Underwater mapping 2024 - Now
BUTTON TEXT Previous page Back to the menu 06 - Editorial: Unmanned marine vehicles for ocean observation
Authors: Shuo Pang, Ye Li, Liang Xiao, Francisco Rego, & Teng Ma This paper intends to inform and highlight the importance and advancements of unmanned marine vehicles (USVs and UUVs) in ocean observation, particularly in extreme environments like the deep sea and polar regions. It aims to showcase recent technological advancements and future implications in areas such as sensing, control, navigation, and communication of these vehicles, emphasizing their role in enhancing the quantity and quality of oceanographic data collected.
07 - Multifidelity topology design of a maritime survey operation with UUVs
Authors: Danielle F. Morey, Randall S. Plate, Cherry Y. Wakayama, & Zelda B. Zabinsky This study focuses on optimizing maritime survey operations' topology using unmanned underwater vehicles (UUVs). It employs a multi-fidelity approach to evaluate different configurations for assigning UUVs to data collection sensors or locations. That involves using both low-fidelity models, which simplify assumptions to allow for sensitivity analysis with low computational cost, and a high-fidelity simulation model, which provides detailed performance metrics under more realistic conditions.
01- MTUW-GAN: A Multi-Teacher Knowledge Distillation Generative Adversarial Network for Underwater Image Enhancement
Authors: Tianchi Zhang and Yuxuan Liu This text describes a new method for enhancing underwater images, specifically designed for use with Autonomous Underwater Vehicles (AUVs). It outlines the challenges faced with current methods, such as high computational demands and complex network structures, and introduces a novel approach using a multi-teacher knowledge distillation GAN (MTUW-GAN) to improve image quality while being resource-efficient, making it suitable for real-time deployment on AUVs. The text also highlights the advantages of this method over existing approaches in terms of visual quality, model parameters, and performance.
02 - A Low-Cost and High-Precision Underwater Integrated Navigation System
Authors: Jiapeng Liu, Te Yu, Chao Wu, Chang Zhou, Dihua Lu, and Qingshan Zeng This document validates a new underwater integrated navigation system for Autonomous Underwater Vehicles (AUVs) that is cost-effective, lightweight, and compact compared to traditional systems. It describes the development and testing of this system, highlighting its advantages in terms of precision and cost, as well as its potential applications in both military and civilian contexts.
03 - A Statistical Evaluation of the Connection between Underwater Optical and Acoustic images
Authors: Rebeca Chinicz, and Roee Diamant This paper describes a research study focused on improving the detection capabilities of Synthetic Aperture Sonar (SAS) in autonomous underwater vehicle (AUV) surveys by combining SAS with optical images. It addresses the challenges of matching multimodal images (optical and acoustic) by proposing an entropic method for recognizing matching images of the same object. It also investigates the probabilistic dependency between the two modalities and presents results that demonstrate improved detection and false alarm rates.
05 - Numerical simulation of hydrodynamics of ocean-observation- used remotely operated vehicle
Authors: Dapeng Zhang, Bowen Zhao, Yi Zhang, and Nan Zhou This document intends to explain the process and importance of determining hydrodynamic coefficients for remotely operated vehicles (ROVs) used in marine engineering. It discusses the advantages of ROVs, the necessity of establishing a motion mathematical model, and the use of simulation techniques to predict performance and guide design improvements to convey the significance of accurate hydrodynamic simulations in enhancing the maneuverability and design of ROVs.
08 - Underwater Camera Calibration Based on Double Refraction
Authors: Yushan Sun, Tian Zhou, Liwen Zhang and Puxin Chai This document describes advancements in the methodology for calibrating underwater cameras to improve measurement accuracy in underwater environments. It outlines the challenges of double refraction errors and presents a solution involving a double refraction model and a parameter optimization method using genetic algorithms to convey the effectiveness of the proposed method in achieving a more precise determination of underwater camera parameters compared to other algorithms.
