IEEE Journal of Oceanic Engineering最新文献

{"title":"MLMFFNet: Multilevel Mixed Feature Fusion Network for Real-Time Forward-Looking Sonar Image Segmentation","authors":"Haotian Zheng;Yushan Sun;Hao Xu;Liwen Zhang;Yatong Han;Shuguang Cui;Zhen Li","doi":"10.1109/JOE.2025.3529132","DOIUrl":"https://doi.org/10.1109/JOE.2025.3529132","url":null,"abstract":"Forward-looking sonar is a critical tool for underwater target detection, and segmentation is an essential component of forward-looking sonar image processing. Accurate segmentation of sonar images is vital, but the complexity of the underwater environment introduces challenges, such as low resolution, significant noise, and blurred target edges. These factors make real-time, precise segmentation particularly difficult. To address these challenges, we propose a novel real-time segmentation network, the multilevel mixed feature fusion network (MLMFFNet), specifically designed for forward-looking sonar images. Our approach leverages a unique MFF module and a multiscale MFF module to extract both local and contextual information using deep convolutional networks, dilated convolutions, and partial convolution combinations for effective information integration. Additionally, we incorporate a context connection module to enhance feature fusion by utilizing high-level contextual information. To further improve accuracy, we introduce three weighted loss functions designed to address imbalanced sample distributions and blurred boundaries. Experimental evaluations on two distinct forward-looking sonar data sets demonstrate that MLMFFNet significantly outperforms many state-of-the-art general and sonar-specific semantic segmentation networks, delivering superior segmentation accuracy while maintaining real-time performance.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 2","pages":"1356-1369"},"PeriodicalIF":3.8,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Few-Shot Underwater Acoustic Target Recognition Using Domain Adaptation and Knowledge Distillation","authors":"Xiaodong Cui;Zhuofan He;Yangtao Xue;Peican Zhu;Jing Han;Xuelong Li","doi":"10.1109/JOE.2025.3532036","DOIUrl":"https://doi.org/10.1109/JOE.2025.3532036","url":null,"abstract":"The complex dynamics of the marine environment pose substantial challenges for underwater acoustic target recognition (UATR) systems, especially when there are limited training samples. However, existing image-based few-shot learning methods might not be applicable, mainly because they fail to capture the temporal and spectral features from acoustic targets and lack the competent domain adaptation ability due to the inefficient usage of base samples. In this article, we develop a novel Domain Adaptation-based Attentional Time–Frequency few-shot recognition method (DAATF) for underwater acoustic targets. The DAATF explicitly utilizes a self-attention-based feature extractor to capture the time–frequency structural dependencies and constructs an autoencoder-based domain adapter to improve the cross-domain knowledge transfer through reusing the base dataset. In addition, a knowledge distillation module is designed to enable the model to reserve the general feature extraction ability of the pretrained network to avoid overfitting. Extensive experiments are conducted to assess prediction accuracy, noise robustness, and cross-domain adaptation. The obtained results validate that the DAATF can achieve outstanding performance, demonstrating its great potential for practical UATR applications. Furthermore, we provide free and open access to the DanShip data set.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 2","pages":"637-653"},"PeriodicalIF":3.8,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ROV Localization Using Ballasted Umbilical Equipped With IMUs","authors":"Juliette Drupt;Christophe Viel;Claire Dune;Vincent Hugel","doi":"10.1109/JOE.2024.3467448","DOIUrl":"https://doi.org/10.1109/JOE.2024.3467448","url":null,"abstract":"This article describes an affordable and setup-friendly cable-based localization technique for underwater remotely operated vehicles, which exploits the piecewise linear shape of the umbilical being equipped with a sliding ballast. Each stretched part of the cable is instrumented with a waterproof inertial measurement unit (IMU) to measure its orientation. Using the cable's geometry, the vehicle's location can be calculated in relation to the fixed or moving end of the cable. Experiments carried out with a robotic system in a water tank prove the reliability of this localization strategy. The study investigates the influence of measurement uncertainties on cable orientation and length, as well as the impact of the IMU location along the cable on localization precision. The accuracy of the localization method is discussed.