IEEE Journal of Oceanic Engineering最新文献_第6页

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

The MBARI Low-Altitude Survey System for 1-cm-Scale Seafloor Surveys in the Deep Ocean MBARI低空测量系统用于深海1厘米尺度海底测量

IF 3.8 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2025-03-18 DOI: 10.1109/JOE.2024.3521256

David W. Caress;Eric J. Martin;Michael Risi;Giancarlo Troni;Andrew Hamilton;Chad Kecy;Jennifer B. Paduan;Hans J. Thomas;Stephen M. Rock;Monica Wolfson-Schwehr;Richard Henthorn;Brett Hobson;Larry E. Bird

{"title":"The MBARI Low-Altitude Survey System for 1-cm-Scale Seafloor Surveys in the Deep Ocean","authors":"David W. Caress;Eric J. Martin;Michael Risi;Giancarlo Troni;Andrew Hamilton;Chad Kecy;Jennifer B. Paduan;Hans J. Thomas;Stephen M. Rock;Monica Wolfson-Schwehr;Richard Henthorn;Brett Hobson;Larry E. Bird","doi":"10.1109/JOE.2024.3521256","DOIUrl":"https://doi.org/10.1109/JOE.2024.3521256","url":null,"abstract":"The Monterey Bay Aquarium Research Institute has developed a low-altitude survey system (LASS) to conduct cm-scale seafloor surveys of complex terrain in the deep ocean. The LASS is integrated with a remotely operated vehicle (ROV), which is operated at a 3-m standoff to obtain 5-cm-lateral-resolution bathymetry using a multibeam sonar, 1-cm-resolution bathymetry using a wide-swath lidar laser scanner, and 2-mm/pixel resolution color photography using stereo still cameras illuminated by strobes. Surveys are typically conducted with 3-m line spacing and 0.2-m/s speed and executed autonomously by the ROV. The instrument frame actively rotates to keep the sensors oriented normal to the seafloor. The strobe lights, mounted on swing arms on either side of the ROV, similarly rotate to face the seafloor. Areas of 120 m × 120 m can be covered in about 8 h. Example surveys include 1) deep-sea soft coral and sponge communities from Sur Ridge, offshore Central California; 2) a warm venting site hosting thousands of brooding octopus near Davidson Seamount, also offshore Central California; and 3) a high-temperature hydrothermal vent field on Axial Seamount, on the Juan de Fuca Ridge. An advantage of combining optical and acoustic remote sensing is that the lidar and cameras map soft animals, while the multibeam sonar maps the solid seafloor. The long-term goal is to field these sensors from a hover-capable autonomous platform rather than ROVs, enabling efficient 1-cm-scale seafloor surveys in the deep ocean.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 3","pages":"1573-1584"},"PeriodicalIF":3.8,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10931848","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144646563","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}

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

Improved Calculation of the Second-Order Ocean Doppler Spectrum for Sea State Inversion 海况反演中二阶海洋多普勒谱的改进计算

IF 3.8 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2025-03-16 DOI: 10.1109/JOE.2025.3550985

Charles-Antoine Guérin

引用次数: 0

Classification of Imaging Artifacts in Synthetic Aperture Sonar With Bayesian Deep Learning 基于贝叶斯深度学习的合成孔径声呐成像伪影分类

IF 3.8 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2025-03-16 DOI: 10.1109/JOE.2025.3538948

Marko Orescanin;Derek Olson;Brian Harrington;Marc Geilhufe;Roy Edgar Hansen;Dalton Duvio;Narada Warakagoda

{"title":"Classification of Imaging Artifacts in Synthetic Aperture Sonar With Bayesian Deep Learning","authors":"Marko Orescanin;Derek Olson;Brian Harrington;Marc Geilhufe;Roy Edgar Hansen;Dalton Duvio;Narada Warakagoda","doi":"10.1109/JOE.2025.3538948","DOIUrl":"https://doi.org/10.1109/JOE.2025.3538948","url":null,"abstract":"Synthetic aperture sonar (SAS) provides high-resolution underwater imaging but can suffer from artifacts due to environment or navigation errors. This work explores Bayesian deep learning for classifying common imaging artifacts while quantifying model reliability. We introduce a novel labeled data set with simulated imaging errors through controlled beamforming perturbations. Two Bayesian neural network variants, Monte Carlo dropout and flipout, were trained on this data to detect three artifacts induced by: sound speed errors, yaw attitude error, and additive noise. Results demonstrate these methods accurately classify artifacts in SAS imagery while producing well-calibrated uncertainty estimates. Uncertainty tends to be higher for uniform seafloor textures where artifacts are harder to perceive, and lower for richly textured environments. Analyzing uncertainty reveals regions likely to be misclassified. By discarding 20% of the most uncertain predictions, classification improves from 0.92 F<inline-formula><tex-math>$_{1}$</tex-math></inline-formula>-score to 0.98 F<inline-formula><tex-math>$_{1}$</tex-math></inline-formula>-score. Overall, the Bayesian approach enables uncertainty-aware perception, boosting model reliability—an essential capability for real-world autonomous underwater systems. This work establishes Bayesian deep learning as a robust technique for uncertainty quantification and artifact detection in SAS.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 3","pages":"2280-2295"},"PeriodicalIF":3.8,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144646576","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

Model-Guided Deep Learning for Line Segment Detection in Time–Frequency Spectrograms of an Ocean Waveguide 海洋波导时频谱图线段检测的模型引导深度学习

IF 3.8 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2025-03-16 DOI: 10.1109/JOE.2025.3548665

