Underwater mapping 2026
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01 - Robust Localization of Flange Interface for LNG Tanker Loading and Unloading Under Variable Illumination a Fusion Approach of Monocular Vision and LiDAR
Authors: Mingqin Liu, Han Zhang, Jingquan Zhu, Yuming Zhang and Kun Zhu This paper addresses the challenge of accurately localizing flange interfaces in LNG tankers under extreme illumination conditions. While monocular vision fails in glare or low light, and LiDAR lacks texture information, the authors propose a vision-LiDAR fusion method with three key innovations: an illumination-adaptive framework that dynamically adjusts parameters based on grayscale evaluation, a multi-constraint flange detection strategy using physical constraints and K-means clustering, and a customized fusion pipeline combining ROI extraction, plane fitting, and 3D circle solving to compensate for individual sensor limitations.
02 - A lightweight underwater image and video enhancement method based on multi-scale feature fusion
Authors: Gaosheng Luo, Haiyang Li, Huanhuan Wang, Hengshou Sui, Xuewen Zhang, Rongjun Zhang, Bocheng Chen, and Zhe Jiang Underwater image quality is reduced by light absorption and scattering, affecting AUVs and marine monitoring. The authors propose UIVE, a lightweight enhancement algorithm for underwater visuals. Key features include using residual blocks instead of batch normalization, multi- scale connections for detail preservation, and an adaptive brightness correction module.
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03 - A Review of Learning-Based Sonar Signal Processing Using Neural Network
Authors: W. S. C. Rodrigo, Udara S. P. R. Arachchige, C. Perera, C. Gunarathna, W. H. Y. N. Samarasinghe, S. L. Mallawathanthri, D. D. P. P. Jayawardhana, Z. I. Coonghe, G. K. W. P. S. G. Kumbura, A. H. M. K. P. Abeysinghe, S. J. M. S. Theekshana, D. M. C. Ronali, D. I. U. Rupasinghe, M. D. Wijesinghe This paper reviews learning-based sonar signal processing techniques, primarily neural networks, as a data-driven alternative to conventional methods that are limited by handcrafted features and model assumptions in complex underwater environments. The review categorizes existing studies into feature-based deep learning, end-to-end learning from raw acoustic data, and hydrophone-array processing. It also covers general applications like sonar imaging and target classification.
04 - Underwater 3D target detection: Semi-analytic Monte Carlo model and UAV-based scanning lidar system
Authors: Xinke Hao, Yan He, Huixin He, Deliang Lv, Yingjie Ruan, Hui Qi, Guangxiu Xu, and Junwu Tang A semi-Monte Carlo model (MCT) was developed to simulate lidar detection of underwater targets, considering interactions with the target, surface waves, and stratified water. This model was validated by a UAV-based linear scanning oceanic lidar system (SOL) and achieved less than 5% error within 50m depth. Field experiments confirmed SOL's consistent target localization. The MCT model was then used to analyze SOL's detection capabilities for different water types (Jerlov II and 3C), introducing an extended detection range concept to assess how horizontal scanning resolution changes with target depth, offering guidance for optimizing detection and efficiency.
05 - An explainable context-adaptive fusion and expert-in-the-loop evaluation framework for underwater sonar image classification
Authors: Kamal Basha, Anukul Kiran, Athira Nambiar, Suresh Rajendran, and Sooraj K Ambat Sonar image interpretation is critical for identifying submerged objects in underwater exploration. However, conventional deep learning models often fail to capture sonar-specific features such as acoustic shadows and highlights, and they typically operate as black boxes, limiting both performance and trust. To address these challenges, the authors propose an explainable sonar image classification system that fuses specialized classifiers for shadows, highlights, and general features through a context-adaptive fusion mechanism. The system further incorporates expert-in-the-loop evaluation via the Augmented QUality Assessment for eXplainability (AQUA- X) framework to ensure interpretability and trust.