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 2","pages":"1045-1064"},"PeriodicalIF":3.8,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Adaptive Sampling of Marine Robots in Ocean Observation: An Overview","authors":"Xiaojuan Ma;Yanhui Wang","doi":"10.1109/JOE.2025.3529087","DOIUrl":"https://doi.org/10.1109/JOE.2025.3529087","url":null,"abstract":"It is a costly and time-consuming practice to achieve ocean observation with sufficient spatial and temporal resolution. Luckily, it can be more efficient and effective by applying marine robots with adaptive sampling. The ocean environment and its uncertainties can be predicted during sampling to make planning of autonomous sensing for future operations of the marine robot. This article reviews various methods of adaptive sampling as well as robot path planning, weighing the benefits and drawbacks of each. In addition, three primary aspects of adaptive sampling are summarized: adaptive sampling architecture, multirobot sampling, and the dimensionality problem. The operation practice of adaptive sampling approaches in real applications is also investigated. Future trends for adaptive sampling of marine robots are also discussed to conclude several research directions that are not fully developed or remain unexplored, which will aid future studies.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 2","pages":"1103-1126"},"PeriodicalIF":3.8,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Chaotic Initialization Particle Filter AUV Cluster Position Calibration Algorithm Based on Intragroup Distance Measurement Under Large Initial Position Error","authors":"Qingyu Zhang;Jin Fu;Nan Zou;Bin Qi;Yuan Hang Fan","doi":"10.1109/JOE.2024.3516096","DOIUrl":"https://doi.org/10.1109/JOE.2024.3516096","url":null,"abstract":"With the increasing diversity and complexity of maritime mission requirements, the technology of collaborative multiautonomous underwater vehicles (AUVs) has garnered widespread attention. In this domain, the positional calibration technology of AUV clusters is an integral aspect that cannot be overlooked. Traditional leader–follower AUV cluster positional calibration models and algorithms have utilized information from either a single leader AUV or multiple leader AUVs in conjunction with a single follower AUV. However, with the expansion of the scale of follower AUVs, the availability of follower–follower AUV information increases. Consequently, this article develops a novel AUV cluster positional calibration model that leverages both the distance information between leader and follower AUVs, and the follower–follower AUV distance information. The observability of this model is analyzed, and building upon this, a chaos-initialized particle filter algorithm for AUV cluster positional calibration is proposed. Finally, experiments are conducted to compare the performance of the algorithm presented in this article with the particle filtering algorithm under different initial error conditions. The results demonstrate that the proposed algorithm exhibits stable convergence speed and calibration error at low initial errors. At high initial errors, it achieves faster convergence, lower calibration error within a finite time, and enhanced stability.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 2","pages":"1140-1152"},"PeriodicalIF":3.8,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Floating Offshore Wind Turbine Optimized Control for Power Regulation With Experimental Validation","authors":"Seydali Ferahtia;Azeddine Houari;Mohamed Machmoum;Mohammad Rasool Mojallizadeh;Mourad Ait-Ahmed;Félicien Bonnefoy","doi":"10.1109/JOE.2024.3520365","DOIUrl":"https://doi.org/10.1109/JOE.2024.3520365","url":null,"abstract":"This article proposes a new strategy for blade pitch control to regulate power production while alleviating the negative effects of the structural motions of floating offshore wind turbines (FOWTs). FOWTs frequently experience significant fluctuations in rotor speed when wind speed is above its rated value in the presence of significant wave heights. This condition reduces the power quality while amplifying the fatigue loads, which can result in damage to the generator. To address this problem, designers frequently use simplified models to design controllers, such as the gain-scheduled proportional integral (GSPI) controller. These models can demonstrate the nonlinear coupling of the platform motions and the rotor speed. However, their performance is limited due to the chosen linearization points. This article proposes an optimal design method based on metaheuristic algorithms. These algorithms treat the system as a black box, allowing for control parameter tuning considering all degrees of freedom, such as those provided by OpenFAST. The Red Tailed Hawk (RTH) Algorithm is used to create an optimized GSPI controller (RTH-GSPI) that maintains power while minimizing platform motion. Consequently, the performance is significantly enhanced. Numerical simulations using co-simulation between MATLAB and OpenFAST, along with experimental validation using an FOWT prototype, have verified the suggested technique's efficiency.