out run favors the scheduled method. A paired bootstrap over 10-second segments gives a candidate-minus-baseline difference of -0.017 m with a 95% confidence interval of [-0.024, -0.008] m, while orientation error remains essentially unchanged. These results indicate that situation-aware covariance scheduling provides a modest but consistent vision-free dead-reckoning improvement without switching estimators or resetting the filter.

Akshay Naik, Ramavarapu S. Sreenivas, Dustin Nottage +1

University of Illinois Urbana-Champaign, Department of Electrical and Computer Engineering · Dept. of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, 306 N. Wright St., Urbana, IL 61801 USA · University of Illinois Urbana-Champaign, Department of Industrial and Enterprise Systems Engineering +3

Extended Kalman Filter Underwater Doppler Velocity Log Covariance

Jul 2, 2026cs.CV

Rethinking Conditional Generation for Underwater Salient Object Detection

Salient Object Detection in underwater images remains challenging due to low contrast, uneven illumination, and color distortion caused by scattering and absorption effects, which limit the effectiveness of conventional SOD methods in underwater environments. To address these challenges, we propose a Degradation-aware Conditional Generation Network (DCGNet), specifically designed to construct reliable conditional features for underwater saliency generation. First, we design a Dynamic Multi-Granularity module (DMG) grounded in the human visual system to robustly detect salient objects of varying scales with blurred boundaries. Then, we develop an Underwater Physics-Prior module (UPP), which utilizes pseudo-depth guidance to estimate underwater light attenuation and backscatter, thereby restoring degradation-aware RGB features and mitigating color distortion and boundary ambiguity. Based on the physics-guided representation, we introduce an Underwater Spatial Gaussian module (USG), which constructs a spatial Gaussian saliency prior from the strongest guided response to enhance object-centered salient regions and suppress cluttered underwater backgrounds. In addition, a lightweight timestep-adaptive Diffusion Transformer (DiT) bottleneck is inserted into the denoising decoder to refine fused features at different diffusion timesteps. Comprehensive experiments on USOD10K, USOD, CSOD10K, MAS3K, and RMAS demonstrate that DCGNet significantly outperforms existing state-of-the-art methods, verifying its potential for complex underwater visual applications.

Hua Li, Yongjie Weng, Yutong Li +3

School of Computer Science and Technology, Hainan University, Haikou 570228, China · School of Control Science and Engineering and the Key Laboratory of Machine Intelligence and System Control, Ministry of Education, Shandong University, Jinan, Shandong 250061, China · School of Data Science, Lingnan University, Hong Kong, China

Underwater Generative Adversarial Network Diffusion Transformers

Jun 29, 2026cs.CV

Bridging the Gap Between Image Restoration and Navigational Safety in Hazy Conditions: A New Visibility Estimation Metric for Maritime Surveillance

Visibility distance is critical to maritime navigational safety because it determines the effective observation range of shipborne and shore-based monitoring systems. Under hazy conditions, degraded visual information shortens observable distance and increases navigational risks and economic losses. Although numerous image dehazing methods have been developed, conventional image quality assessment metrics, such as PSNR, SSIM, FSIM, FADE, and NIQE, cannot establish a physically interpretable relationship between restoration quality and practical visibility thresholds. To address this limitation, this work proposes a visibility-oriented evaluation framework that links dehazing performance with visible-distance estimation. First, a Maritime Simulated Visibility Dataset (MSVD) is constructed using Unity3D to simulate maritime traffic scenes under graded visibility conditions. The dataset provides paired hazy and clear images with precise visibility annotations, enabling quantitative analysis of visibility restoration. Second, a dehazing visibility evaluation metric is developed by using object detection accuracy as an intermediate indicator. By establishing a mapping between visibility distance and detection performance, the proposed metric converts image restoration improvements into measurable visibility gains. Six representative dehazing methods are evaluated using both conventional image quality metrics and the proposed visibility metric. Experimental results under different imaging conditions demonstrate that MSVD provides a reliable benchmark for evaluating dehazing performance across graded visibility levels, while the proposed metric enables interpretable and reliable visible-distance estimation, thereby supporting the assessment of navigational safety and operational efficiency.