06 - Analysis of Underwater Single-Photon LiDAR Signals: A Comprehensive Study on Multi-Parameter Coupling Effects
Authors: Ceyuan Wang, Shijie Liu, Shouzheng Zhu, Wenhang Yang, Chenhui Hu, Yuwei Chen, Chunlai Li, and Jianyu Wang Underwater laser signal attenuation poses challenges for conventional detection, but single-photon LiDAR (SPL) with high sensitivity offers a promising solution. Prior studies mainly examined isolated parameters, leaving the coupled effects of environmental and system factors underexplored. This research developed a 532 nm underwater SPL system to systematically investigate multi- parameter coupling in laboratory water tanks, varying turbidity, detection distances, laser energy levels, integration times, and target types.
07 - Comparison of Conventional, Rake, and Sonar-Based Biophysical Habitat Measurements in a Shallow Ontario River
Authors: Karl A. Lamothe, Jason Barnucz, D.  Andrew,   R. Drake Accurate habitat data is vital for managing and restoring freshwater species, but fine-scale sampling is labor- intensive. This study in Ontario, Canada, compared sonar- derived habitat measurements (depth, macrophyte volume, substrate) with conventional point-based methods in a shallow river. Both methods showed nearly all areas were <2m deep, though conventional depth readings were higher at 88% of sites. Depth correlations were strong, but substrate and macrophyte measurements showed weak alignment. Differences stemmed from measurement scale and inherent errors in both methods. The optimal approach depends on the precision required for management decisions.
08 - SonarKAN:Sonar Kolmogorov-Arnold Network for Disentangling Passive Sonar Signatures
Authors: Xiaoliang Chen Data-driven neural models for passive sonar can be accurate but hard to interpret. The proposed Physics- aligned Sonar Kolmogorov–Arnold Network (SonarKAN) aligns networks with the passive sonar equation's structure. It separates transmission loss from source signatures and uses a physics-informed ridge projection for faster convergence. Simulations show improved accuracy and robustness to label noise compared to a multilayer perceptron (MLP).
09 - Underwater SLAM and Calibration with a 3D Profi ling Sonar
Authors: António Ferreira, José Almeida, Aníbal Matos, and Eduardo Silva High-resolution underwater mapping is essential for the sustainable blue economy, supporting offshore energy expansion, marine habitat protection, and resource monitoring. This work presents a pose-graph Simultaneous Localization and Mapping (SLAM) and calibration framework specifically designed for 3D profiling sonars such as the Coda Octopus Echoscope 3D. It employs a probabilistic scan-matching method for direct scan-to-scan 3D sonar registration, enabling accurate trajectory and map estimation even under degraded dead-reckoning conditions.
10 - Volumetric Path Planning and Visualization for ROV-Based Forward-Looking Sonar Scanning of 3D Water Areas
Authors: Yu-Cheng Chou, and Wei-Shan Chang Remotely operated vehicles (ROVs) equipped with multibeam forward-looking sonar are widely used for underwater object searches in low-visibility environments. Achieving complete three-dimensional (3D) scan coverage within a limited mission duration is challenging due to sonar beam geometry and vehicle motion constraints. This study presents a deterministic, geometry- driven framework for volumetric path planning of forward-looking sonar scans in predefined circular and rectangular underwater volumes. The approach constructs layered planar scan trajectories by explicitly incorporating sonar detection range, horizontal and vertical beamwidths, and scan volume geometry.
11 - A Comparison of Detection Methods for Identifying the Presence of Active Sonar in Long-Term Passive Acoustic Data: A Case Study Within a Scottish Marine Protected Area
Authors: Benedict L. Dell, Ellen L. White, Denise Risch, Jonathan M. Bull, Paul R. White & Nienke C. F. van Geel Standardized methods for detecting and classifying active sonar are lacking, which affects ecological research using Passive Acoustic Monitoring (PAM) data. This chapter compares two classification pipelines for identifying military sonar in 20-minute files using deep learning and Gradient Boosted Random Forest.
12 - The Principle and Application of Underwater Sonar Systems
Authors: Siyi Wang, Yiduo Wang, and Lang Yin As underwater detection needs increase in marine research and engineering, sonar systems have become a key research focus. Enhancing their precision, efficiency, and adaptability is urgent. The article outlines different sonar types, detailing single-beam's lower cost and limited precision versus multi-beam's higher cost but better accuracy for complex seabeds. It summarizes current sonar systems, highlighting their deficiencies for future development.