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 2","pages":"1231-1243"},"PeriodicalIF":3.8,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HG2former: HSV-Gamma Guided Transformers for Efficient Underwater Image Enhancement","authors":"Yuhao Qing;Liquan Shen;Zhijun Fang;Yueying Wang","doi":"10.1109/JOE.2024.3525150","DOIUrl":"https://doi.org/10.1109/JOE.2024.3525150","url":null,"abstract":"Due to optical phenomena, such as the absorption and scattering of light in underwater environments, underwater images often suffer from degradation in color, contrast, clarity, and noise. Existing deep learning-based methods for underwater image enhancement typically learn a direct mapping from low-quality to high-quality underwater images, without fully considering the mapping of local luminance, chrominance, and contrast features. In this article, we propose a transformer model guided hue, saturation, value (HSV) and gamma correction for underwater image enhancement. The HG2former combines the HSV color model and gamma correction techniques to isolate the three fundamental characteristics of color, providing rich, differentiated enhancement for both color and contrast in underwater images. In addition, nonlinear gamma correction adaptively adjusts the brightness and contrast of images, addressing issues of visibility reduction and color distortion in underwater imaging. Furthermore, we introduce a meticulously designed encoder–decoder structure, along with an improved multihead self-attention module, to capture the spatial distribution patterns of underwater images while modeling both local and long-range dependencies. Extensive experimental results on multiple data sets demonstrate that the proposed HG2former outperforms other state-of-the-art methods.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 2","pages":"866-878"},"PeriodicalIF":3.8,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Velocity Form Model Predictive Control of an Autonomous Underwater Vehicle","authors":"Isah A. Jimoh;Hong Yue","doi":"10.1109/JOE.2024.3519680","DOIUrl":"https://doi.org/10.1109/JOE.2024.3519680","url":null,"abstract":"This article presents a model-predictive control (MPC) scheme to achieve 3-D trajectory tracking control and point stabilization of an autonomous underwater vehicle (AUV) subject to environmental disturbances. The AUV is modeled as a coupled nonlinear system. The control scheme is developed using a linear parameter-varying formulation of the nonlinear model in velocity form to obtain an optimization control problem with efficient online solvers and does not require model augmentation that can potentially increase computational efforts. The control strategy inherently provides offset-free control when tracking piecewise constant reference signals, ensures feasibility for trajectories containing unreachable points, and is relatively simple to implement, as parameterization of all equilibria is not required. A simple switching law is proposed for task switching between the 3-D trajectory tracking and point stabilization. The MPC is designed to ensure the closed-loop stability of the vehicle in both motion control tasks via the imposition of terminal constraints. Through simulations of the coupled nonlinear Naminow-D AUV under ocean current and wave disturbances, the effectiveness of the control strategy in trajectory tracking and point stabilization is demonstrated.