Jongkwon Choi;Youngmin Choo;Geunhwan Kim;Wooyoung Hong;Keunhwa Lee

{"title":"Model-Guided Deep Learning for Line Segment Detection in Time–Frequency Spectrograms of an Ocean Waveguide","authors":"Jongkwon Choi;Youngmin Choo;Geunhwan Kim;Wooyoung Hong;Keunhwa Lee","doi":"10.1109/JOE.2025.3548665","DOIUrl":"https://doi.org/10.1109/JOE.2025.3548665","url":null,"abstract":"The application of machine learning in underwater acoustics is often limited by the lack of high-quality data. One method to avoid this data issue is to use modeled data to train a machine learning algorithm, called model-guided learning. In this study, a U-Net-based model-guided deep learning approach was developed to identify dispersion curves in an oceanic waveguide. The U-Net is trained using supervised learning with modeled data generated from an ocean propagation model to detect line segments in a time–frequency spectrogram. The evaluation of U-Net with the test data, based on the performance metrics, such as probability of false alarm, probability of detection, and normalized cross-correlation coefficient, reveals that it effectively extracts the dispersion curves. The proposed network was successfully applied to unseen simulated and experimental data. Our results demonstrate that the dispersion curve images generated through model-guided deep learning can serve as concise image features, including information regarding ocean environments.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 3","pages":"1812-1821"},"PeriodicalIF":3.8,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11006262","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144646420","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}

引用次数: 0

Full-Dimensional Nonlinear Dynamic Analysis for Lift Operation of a DP Crane Vessel DP起重船提升作业的全维非线性动力学分析

IF 3.8 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2025-03-15 DOI: 10.1109/JOE.2025.3557106

Shiyu Liu;Jun Ye;Mingsheng Chen;Junfeng Dong;Zhiyong Liu;Xinran Guo;Hongxing Wang

引用次数: 0

Blind Quality Assessment Using Channel-Based Structural, Dispersion Rate Scores, and Overall Saturation and Hue for Underwater Images 使用基于信道的结构，色散率分数，以及水下图像的总体饱和度和色相的盲质量评估

IF 3.8 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2025-03-15 DOI: 10.1109/JOE.2025.3553888

Hamidreza Farhadi Tolie;Jinchang Ren;Jun Cai;Rongjun Chen;Huimin Zhao

{"title":"Blind Quality Assessment Using Channel-Based Structural, Dispersion Rate Scores, and Overall Saturation and Hue for Underwater Images","authors":"Hamidreza Farhadi Tolie;Jinchang Ren;Jun Cai;Rongjun Chen;Huimin Zhao","doi":"10.1109/JOE.2025.3553888","DOIUrl":"https://doi.org/10.1109/JOE.2025.3553888","url":null,"abstract":"In underwater subsea environments light attenuation, water turbidity, and limitations of the optical devices make the captured images suffer from poor contrast and quality, proportional degradation, low visibility, and low color richness. In recent years, various image enhancement techniques have been applied to improve the image quality, resulting in a new challenge, i.e., the quality assessment of the underwater images. In this study, we introduce an innovative and versatile blind quality assessment method for underwater images without using any references. Our approach leverages structural and contour-based metrics, combined with dispersion rate analysis, to quantify image degradation and color richness within an opponent color space. Specifically, we measure the proportional degradation by computing the edge magnitude using the directional Kirsch kernels, strengthened by image contour and saliency maps. To assess the color quality, chrominance dispersion rates and the overall saturation and hue are used to capture color distortions introduced by enhancement methods. The final quality score is obtained via a multiple linear regression model trained on extensive data sets. Experiments on three benchmark data sets have demonstrated the superior accuracy, consistency, and computational efficiency of the proposed method for both raw and enhanced underwater images.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 3","pages":"1944-1959"},"PeriodicalIF":3.8,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144646064","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

Rotation Invariant Sonar Image Segmentation for Undersea Cables 海底电缆旋转不变声呐图像分割

IF 3.8 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2025-03-15 DOI: 10.1109/JOE.2025.3557927

Songbo Xu;He Shen;Yixin Yang

引用次数: 0

Parameter Estimation by Alternating Reconstruction and Sensation for Sonar System 声纳系统的交替重建与感知参数估计

IF 3.8 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2025-03-15 DOI: 10.1109/JOE.2025.3529255

Haoran Ji;Lei Wang;Shuhao Zhang;Wenjie Zhou;Cong Peng

{"title":"Parameter Estimation by Alternating Reconstruction and Sensation for Sonar System","authors":"Haoran Ji;Lei Wang;Shuhao Zhang;Wenjie Zhou;Cong Peng","doi":"10.1109/JOE.2025.3529255","DOIUrl":"https://doi.org/10.1109/JOE.2025.3529255","url":null,"abstract":"Deconvolution algorithms often rely on conventional beamforming methods to obtain beamforming vectors, which limit their resolution. To enhance parameter estimation resolution, this article introduces the Parameter Estimation by Alternating Reconstruction and Sensation (PEARS) algorithm. In the proposed algorithm, direction estimation leverages a linearly constrained quadratic programming method and weighted L1-norm to solve the objective function, achieving higher resolution in the direction spectrum under fixed weighted vector conditions. The algorithm utilizes the gradient descent method to update the weighted vector, and the relationship among the dictionary matrix, direction spectrum, and weighted vector is computed using the chain rule. This process improves direction estimation results, particularly in scenarios with low signal-to-noise ratios. By alternating between target parameter estimation and weight vector calculation, the PEARS algorithm achieves highly accurate target azimuth estimation. Simulation results validate the algorithm's ability to accurately estimate target azimuth angles. In addition, lake and sea experimental results demonstrate the algorithm's effectiveness in correctly estimating direction in complex environments.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 3","pages":"2311-2326"},"PeriodicalIF":3.8,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144646677","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