IEEE Journal of Oceanic Engineering最新文献_第2页

Robust Exact-Time Trajectory Tracking Control for Autonomous Surface Vessels 自主水面舰艇的鲁棒实时轨迹跟踪控制

IF 3.8 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2025-03-24 DOI: 10.1109/JOE.2025.3529062

Susan Basnet;Saurabh Kumar;Shashi Ranjan Kumar

引用次数: 0

Multiangle Sonar Imaging for 3-D Reconstruction of Underwater Objects in Shadowless Environments 多角度声纳成像在无影环境下水下目标的三维重建

IF 3.8 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2025-03-21 DOI: 10.1109/JOE.2025.3535563

Zhijie Tang;Yang Li;Chi Wang

{"title":"Multiangle Sonar Imaging for 3-D Reconstruction of Underwater Objects in Shadowless Environments","authors":"Zhijie Tang;Yang Li;Chi Wang","doi":"10.1109/JOE.2025.3535563","DOIUrl":"https://doi.org/10.1109/JOE.2025.3535563","url":null,"abstract":"In the realm of underwater detection technologies, reconstructing the three-dimensional structure of underwater objects is crucial for applications such as underwater target tracking, target locking, and navigational guidance. As a primary tool for underwater detection, acoustical imaging faces significant challenges in recovering the three-dimensional structure of objects from two-dimensional images. Current 3-D reconstruction methods mainly focus on reconstructing objects at the riverbed, overlooking the reconstruction of objects in the water in the absence of shadows. This study introduces a multiangle shape and height recovery method for such specific situations. By fixing the sonar detection angle and utilizing ViewPoint software to measure the contours of objects at different depths, a superimposition technique for two-dimensional sonar images was developed to achieve three-dimensional reconstruction of shadowless sonar image data. The proposed method is specifically designed for scenarios with diffuse echoes, where the sound waves scatter from rough surfaces rather than reflect specularly from smooth surfaces. This limitation ensures the method's applicability to objects lacking strong mirror-like reflections. This technique has been validated on three different categories of targets, with the reconstructed 3-D models accurately compared to the actual size and shape of the targets, demonstrating the method's effectiveness and providing a theoretical and methodological foundation for the 3-D reconstruction of underwater sonar targets.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 2","pages":"1344-1355"},"PeriodicalIF":3.8,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852452","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}

引用次数: 0

Oceanic 3-D Thermohaline Field Reconstruction With Multidimensional Features Using SABNN 基于SABNN的多维特征海洋三维温盐场重建

IF 3.8 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2025-03-21 DOI: 10.1109/JOE.2025.3535591

Juan Li;Yajie Bai;Xuerong Cui;Lei Li;Bin Jiang;Shibao Li;Jungang Yang

{"title":"Oceanic 3-D Thermohaline Field Reconstruction With Multidimensional Features Using SABNN","authors":"Juan Li;Yajie Bai;Xuerong Cui;Lei Li;Bin Jiang;Shibao Li;Jungang Yang","doi":"10.1109/JOE.2025.3535591","DOIUrl":"https://doi.org/10.1109/JOE.2025.3535591","url":null,"abstract":"Aiming at the problems of missing data and outliers in ocean observations and incomplete characterization of thermohaline related features, a 3-D thermohaline reconstruction model of the ocean based on multisource data are proposed. Multisource data from remote sensing and Current and Pressure recording Inverse Echo Sounders were used to analyze the projection relationship between 12-D features, such as sea surface temperature, bidirectional propagation time, and seafloor current velocity, and the distribution of ocean temperature and salinity at different depths (10–1000 m). A Bayesian optimization algorithmic framework is used to evaluate and gradually remove uncertainty from currently known data during the iterative process by extracting network parameters from the approximate probability distribution. More informed decision making improves the stability of the iterative process and reconstruction. In addition, a self-attention mechanism is introduced to dynamically focus on the dependencies between features of different dimensions by calculating the correlation matrix between features at arbitrary locations, enabling the model to more comprehensively characterize the thermohaline distribution and its changes. A Self-attentive Bayesian neural network (SABNN) model is established through empirical regression. The reconstructed model is validated using observational data from the Gulf of Mexico, and the experimental results show that the SABNN model has a significant improvement in temperature and salinity reconstruction accuracy compared with other network models or methods, with the RMSE and <inline-formula><tex-math>$R^{2}$</tex-math></inline-formula> improved by more than 29.68%, 21.14% and 31.01%, 37.33%, respectively.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 2","pages":"1273-1289"},"PeriodicalIF":3.8,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852391","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}

引用次数: 0

AO-UOD: A Novel Paradigm for Underwater Object Detection Using Acousto–Optic Fusion AO-UOD：一种基于声光融合的水下目标探测新范式