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 2","pages":"1127-1139"},"PeriodicalIF":3.8,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HKCoral: Benchmark for Dense Coral Growth Form Segmentation in the Wild","authors":"Ziqiang Zheng;Haixin Liang;Fong Hei Wut;Yue Him Wong;Apple Pui-Yi Chui;Sai-Kit Yeung","doi":"10.1109/JOE.2024.3494112","DOIUrl":"https://doi.org/10.1109/JOE.2024.3494112","url":null,"abstract":"Underwater coral reef monitoring plays an important role in the maintenance and protection of the underwater ecosystem. Extracting information from the collected coral reef images and videos based on computer vision techniques has recently gained increasing attention. Semantic segmentation, which assigns semantic category information to each pixel in images, has been introduced to understand coral reefs. Satisfactory semantic segmentation performance has been achieved based on large-scale in-air data sets with densely labeled annotations. However, underwater coral reef understanding is less explored and existing underwater coral reef data sets are mainly captured under <italic>ideal</i> and <italic>normal</i> conditions and lack variance. They cannot fully reflect the diversity and properties of coral reefs. Thus, trained coral reef segmentation models show very limited performance when deployed in <italic>practical</i>, <italic>challenging</i>, and <italic>adverse</i> conditions. To address these issues, in this article, we propose an <italic>in-the-wild</i> coral reef data set named <italic>HKCoral</i> to close the gap for performing in-situ coral reef monitoring. The collected data set with dense pixel-wise annotations possesses larger diversity, appearance, viewpoint, and visibility variations. Besides, we adopt the fundamental coral <italic>growth form</i> as the foundation of our semantic coral reef segmentation, which enables a strong generalizability to unseen coral reef images from different sites. We benchmark the coral reef segmentation performance of 17 state-of-the-art semantic segmentation algorithms (including the recent generalist segment anything model) and further introduce a complementary architecture to better utilize underwater image enhancement for improving the segmentation performance of models. We have conducted extensive experiments based on various up-to-date segmentation models on our benchmark and the experimental results demonstrate that there is still ample room to improve coral segmentation performance. Ablation studies and discussions are also included. The proposed benchmark could significantly enhance the efficiency and accuracy of real-world underwater coral reef surveying.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 2","pages":"697-713"},"PeriodicalIF":3.8,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10904825","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CE$^{3}$USOD: Channel-Enhanced, Efficient, and Effective Network for Underwater Salient Object Detection","authors":"Qingyao Wu;Jiaxin Xie;Zhenqi Fu;Xiaotong Tu;Yue Huang;Xinghao Ding","doi":"10.1109/JOE.2024.3523356","DOIUrl":"https://doi.org/10.1109/JOE.2024.3523356","url":null,"abstract":"Underwater salient object detection (USOD) aims to identify the most crucial elements in underwater environments, holding significant potential for underwater exploration. Existing methods often overlook light degradation or involve larger network sizes, which are unsuitable for underwater mobile platforms and pose challenges to implement in practice. Given the importance of low-complexity algorithms in underwater applications to optimize system efficiency, this article introduces CE<inline-formula><tex-math>$^{3}$</tex-math> </inline-formula>USOD—an efficient network tailored to deliver an effective solution for salient object detection in underwater scenarios. On the one hand, we reconsider long-range dependencies and feature computation from a neighborhood perspective, leading to the development of the long-range context-aware module. Specifically, we approximate local and global context awareness by incorporating the maximum and average values of neighboring pixels within varying window sizes, which allows our method to achieve high performance while maintaining low computational cost. On the other hand, light scattering and absorption during underwater imaging frequently result in channel intensity imbalances in captured underwater images. To address this, we propose the color-guided pyramid aggregation module, which utilizes the weaker color channels enhanced by underwater image enhancement techniques as guiders for multiscale feature fusion, finally facilitating the model to obtain underwater domain information. Extensive experiments on four public benchmarks demonstrate that our innovative network achieves state-of-the-art results while maintaining a low model size (Params of 0.546M) and computational complexity (FLOPs of 0.416G). Therefore, CE<inline-formula><tex-math>$^{3}$</tex-math> </inline-formula>USOD proves to be effective and efficient, establishing its practicality, particularly for underwater applications.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 2","pages":"941-954"},"PeriodicalIF":3.8,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}