Wentao Feng, Guobei Peng, Wengang Mao +1

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

Single Image Dehazing Ship Detection Underwater Image Restoration

Jun 29, 2026cs.LG

Redefining Maritime Anomaly Detection via Equation-Grounded Synthetic Anomalies

Maritime anomaly detection is essential for ensuring maritime safety, security, and efficient traffic management at sea, with Automatic Identification System (AIS) data serving as a primary data source. Despite its importance, most publicly available AIS datasets lack predefined anomaly labels, forcing prior studies to rely on either distribution-based rarity or domain rule/expert-assisted labeling. These approaches, however, face fundamental limitations: statistical rarity often fails to reflect practically critical events, while expert-based labeling is costly, subjective, and difficult to scale. Moreover, both paradigms tend to overlook interaction-driven hazards such as near-miss approaches between vessels. To address these challenges, we propose an equation-grounded anomaly taxonomy that is implementable under a limited AIS observation schema and extensible to other AIS datasets. Specifically, the taxonomy defines three anomaly types: unexpected AIS activity (A1), route deviation (A2), and close approach (A3), covering both single-vessel and inter-vessel anomalies. Building on this taxonomy, we introduce a unified score-synthesize-label pipeline that produces LLM-guided plausibility scores, uses them to synthesize anomalies, and assigns timestamp-level labels. To rigorously assess detection performance, we further design benchmark evaluation settings that account for variations in temporal-window length and anomaly-type composition, and evaluate a broad range of time-series models and anomaly detection models. Together, these contributions provide a systematic basis for evaluating maritime anomaly detection methods across different anomaly types. Our code is available at https://github.com/snudial/open-maritime-anomaly-detection.

Youngseok Hwang, Sungho Bae, Dohun Lee +4

Seoul National University Seoul, Republic of Korea · KRISO Daejeon, Republic of Korea

Abnormality Automatic Identification System Underwater

Jun 22, 2026cs.CV

Ocean4D: Generative Underwater 4D Reconstruction via Medium-Aware Video Diffusion

Underwater 4D reconstruction remains challenging due to the coupling between degraded light transport in participating media and dynamic water variations. Most existing Methods are developed under in-air assumptions and do not explicitly account for underwater absorption and backscatter. Additionally, near-static assumptions make these approaches sensitive to drifting particles and dynamic distractors , leading to unstable geometry and inconsistent cross-view results. To address these issues, we propose a generative framework for underwater 4D reconstruction, named Ocean4D, which is built on two complementary components. Specifically, 4D-GCC constructs 4D geometrically consistent conditioning with improved cross-frame coverage, while the Medium-Aware Block performs implicit medium-aware denoising in the latent diffusion process to stabilize underwater appearance under absorption and scattering. Given a monocular video and target cameras, our method generates videos along the target trajectories while preserving global structure and cross-view consistency. Extensive experiments on both dynamic and static underwater benchmarks demonstrate state-of-the-art performance on underwater reconstruction.

Yuqiang Huang, Yuxi Wang, Junyu Dong +1

Faculty of Information Science and Engineering, Ocean University of China · Institute of Automation, Chinese Academy of Sciences

Underwater Image Enhancement Methods 4D Reconstruction Underwater Monocular Video Video Diffusion Models Iterative Denoising Methods+1

Jun 14, 2026cs.CV

Fusing Transferred Priors and Physics-based Decomposition for Underwater Image Enhancement

The underwater images are captured within diverse water-medium conditions, leading to complex degradation, including color bias, low contrast, and blur effect. Recently, learning-based methods have demonstrated their potential for underwater image enhancement (UIE). However, most of the previous work focus on the training strategy or network design to make the enhanced result aligned well with the labels in datasets, ignoring that the labels are selected from the enhanced results of previous UIE methods and these pseudo-labels are noisy. Consequently, the performance of their models is not satisfactory to a certain extent. However, collecting the true labels of the underwater images is challenging. In this work, we propose a transfer learning-based UIE that does not require underwater images to have paired noisy or true labels for learning. Instead, the UIE task is first divided into global color correction, haze removal, and background noise suppression following the underwater physics. Then multiple types of prior from other vision tasks are leveraged as cross-domain supervision in each step. In this way, a novel UIE is available via transfer learning, and the physics-aligned UIE decomposition provides theoretical soundness. Qualitative and quantitative experiments demonstrate that our proposal based on physics and priors fusion achieves SOTA performance in the UIE task and effectively boosts downstream vision tasks, significantly outperforming benchmark methods. Project repo: https://github.com/Haru2022/P2-UIE.