IF 3.8 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2025-03-21 DOI: 10.1109/JOE.2025.3529121

Fengxue Yu;Fengqi Xiao;Congcong Li;En Cheng;Fei Yuan

{"title":"AO-UOD: A Novel Paradigm for Underwater Object Detection Using Acousto–Optic Fusion","authors":"Fengxue Yu;Fengqi Xiao;Congcong Li;En Cheng;Fei Yuan","doi":"10.1109/JOE.2025.3529121","DOIUrl":"https://doi.org/10.1109/JOE.2025.3529121","url":null,"abstract":"Autonomous underwater vehicles can carry multiple sensors, such as optical cameras and sonars, providing a common platform for underwater multimodal object detection. High-resolution optical images contain color information but are not applicable to turbid water environments. In contrast, acoustical waves are highly penetrating and travel long distances, making them suitable for low-light, turbid underwater environments, but sonar imaging has low resolution. The combination of the two can play to their respective advantages. This article presents a novel paradigm for underwater object detection using acousto–optic fusion (AO-UOD). Given that there is no publicly available data set, this article first constructs a paired data set for fusing optical and sonar images for underwater object detection. Paired sonar images and optical images were acquired by aligning the simulated plane of the ocean bottom. Based on this, a dual-stream interactive object detection network is designed. The network utilizes the structures of the fusion backbone, dual neck, and dual head to establish cross-modal information interaction between acoustical and optical. The attention interactive twin-branch fusion module is used to realize the fusion between features. Experimental results on the data collected show that AO-UOD can effectively fuse optical and sonar images to achieve robust detection performance. The multimodal method can utilize more information and possesses greater advantages over the unimodal method. This research not only provides a solid theoretical foundation for future multimodal object detection in marine environments but also points out the direction of technology development in practical applications.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 2","pages":"919-940"},"PeriodicalIF":3.8,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848874","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}

引用次数: 0

An Improved YOLOv8-Based Shallow Sea Creatures Object Detection Method 基于yolov8的改进浅海生物目标检测方法

IF 3.8 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2025-03-21 DOI: 10.1109/JOE.2025.3538954

Yan Liu;Yue Zhao;Bin Yu;Changsheng Zhu;Guanying Huo;Qingwu Li

{"title":"An Improved YOLOv8-Based Shallow Sea Creatures Object Detection Method","authors":"Yan Liu;Yue Zhao;Bin Yu;Changsheng Zhu;Guanying Huo;Qingwu Li","doi":"10.1109/JOE.2025.3538954","DOIUrl":"https://doi.org/10.1109/JOE.2025.3538954","url":null,"abstract":"With the development and utilization of marine resources, object detection in shallow sea environments becomes crucial. In real underwater environments, targets are often affected by motion blur or appear clustered, increasing detection difficulty. To address this problem, we propose an improved YOLOv8-based shallow sea creatures object detection method. We integrate receptive-field coordinate attention (RFCA) into the cross-stage partial bottleneck with the two convolutions (C2f) module of YOLOv8, creating the RFCA-enhanced C2f (C2f_RFCA). This enhancement improves feature extraction and fusion by leveraging multiscale receptive fields and refined feature fusion strategies, enabling better detection of blurred and occluded objects. The C2f_RFCA module captures both local and global features, enhancing detection accuracy in complex underwater scenarios. We additionally devised an improved dynamic head by substituting the deformable ConvNets version two (DCNv2) with DCNv3, forming dynamic head with DCNv3. This upgrade increases the flexibility of feature mapping and improves accuracy in detecting densely clustered objects by allowing adaptive receptive fields and enhancing boundary delineation. To evaluate the algorithm performance, we trained it on real-world underwater object detection data sets and conducted generalization experiments on detecting underwater objects, the underwater robot professional competition 2020 and underwater target detection and classification 2020 data sets. Experimental results show that, compared with YOLOv8n, our method increases mAP@0.5 by 1.9%, 1.7%, 4.3%, and 3.3%, and mAP@0.5:0.95 by 2.9%, 2.2%, 3.8%, and 5.0% in the four data sets. The proposed method significantly improves object detection accuracy for organisms in complex marine environments.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 2","pages":"817-834"},"PeriodicalIF":3.8,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848773","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}

引用次数: 0

SCN: A Novel Underwater Images Enhancement Method Based on Single Channel Network Model 基于单通道网络模型的水下图像增强新方法

IF 3.8 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2025-03-20 DOI: 10.1109/JOE.2024.3474924