09 - A Simulation Method for Underwater SPAD Depth Imaging Datasets
Authors: Taoran Lu, Su Qiu, Hui Wang, Shihao Zhu and Weiqi Jin This text presents a research solution for improving underwater imaging using single-photon avalanche diode (SPAD) devices. The text outlines the challenges associated with SPAD devices, such as high production costs and small array areas, and proposes a method to simulate SPAD data and develop a denoising network using deep learning to remove backward-scattering interference. It also highlights the effectiveness of this approach by discussing the experimental results and improvements in specific performance metrics.
10 - Deep Learning-Based Geomorphic Feature Identification in Dredge Pit Marine Environment
Authors: Wenqiang Zhang, Xiaobing Chen, Xiangwei Zhou, Jianhua Chen, Jianguo Yuan, Taibiao Zhao, and Kehui Xu This study introduces a new dataset and a classification model for identifying geomorphic features in dredge pit environments using sidescan sonar (SSS) images and deep learning methods. It aims to highlight the effectiveness and efficiency of the new Effective Geomorphology Classification (EGC) model, discuss the utility of transfer learning and data augmentations, and emphasize the value of the dataset and model for hazard monitoring and other machine learning applications.
11 - Underwater target laser polarization suppression scattering detection technology and verification
Authors: Qiang Fu, Chao Dong, Kaikai Wang, Qingyi He, Xiansong Gu, Jianhua Liu, Yong Zhu, Jin Duan This text describes the development and effectiveness of a new underwater laser polarization detection technology. It outlines the challenges addressed by this technology, the proposed solution, and the results of experiments conducted to validate the device's accuracy and precision in detecting underwater targets.
12 - Path Planning for Autonomous Underwater Vehicles (AUVs) Considering the Influences and Constraints of Ocean Currents
Authors: Ziming Chen, Jinjin Yan, Ruen Huang, Yisong Gao, Xiuyan Peng, and Weijie Yuan The text describes a new path-planning algorithm for autonomous underwater vehicles (AUVs) that effectively addresses the challenges posed by ocean currents. It outlines the limitations of conventional algorithms and introduces a novel approach that combines elements of the influences and constraints of ocean currents, which leverages the strengths of two widely employed path- planning algorithms and genetic algorithms to improve path planning by considering ocean current influences.
13 - Dual-Mode Square Root Cubature Kalman Filter for Miniaturized Underwater Profiler Dead Reckoning
Authors: Yang Zhang, Qingchao Xia, Canjun Yang, Ruiyin Song, Dingze Wu, Xin Zhang, Rui Zhou, and Shuyang Ma The intent of the text is to describe the development and evaluation of a miniaturized underwater profiler system for ocean observation. It outlines the advantages of such profilers, the design and modeling process, and the implementation of a new algorithm (DSRCKF) for dead reckoning. The text also compares this algorithm with others and discusses its effectiveness, particularly in certain directions and attitudes. The intent is to inform about the research and development process, the technical details of the system, and the future plans for real-world testing.
14 - An Underwater Multisensor Fusion Simultaneous Localization and Mapping System Based on Image Enhancement
Authors: Zeyang Liang, Kai Wang, Jiaqi Zhang, and Fubin Zhang The intent of this text is to present a study focused on improving the positioning accuracy of autonomous underwater vehicles (AUVs) through the development of a novel method for visual simultaneous localization and mapping (SLAM) in underwater environments. The text outlines the proposed approach, which involves an underwater multisensor fusion SLAM system based on image enhancement, and highlights the effectiveness of this method in reducing errors compared to other algorithms.
15 - Redefining Accuracy: Underwater Depth Estimation for Irregular Illumination Scenes
Authors: Tong Liu, Sainan Zhang, and Zhibin Yu This paper outlines the challenges current methods face due to irregular illumination in underwater environments, introduces a proposed solution, an underwater self- supervised monocular depth estimation network, and presents a new approach for improving underwater depth estimation. This approach integrates image enhancement and auxiliary depth information to address issues like low-light conditions and overexposure.