Haochen Hu, Yanrui Bin, Zhengyan Zhang +3

The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR

Underwater Image Enhancement Methods Underwater Visual Priors Unpaired Image-To-Image Translation Transfer Learning Decomposition

Jun 12, 2026cs.CV

HARBOR: Heading Analysis and Reconstruction from Behavioral Observation and Radar

Maritime situational awareness often relies on Automatic Identification System (AIS) transmissions to track vessel movements. However, in operational or conflict scenarios, these data may be unavailable due to signal loss, deliberate deactivation, or intentional spoofing. In such conditions, synthetic aperture radar (SAR) imagery becomes a critical sensing alternative for wide-area maritime monitoring, despite providing only static scene snapshots. This work introduces HARBOR (Heading Analysis and Reconstruction from Behavioral Observation and Radar), a complete pipeline for transforming a single SAR image into predictive motion information without requiring any auxiliary data source at inference time. The method begins with SAR image preprocessing to enhance and segment vessel candidates, followed by automatic detection, size-based classification, and heading estimation using skeleton geometry and local intensity patterns. AIS data are used exclusively during an offline calibration phase to derive vessel-type-dependent motion parameters, which are then applied to generate probabilistic heatmaps of candidate future vessel positions. A case study using real COSMO-SkyMed SAR imagery demonstrates the pipeline on a maritime scene in southern Brazil, showing its ability to extract motion tendencies and generate probabilistic projections of vessel positions in data-denied environments.

Joao P. A. Dantas, Paulo F. Silva Filho, Jelton A. Cunha +1

Institute for Advanced Studies, São José dos Campos, São Paulo, Brazil

Ship Detection Synthetic Aperture Radar Underwater Radar Automatic Identification System Agent Behavior Modeling

Jun 11, 2026cs.LG

Rarity-Gated Context Conditioning for Offline Imitation Learning-Based Maritime Anomaly Detection

Contextual anomaly detection aims to identify abnormal behavior conditional on context variables, but practical deployments often face highly imbalanced context distributions where rare regimes can be critical information. Under such frequency bias, context-conditioned models can produce unstable decisions and excessive false alarms in rare contexts. We propose Rarity-Gated Feature-wise Linear Modulation (RGFiLM), a rarity-aware conditioning module that combines feature-wise modulation (i.e., context-conditioned scaling and shifting of hidden features) with a gate controlled by a data-driven rarity score. The rarity score is estimated from the empirical distribution of context variables and regulates how strongly context modulates intermediate representations: the gate becomes more decisive under rare contexts while remaining conservative under frequent contexts. We evaluate RGFiLM on maritime trajectory anomaly detection using AIS motion sequences with ERA5 environmental context in an environment-sensitive detour scenario. When instantiated in a sequential anomaly scoring pipeline, RGFiLM achieves the best mean F1--False Positive Rate (FPR) trade-off among the compared context-agnostic and context-conditioned methods. These results suggest that explicitly accounting for context rarity is an effective approach for reducing false alarms in context-sensitive anomaly detection.

Yongmin Kim, ByeongHoon Jeon, Sungil Kim

Department of Industrial Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea.

Jun 10, 2026cs.RO

Traceable Virtual Sea Trials in the Marine Robotics Unity Simulator for Manoeuvring Assessment of Unmanned Surface Vehicles

Accurate identification of hydrodynamic derivatives is essential for precise control and autonomous navigation of Unmanned Surface Vehicles (USVs). However, acquiring high-fidelity manoeuvring data from physical sea trials is often constrained by cost, safety, and environmental disturbances. Standard manoeuvring trials, particularly Turning Circle (TC) and Zig-Zag (ZZ), remain fundamental to IMO and ITTC assessment procedures because they provide comparable performance metrics reflective of underlying hydrodynamic behaviour. This paper extends the open-source Marine Robotics Unity Simulator (MARUS) by introducing a standardised Virtual Sea Trial framework for automated execution and data generation of TC/ZZ manoeuvres. The framework provides traceable command-actuation logging, system-identification (SI)-focused data conditioning, and automated extraction of IMO/ITTC-aligned manoeuvring metrics. A key contribution is a dedicated TC/ZZ data acquisition and post-processing pipeline, improving the repeatability and auditability of simulator-based manoeuvres while producing SI-ready datasets for hydrodynamic-derivative identification and digital-twin workflows. The framework also provides explicit command-execution separation for differential-thrust steering, where manoeuvre inputs are recorded as ordered rudder-equivalent commands and realised actuation is logged as an execution-level proxy derived from applied thrust. Case study results demonstrate repeatable and IMO-compliant manoeuvre behaviour. For TC tests, the normalised advance differs by approximately 3.9% between port and starboard turns, while the tactical diameter differs by 4.6-4.7%. For ZZ tests, first and second overshoot excesses remain below 1 degree for both +/-10-degree and +/-20-degree manoeuvres, satisfying IMO criteria, while peak yaw rates range from approximately 4.1 to 5.8 degrees/second.