Fuheng Zhou;Siqing Zhang;Yulong Huang;Pengsen Zhu;Yonggang Zhang

{"title":"SCN: A Novel Underwater Images Enhancement Method Based on Single Channel Network Model","authors":"Fuheng Zhou;Siqing Zhang;Yulong Huang;Pengsen Zhu;Yonggang Zhang","doi":"10.1109/JOE.2024.3474924","DOIUrl":"https://doi.org/10.1109/JOE.2024.3474924","url":null,"abstract":"Light is absorbed, reflected, and refracted in an underwater environment due to the interaction between water and light. The red and blue channels in an image are attenuated due to these interactions. The red, green, and blue channels are typically employed as inputs for deep learning models, and the color casts, which result from different attenuation rates of the three channels, may affect the model's generalization performance. Besides, the color casts existing in the reference images will impact the deep-learning models. To address these challenges, a single channel network (SCN) model is introduced, which exclusively employs the green channel as its input, and is unaffected by the attenuations in the red and blue channels. An innovative feature processing module is presented, in which the characteristics of transformers and convolutional layers are fused to capture nonlinear relationships among the red, green, and blue channels. The public EUVP and LSUI data set experiments show that the proposed SCN model achieves competitive results with the existing best three channel models for the case of slight signal attenuation, and outperforms the existing state of arts three-channel models for the case of strong signal attenuation. Furthermore, the proposed model is trained on the self-built noncolor biased underwater image data set and is also tested on the public UCCS data set with three different types of color casts, whose experimental results exhibit balanced color distribution.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 2","pages":"758-775"},"PeriodicalIF":3.8,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848808","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}

引用次数: 0

Toward Real-World Applicability: Lightweight Underwater Acoustic Localization Model Through Knowledge Distillation 面向现实应用：基于知识精馏的轻量级水声定位模型

IF 3.8 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2025-03-20 DOI: 10.1109/JOE.2025.3538928

Runze Hu;Xiaohui Chu;Daowei Dou;Xiaogang Liu;Yining Liu;Bingbing Qi

{"title":"Toward Real-World Applicability: Lightweight Underwater Acoustic Localization Model Through Knowledge Distillation","authors":"Runze Hu;Xiaohui Chu;Daowei Dou;Xiaogang Liu;Yining Liu;Bingbing Qi","doi":"10.1109/JOE.2025.3538928","DOIUrl":"https://doi.org/10.1109/JOE.2025.3538928","url":null,"abstract":"Deep learning (DL) approaches in underwater acoustic localization (UAL) have gained a great deal of popularity. While numerous works are devoted to improving the localization precision, they neglect another critical challenge inherent in the DL-based UAL problem, i.e., the model's practicality. Advanced DL models generally exhibit extremely high complexity, requiring a large amount of computational resources and resulting in slow inference time. Unfortunately, the limited processing power and real-time demands in oceanic applications make the deployment of complex DL models exceedingly challenging. To address this challenge, this article proposes a lightweight UAL framework based on knowledge distillation (KD) techniques, which effectively reduces the size of a deep UAL model while maintaining competitive performance. Specifically, a dedicated teacher network is designed using attention mechanisms and convolutional neural networks (CNNs). Then, the KD is performed to distill the knowledge from the teacher network into a lightweight student model, such as a three-layer CNN. In practical deployment, only the lightweight student model will be utilized. With the proposed lightweight framework, the student model has 98.68% fewer model parameters and is 87.4% faster in inference time compared to the teacher network, while the prediction accuracy drops to only 1.07% (97.55% <inline-formula><tex-math>$rightarrow$</tex-math></inline-formula> 96.48%). In addition, the generalization ability of the student model is examined through transfer learning, where the model is transferred between two different ocean environments. The student model demonstrates a stronger generalization ability compared to the model without the KD process, as it can quickly adapt itself to a new application environment using just 10% of the data.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 2","pages":"1429-1442"},"PeriodicalIF":3.8,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852450","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}

引用次数: 0

A New Active Sonar Detector Based on Beamformed Deep Neural Network 一种基于波束形成深度神经网络的新型主动声呐探测器

IF 3.8 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2025-03-18 DOI: 10.1109/JOE.2025.3535597