16 - Research Advances and Prospects of Underwater Terrain-Aided Navigation
Authors: Rupeng Wang, Jiayu Wang, Ye Li, Teng Ma, and Xuan Zhang This document informs researchers about the current state and future developments in the field of underwater terrain-aided navigation (TAN) to provide a comprehensive overview of the application background, operating principles, technical aspects, and relevant algorithms involved in underwater TAN, as well as to summarize cutting-edge issues in the field.
17 - Multi-Scale Marine Object Detection in Side-Scan Sonar Images Based on BES-YOLO
Authors: Quanhong Ma, Shaohua Jin, Gang Bian, and Yang Cui This document describes a proposed solution to improve the accuracy of multi-scale seafloor target detection in side-scan sonar images, which are challenged by high noise and complex background textures. It outlines the development and implementation of a model using the BES-YOLO network, detailing the enhancements made to the YOLOv8 network, such as the introduction of an efficient multi-scale attention mechanism, a bi-directional feature pyramid network, and a Shape_IoU loss function. It also discusses the preprocessing of the dataset to enhance network robustness and presents experimental results that demonstrate improved detection accuracy.
18 - Underwater Unsupervised Stereo Matching Method Based on Semantic Attention
Authors: Qing Li, Hongjian Wang, Yao Xiao, Hualong Yang, Zhikang Chi, and Dongchen Dai This article introduces an unsupervised stereo matching method based on semantic attention, addressing the challenge of insufficient training data. It aims to improve the intelligence of underwater robots and advance scientific research and marine resource development. It highlights the design of an adaptive double quadtree semantic attention model and an unsupervised semantic loss function, emphasizing the method's robustness and improved performance metrics through evaluations.
19 - Enhancing Underwater Object Detection and Classification Using Advanced Imaging Techniques: A Novel Approach with Diffusion Models
Authors: Prabhavathy Pachaiyappan, Gopinath Chidambaram , Abu Jahid, and Mohammed H. Alsharif This study proposes a new approach to improve underwater object detection and classification to address challenges posed by environmental factors such as water turbidity and variable lighting conditions by integrating advanced imaging techniques with diffusion models. This research aligns with Sustainable Development Goal (SDG) 14: Life Below Water, and it introduces a new imaging technique, AIT-YOLOv7, which enhances the accuracy of detecting and classifying underwater objects. It highlights the methodology, the use of the TrashCan dataset for validation, and the significant improvement in detection accuracy compared to existing methods.
21 - Prediction-Based Submarine Cable-Tracking Strategy for Autonomous Underwater Vehicles with Side-Scan Sonar
Authors: Hao Feng, Yan Huang, Jianan Qiao, Zhenyu Wang, Feng Hu, and Jiancheng Yu This study focuses on improving the tracking of underwater cables using autonomous underwater vehicles (AUVs) equipped with side-scan sonar. It aims to address issues related to AUV motion stability and imaging quality by proposing a new strategy involving non-myopic receding-horizon optimization and the use of a long short- term memory network. Also, it highlights the development of an efficient decision-making framework to execute these strategies in real time, and it reports on the validation and effectiveness of the proposed method through comparative experiments.
22 - A Tank Experiment of the Autonomous Detection of Seabed- Contacting Segments for Submarine Pipelaying Operations
Authors: Bo Wang, Jie Wang, Chen Zheng, Ye Li, Jian Cao, and Yueming Li This document describes a proposed solution to improve the detection of seabed-contacting segments during underwater pipeline operations. It outlines the challenges faced due to weak structural features and environmental conditions and introduces a technical solution involving a cascade attention module and a prefusion module integrated with a convolutional neural network. The intent is to convey the effectiveness of these modules in enhancing the performance of neural network models for target detection and instance segmentation, as evidenced by experimental results.
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04 - Learning hybrid dynamic transformers for underwater image super-resolution.
Authors: Xin He, Junjie Li, and Tong Jia Underwater image super-resolution is important for making underwater images clearer, which aids in analysis and exploration. This paper presents an effective hybrid dynamic Transformer (HDT-Net) for this task. It uses dynamic local self-attention to capture details and sparse non-local self-attention to avoid irrelevant information. Our method outperforms previous models in both quantitative and qualitative tests.