Paria Rezayan

School of Engineering and Built Environment, Sheffield Hallam University, UK

Underwater Advanced Jet Trainer Steering

May 29, 2026cs.RO

Cuttlebot: a platform demonstration for complex, autonomous, bio-inspired swimmers

Increasing interest in deep-sea operations and resources motivates the development of ecologically sensitive but environmentally durable robots. Dielectric elastomer actuator artificial muscles are good candidates for powering such systems due to their pressure and temperature tolerance and soft makeup, but they are difficult to integrate with robotic systems. This work presents an autonomous robotic platform: the CORE, capable of driving six artificial muscles while sensing visual and spatial information. To validate the platform, we developed the Cuttlebot - a cuttlefish-inspired robot that swims in three dimensions using undulatory fin locomotion. The Cuttlebot has four primary artificial muscles in its fins in addition to a tentacle-inspired soft gripper. The robot was evaluated in a series of tethered and untethered swimming tests, demonstrating a top speed of 2.5 centimeters per second translation and 10 degrees per second rotation. Furthermore, the CORE system was capable of driving specialized control signals into the artificial muscles to controllably output force and torque in six axes. This work provides a platform for developing complex, bio-inspired swimming robots for ocean exploration and monitoring, laying the foundation with our leading example: the Cuttlebot.

Alexander Nicholas White, Ang Leo Li, Alexander Yin +5

School of Mechanical, Aerospace, and Manufacturing Engineering, University of Connecticut; Storrs, CT, U.S.A. · Department of Mechanical and Industrial Engineering, University of Toronto; Toronto, Ontario, Canada. · University of Connecticut, Institute of Materials Science; Storrs, CT, U.S.A. +1

Underwater Robots Soft Robotics Soft Pneumatic Actuators Underwater Gripper

May 28, 2026cs.AI

Compass: Navigating Global Marine Lead Data Integration through Expert-Guided LLM Agent

Marine lead (Pb) and its isotopes are critical tracers for ocean circulation and anthropogenic pollution, yet in-situ observations remain costly and sparse. While vast historical records exist, they lie buried within the unstructured content of academic papers, creating "data silos" inaccessible to comprehensive analysis. Manual extraction is unscalable, while general-purpose Large Language Models (LLMs) lack the necessary domain-specific knowledge, leading to hallucinations and scientifically invalid outputs. To address this, we introduce an expert-guided adaptation approach that enables LLMs to perform rigorous scientific data extraction without fine-tuning. We operationalize this approach through Compass, an LLM agent framework enhanced by a Knowledge Tree co-designed with marine scientists, which decomposes complex tasks into verifiable steps, guiding the agent's reasoning to ensure scientific validity. Deploying Compass across a corpus of over 230,000 relevant open-access papers, we successfully extract 3,751 previously unincorporated Pb records. This effort establishes the largest integrated marine Pb database to date. Beyond standard metrics, Compass demonstrates superior reliability through multi-layered validation, achieving 92% accuracy as confirmed through expert manual verification. The newly integrated data expand coverage in previously under-sampled regions such as the East China Sea and the Southern Ocean, providing an enriched data foundation for future scientific discoveries. We release an interactive visualization platform to facilitate open scientific access. Our work demonstrates that expert-guided agents can effectively bridge the gap between general-purpose LLMs and high-stakes scientific domains, enabling scalable data discovery in geosciences.