Cong Peng;Lei Wang;Juncheng Gao;Shuhao Zhang;Haoran Ji

{"title":"A New Active Sonar Detector Based on Beamformed Deep Neural Network","authors":"Cong Peng;Lei Wang;Juncheng Gao;Shuhao Zhang;Haoran Ji","doi":"10.1109/JOE.2025.3535597","DOIUrl":"https://doi.org/10.1109/JOE.2025.3535597","url":null,"abstract":"This article proposes a new active sonar detector based on a beamformed deep neural network (BDNN) in three steps. The process involves a preprocessing step, a deep neural network (DNN) application step, and a subsequent postprocessing step. In the preprocessing step, partial spectra are extracted from multiple directions through frequency-domain beamforming. These partial spectra from different directions serve as DNN input, yielding estimated target probabilities as output in the DNN application step. In the postprocessing step, a multiframe probability multiplication technique is proposed, and the number of frames is determined adaptively. The proposed BDNN generates a gridded azimuth-distance graph, where each grid cell represents the probability of a target's presence at a specific azimuth and distance. To guarantee real-time application, we also propose a graphics processing unit based parallel acceleration method, which increases the computation speed of the beamforming process by nearly two orders of magnitude compared to the CPU. The proposed BDNN is verified through sea and lake trials. The results demonstrate that the proposed BDNN achieves better detection performance compared to the conventional matched filter method and exhibits remarkable generalization capabilities.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 2","pages":"1370-1386"},"PeriodicalIF":3.8,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852432","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}

引用次数: 0

Trajectory Tracking Control for a Hybrid Underwater Vehicle in Free-Flying and Crawling Operation Modes 自由飞行和爬行混合动力水下航行器的轨迹跟踪控制

IF 3.8 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2025-03-11 DOI: 10.1109/JOE.2024.3519679

Qi Chen;Chengjun Ming;Guoyang Qin;Daqi Zhu

{"title":"Trajectory Tracking Control for a Hybrid Underwater Vehicle in Free-Flying and Crawling Operation Modes","authors":"Qi Chen;Chengjun Ming;Guoyang Qin;Daqi Zhu","doi":"10.1109/JOE.2024.3519679","DOIUrl":"https://doi.org/10.1109/JOE.2024.3519679","url":null,"abstract":"A hybrid underwater vehicle (HUV) equipped with thrusters and tracks has the great ability of free flying in the water and crawling on the surfaces of underwater structures, making it highly effective for inspecting underwater structures and cleaning hulls. In this article, a novel cascade control strategy that consists of a kinematic controller and a dynamic controller is proposed for trajectory tracking control of HUVs in free-flying and crawling operation modes. Based on the tracking error, a model predictive control (MPC)-based kinematic controller is designed for both free-flying and crawling modes. To improve the tracking accuracy, an improved snake optimizer is used in the optimization process of MPC to derive the expected optimal velocity. Then, the error between the expected optimal velocity and the real velocity is used as the input of the dynamic controller. To compensate for external disturbances, such as ocean currents and waves, a dynamic controller composed of a nonlinear disturbance observer and an integral sliding mode control (ISMC) is adopted to optimize the thrust force for trajectory tracking in free-flying mode. In addition, a dynamic controller composed of a radial basis function neural network and an ISMC is established to reduce the impact of slipperiness in crawling mode. The simulation results show that the proposed cascade trajectory tracking control strategy for HUVs in free-flying and crawling modes can improve the trajectory tracking accuracy and robustness to unknown dynamic factors.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 2","pages":"1001-1014"},"PeriodicalIF":3.8,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848873","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}

引用次数: 0

Cross-Domain Underwater Sound Source Localization Algorithm Based on Binaural Matrix and Mutual Information Constraint Loss 基于双耳矩阵和互信息约束损失的跨域水声声源定位算法

IF 3.8 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2025-03-10 DOI: 10.1109/JOE.2024.3516204

Ruwei Li;Man Li;Qiuyan Li;Jiangqiao Li

{"title":"Cross-Domain Underwater Sound Source Localization Algorithm Based on Binaural Matrix and Mutual Information Constraint Loss","authors":"Ruwei Li;Man Li;Qiuyan Li;Jiangqiao Li","doi":"10.1109/JOE.2024.3516204","DOIUrl":"https://doi.org/10.1109/JOE.2024.3516204","url":null,"abstract":"The accuracy of existing underwater sound source localization algorithms is unsatisfactory, and most of them cannot achieve cross-domain localization. To solve these problems, a cross-domain underwater sound source localization algorithm based on a binaural matrix and mutual information constraint loss is proposed. In this algorithm, a new binaural matrix feature is first extracted based on binaural cues, which is less susceptible to environmental interference and can obtain reliable direction information from received signals. Then, a constrained loss based on mutual information is designed to constrain the proposed neural network to accurately learn the shared representations of different domains. This ensures that the high-dimensional representations used for localization have more explicit orientation directionality. Finally, a cross-domain underwater sound source localization network is constructed to achieve accurate cross-domain localization. Experimental results indicate that the algorithm proposed in this study has a higher localization accuracy than comparative algorithms, both in the same domain and in different domains.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 2","pages":"1419-1428"},"PeriodicalIF":3.8,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852371","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}

引用次数: 0