20 - NeuRSS: Enhancing AUV Localization and Bathymetric Mapping With Neural Rendering for Sidescan SLAM.
Author: Yiping Xie , Jun Zhang, Nils Bore, and John Folkesson Implicit neural representations and neural rendering help estimate bathymetry from sidescan sonar data. The accuracy is affected by the positioning of underwater vehicles. This article proposes a framework, NeuRSS, that improves localization and bathymetric mapping using dead reckoning and loop closures. It includes an initial bathymetric estimate from sidescan sonar to guide further optimization. The approach is validated with large surveys from both surface vessels and AUVs, comparing results to ground truth and multibeam echo sounders.
23 - Monitoring dispersal patterns sea foam injected by offshore platforms using satellite optical multispectral imagery.
Authors: M. Tedeschi, F. Filipponi, M. Picone, A. Grillo, M. Gabellini, G. Trinchera Liquefied natural gas regasification plants can use seawater to convert gas from liquid to gas. An initiative was launched to identify "foams" created during this process using satellite data. The sea foam detection procedure involves cloud masking, mask refinement, detection, and spatial filtering. Results from 2015-2022 demonstrated the procedure's effectiveness in identifying sea foam dispersal patterns, which can be applied to other remote sensing imagery.
24 - RUSNet: Robust fish segmentation in underwater videos based on adaptive selection of optical flow
Authors: Peng Zhang, Zongyi Yang, Hong Yu, Wan Tu, Chencheng Gao, and Yue Wang Fish segmentation in underwater videos is important for tracking fish populations and surveying fishery resources. Researchers have worked on improving this process using underwater optical flow. However, existing methods struggle with non-fish motions, prompting the development of RUSNet, a new segmentation network. This model includes a quality evaluation module for optical flow, a decoder to refine fish localization, and a selective fusion method for predictions. Testing showed RUSNet outperformed current models, offering a reliable solution for fish segmentation in challenging underwater environments.
28 - Cooperative SLAM Algorithm for Multi-AUV Underwater Exploration and Mapping
Author: Rongren Wu SLAM (Simultaneous Localisation and Mapping) is crucial for mapping unknown deep-sea areas. This paper suggests an AUV cluster SLAM algorithm to enhance mapping efficiency. It has three parts: (1) a multi-beam sonar image processing algorithm that filters dynamic points and redundant information; (2) SLAM using data from DVL, IMU, and DM based on a Rao-Blackwellised particle filter; and (3) an iUSBL system for cooperative positioning of AUVs. This algorithm significantly improves mapping efficiency and stabilizes SLAM performance by reducing errors from dynamic points.
27 - An overview of industrial image segmentation using deep learning models.
Authors: Guina Wang, Zhen Li, Guirong Weng, Yiyang Chen Image segmentation is important in AI and computer vision, used for tasks like industrial picking and video surveillance. This review covers traditional and deep learning segmentation algorithms, including their integration. It discusses various label frameworks and compares methods on benchmark datasets. Lastly, it addresses challenges and opportunities for future research.
25 - Infrared and visible image fusion based on multi-level detail enhancement and generative adversarial network.
Authors: Xiangrui Tian, Xiaohan Xianyu, Zhimin Li, Tong Xu, Yinjun Jia Infrared and visible image fusion technology is useful in target detection and tracking. Current methods often miss the connection between local and global features, leading to image blurring and loss of detail. This paper proposes a GAN-based method that enhances edge information and improves feature extraction. Experimental results show better texture detail and superior performance compared to other methods.
26 - Remote Awareness of Image Quality for Multi-week Shore- launched AUV Surveys
Authors: Adrian Bodenmann, Daniel O.B. Jones, Alexander B. Phillips, Robert Templeton, Rashiid Sherif, Francesco Fanelli, Darryl Newborough, and Blair Thornton Visual seafloor imaging using autonomous underwater vehicles (AUVs) is now a common method for mapping and monitoring. This research creates a new way to assess image quality on-site during a 21-day AUV campaign. AUV operators received quality scores via satellite to improve data collection.