Yiming Liu, Bin Lu, Meng Jin +6

May 28, 2026cs.LG

A Novel Evaluation Metric for Unsupervised Learning in AIS-Based Maritime Anomaly Detection: MADQI

This paper introduces a new systematic framework for detecting anomalies in maritime Automatic Identification System (AIS) datasets. These anomalies include abnormal vessel behaviours related to speed, position jumps, time gaps, and turn angles. Although unsupervised learning algorithms such as Isolation Forest are widely used for detecting anomalous vessel movements, they often lack systematic and meaningful evaluation measures. To address this limitation, we propose a novel quality metric called Maritime Anomaly Detection Quality Index (MADQI). The prosed MADQI is a composite index designed to evaluate the anomaly detection performance of machine learning models without requiring labelled data. The proposed framework uses Haversine distance calculations to analyse AIS datasets and identify anomalies based on their spatial and behavioural characteristics. The proposed MADQI evaluation framework integrates four interconnected metrics: Anomaly Rate Consistency (ARC), Physical Plausibility Score (PPS), Score Distribution Separation (SDS), and Extreme Case Evidence (ECE). These metrics are combined through automatic normalisation using multi-chunk evaluation and adaptive scaling techniques. Experimental results on the AIS dataset show that the proposed framework achieved a MADQI score of 80.37%, demonstrating its effectiveness for unsupervised anomaly detection. In particular, the algorithm performed strongly in identifying abnormal vessel behaviour. Among the individual MADQI components, ECE and ARC achieved scores of 0.907 and 1.000, respectively, indicating excellent capability in detecting extreme anomalies and maintaining anomaly rate consistency. Overall, these results are encouraging and demonstrate that the proposed framework provides a reliable and meaningful approach for evaluating unsupervised anomaly detection in maritime AIS data.

Ismet Gocer, Zakirul Bhuiyan, Raza Hasan +1

Southampton Solent University School of Technology and Maritime Industries Southampton, United Kingdom

Automatic Identification System Underwater Unsupervised

May 26, 2026cs.CV

CmIVTP: Cross-modal Interaction-based Vessel Trajectory Prediction for Maritime Intelligence

Maritime intelligent transportation systems (MITS) are essential for ensuring navigation safety and efficiency in busy waterways. However, accurate vessel trajectory prediction remains challenging due to the limitations of single-source data. Automatic identification system (AIS) data is often sparse or unavailable for small vessels, while closed-circuit television (CCTV) data alone cannot fully capture dynamic vessel behavior. To mitigate these challenges, we propose a cross-modal interaction-based vessel trajectory prediction (named CmIVTP) framework to model the intricate interactions between vessel dynamics and environmental constraints. Specifically, we introduce a target-aware scene encoder to extract scene semantic features, effectively capturing vessel-environment interactions and enhancing trajectory prediction accuracy. In addition, we propose a cross-modal interaction transformer, which integrates AIS-derived motion features, CCTV-based environmental features, and scene representations. It leverages cross-modal attention mechanisms to simultaneously capture intra-modal semantics and inter-modal interactions, ensuring dynamically consistent and environmentally feasible predictions. Furthermore, we construct a vessel group trajectory bank by clustering historical AIS trajectories into representative motion patterns, providing an efficient and scalable approach for candidate trajectory generation. Additionally, we introduce the maritime multimodal dataset plus (named Maritime-MmD

^+

), a large-scale dataset that synchronizes AIS data and CCTV video data, providing robust support for multimodal trajectory prediction research. Extensive experiments demonstrate that CmIVTP achieves better performance on multimodal-driven vessel trajectory prediction benchmarks. The code resources for this work can be available at https://github.com/LouisYxLu/CmIVTP.

Yuxu Lu, Dong Yang, Xiaoyu Li +2

Department of Logistics and Maritime Studies, the Hong Kong Polytechnic University, Hong Kong 999077 · Department of Logistics and Maritime Studies and the Research Centre for ESG Advancement (RCESGA), the Hong Kong Polytechnic University, Hong Kong 999077 · Department of Logistics and Maritime Studies, the Hong Kong Polytechnic University, Hong Kong 999077, and also with the School of Navigation, Wuhan University of Technology, Wuhan 430063

Maritime Navigation Underwater Cross-Modal

May 26, 2026cs.CV

Underwater360: Reconstructing Underwater Scenes from Panoramic Images with Omnidirectional Gaussian Splatting

Underwater scene reconstruction is essential for immersive exploration of aquatic environments, yet remains challenging due to complex participating-media effects such as absorption and scattering, as well as the limited field of view (FoV) of conventional cameras. Although combining panoramic imaging with 3D Gaussian Splatting (3DGS) offers a promising direction for photorealistic underwater rendering, traditional 3DGS struggles with both spherical projection distortion and underwater medium degradation. In this paper, we propose \textbf{Underwater360}, a physics-informed omnidirectional 3DGS framework for underwater panoramic scene reconstruction. First, we introduce an Omnidirectional Gaussian Splatting module that performs ray casting directly in spherical camera space instead of relying on 2D projection approximations, thereby reducing geometric distortions under 360

^\circ

FoV. Second, we design a physics-based appearance-medium modeling architecture with pose-conditioned appearance embeddings to explicitly decouple intrinsic scene radiance from depth-dependent backscatter and attenuation, enabling physically grounded scene appearance restoration. Finally, we establish a new panoramic underwater benchmark dataset containing both synthetic and real-world scenes. Extensive experiments demonstrate that Underwater360 achieves superior performance in underwater novel view synthesis and scene appearance restoration, delivering improved rendering quality and cross-view consistency in complex underwater environments. The code and datasets are released at https://github.com/SwcK423/Underwater360

Jiangbei Hu, Weichao Song, Shibo Yu +8

School of Software, Dalian University of Technology · College of Computing and Data Science, Nanyang Technological University

Underwater Image Enhancement Methods Underwater Object Detection Underwater Omnidirectional Images Gaussian Splatting Gaussian Primitives

May 25, 2026cs.RO

Path Following Control System of Line-of-Sight Guidance for Robotic Dolphin with Multi-Link Mechanism in Underwater Simulator

Biomimetic autonomous underwater vehicle (BAUV) with multi-link mechanism is widely used in aquatic life observation and environmental surveys due to its low power consumption and high maneuverability. An environmental survey requires a path following system that automatically follows specific points. However, the path following system of BAUV is limited, and its evaluation with multi-link mechanism robots has not yet been clarified. The path following system in BAUV requires prior simulation because the model differs depending on the type of biomimetics. In this study, we propose a path following system for BAUVs with a multi-link mechanism and evaluation in underwater simulation. In this result, it was possible to design a path following system suitable for BAUV, determine parameters using a simulator, and evaluate control methods.

Takumi Asada, Takao Oki, Hideo Furuhashi +3

Utsunomiya University, 7-1-2 Yoto, Utsunomiya, Tochigi, 321-8585, Japan · Aichi Institute of Technology, 1247 Yachigusa, Yakusa, Toyota, Aichi, Japan

Underwater Robots Underwater Underactuated Dynamics Multi-Uav Kinematic Chain Coral+1

May 4, 2026eess.SP

Memory-Efficient EDA Denoising via Knowledge Distillation for Wearable IoT Under Severe Motion Artifacts and Underwater Conditions

Electrodermal activity (EDA) is widely used in wearable Internet of Medical Things (IoMT) systems for continuous health monitoring, including autonomic assessment. However, EDA signals are highly vulnerable to motion artifacts and environmental noise, limiting reliable deployment in harsh operating conditions such as underwater. This study proposes a robust, deployable EDA denoising framework that generalizes across multiple measurement locations and harsh environments. The framework integrates a hybrid CNN-Transformer teacher model with a lightweight depth-wise separable CNN student model via a knowledge distillation (KD) strategy. To further improve robustness, a realistic data augmentation scheme is introduced to simulate diverse motion artifacts and environmental distortions. The KD-based student model significantly reduces model size (7.87 MB to 0.51 MB) and computational cost (105.1M to 11.61M FLOPs) while maintaining denoising performance (MAE: 0.144, SNR improvement: 12.08 dB) using the public dataset validation. In real-world underwater conditions (UMAC dataset) testing, the proposed method substantially improves skin conductance response reconstruction, reducing mean absolute error from 2.809 to 0.215. Furthermore, on independent testing using the CNS-OT dataset, the denoised signals enhanced downstream CNS-OT prediction performance, achieving the highest AUROC (0.806) compared to prior denoising methods. The proposed method also improved the early prediction rate (sensitivity) from 0.550 to 0.767, enabling CNS-OT prediction up to a median of 6.9 minutes before symptom onset. These results demonstrate that the proposed framework not only improves EDA signal quality but also enhances clinically relevant prediction performance while remaining suitable for deployment in resource-constrained wearable Internet of Things systems operating in harsh environments.

Yongbin Lee, Andrew Peitzsch, Youngsun Kong +4

Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA

Wearable Devices Surface Electromyography Deep Learning Framework Underwater Compression Artifacts Knowledge Distillation

Apr 28, 2026cs.CV

Why Domain Matters: A Preliminary Study of Domain Effects in Underwater Object Detection

Domain shift, where deviations between training and deployment data distributions degrade model performance, is a key challenge in underwater environments. Existing benchmarks testing performance for underwater domain shift simulate variability through synthetic style transfer. This fails to capture intrinsic scene factors such as visibility, illumination, scene composition, or acquisition factors, limiting analysis of real-world effects. We propose a labeling framework that defines underwater domains using measurable image, scene, and acquisition characteristics. Unlike prior benchmarks, it captures physically meaningful factors, enabling semantically consistent image grouping and supporting domain-specific evaluation of detection performance including failure analysis. We validate this on public datasets, showing systematic variations across domain factors and revealing hidden failure modes.

Melanie Wille, Dimity Miller, Tobias Fischer +1

QUT Centre for Robotics, Queensland University of Technology

Underwater Object Detection Underwater Preliminary Study

Apr 21, 2026cs.CV

BALTIC: A Benchmark and Cross-Domain Strategy for 3D Reconstruction Across Air and Underwater Domains Under Varying Illumination

Robust 3D reconstruction across varying environmental conditions remains a critical challenge for robotic perception, particularly when transitioning between air and water. To address this, we introduce BALTIC, a controlled benchmark designed to systematically evaluate modern 3D reconstruction methods under variations in medium and lighting. The benchmark comprises 13 datasets spanning two media (air and water) and three lighting conditions (ambient, artificial, and mixed), with additional variations in motion type, scanning pattern, and initialization trajectory, resulting in a diverse set of sequences. Our experimental setup features a custom water tank equipped with a monocular camera and an HTC Vive tracker, enabling accurate ground-truth pose estimation. We further investigate cross-domain reconstruction by augmenting underwater image sequences with a small number of in-air views captured under similar lighting conditions. We evaluate Structure-from-Motion reconstruction using COLMAP in terms of both trajectory accuracy and scene geometry, and use these reconstructions as input to Neural Radiance Fields and 3D Gaussian Splatting methods. The resulting models are assessed against ground-truth trajectories and in-air references, while rendered outputs are compared using perceptual and photometric metrics. Additionally, we perform a color restoration analysis to evaluate radiometric consistency across domains. Our results show that under controlled, texture-consistent conditions, Gaussian Splatting with simple preprocessing (e.g., white balance correction) can achieve performance comparable to specialized underwater methods, although its robustness decreases in more complex and heterogeneous real-world environments

Michele Grimaldi, David Nakath, Oscar Pizarro +3

School of Engineering & Physical Sciences, Heriot-Watt University, Edinburgh, UK · Marine Data Science Group, Christian Albrechts University of Kiel, Kiel, Germany · Department of Marine Technology, Norwegian University of Science and Technology (NTNU), Trondheim, Norway +1

Underwater Image Enhancement Methods 3D Reconstruction Underwater Object Detection Underwater Illumination Variations Robotic Perception

May 12, 2025cs.CV

TUGS: Physics-based Compact Representation of Underwater Scenes by Tensorized Gaussian

Underwater 3D scene reconstruction is crucial for multimedia applications in adverse environments, such as underwater robotic perception and navigation. However, the complexity of interactions between light propagation, water medium, and object surfaces poses significant difficulties for existing methods in accurately simulating their interplay. Additionally, expensive training and rendering costs limit their practical application. Therefore, we propose Tensorized Underwater Gaussian Splatting (TUGS), a compact underwater 3D representation based on physical modeling of complex underwater light fields. TUGS includes a physics-based underwater Adaptive Medium Estimation (AME) module, enabling accurate simulation of both light attenuation and backscatter effects in underwater environments, and introduces Tensorized Densification Strategies (TDS) to efficiently refine the tensorized representation during optimization. TUGS is able to render high-quality underwater images with faster rendering speeds and less memory usage. Extensive experiments on real-world underwater datasets have demonstrated that TUGS can efficiently achieve superior reconstruction quality using a limited number of parameters. The code is available at https://liamlian0727.github.io/TUGS

Shijie Lian, Ziyi Zhang, Hua Li +4

1Huazhong University of Science and Technology · 2Zhongguancun Academy · 3The Chinese University of Hong Kong, Shenzhen +3

Underwater Image Enhancement Methods Underwater Acoustic Target Recognition Underwater Gaussian Primitives