IEEE Journal of Oceanic Engineering最新文献

{"title":"Adaptive Ocean Gradient Tracking Using an Autonomous Underwater Vehicle With a Boundless Model","authors":"Tore Mo-Bjørkelund;Renato Mendes;Francisco López-Castejón;Martin Ludvigsen","doi":"10.1109/JOE.2024.3484577","DOIUrl":"https://doi.org/10.1109/JOE.2024.3484577","url":null,"abstract":"This work presents a method for exploring a dynamic river plume boundary using an autonomous underwater vehicle with an on-board lightweight <italic>boundless</i> model. The <italic>boundless</i> approach is achieved by not constraining the path evaluations or the Gaussian random field to a predefined geographical area. In-situ decision-making enables targeted sampling of the ocean–river plume interaction. The data-driven and adaptive approaches provide the capability and opportunity to fully utilize the operational window for the vehicle. The method was developed using a simulated plume and vehicle, and results from simulation studies and successful field trials from the Douro River plume outside Porto, Portugal, are presented. The vehicle adapts its path based on the underway real-time assimilation of measurements, seeking to gain new information while not straying away from the front. Owing to the unpredictable shape and size of the river front, a model-based <italic>boundless</i> method for adaptive sampling was constructed, generating potential waypoints as a function of the vehicle's position and the accumulated knowledge of the plume. Not bounding the spatial or geographical extent of the method allows for greater variation in plume shape and size. The river plume's extent is defined here as the area within the sharpest spatial salinity gradient, containing less saline water than the surrounding ocean. In the method, the depth of the sharpest vertical salinity gradient, or plume depth, is estimated using a 2-D <italic>Gaussian Process</i>, where the plume depth is estimated from a dive and ascent envelope of the robot traversing the ocean in an undulating fashion. Computational efficiency is gained from the resulting low number of inputs to the Gaussian process, compared to the number of salinity measurements, ensuring rapid on-board adaption. The next waypoint is chosen as the first waypoint in a path that maximizes the weighted sum of uncertainty, estimated plume depth, and the absolute value of the difference between the current plume depth and the estimated river plume depth along the path. This encourages traversal of the plume in a fashion that enables the extent of the plume to be resolved in high detail. The data-driven method was field verified in the Douro River, proving the ability to track the river plume to balance exploration and exploitation behavior to maximize the information value of the mission in real time onboard the vehicle.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 2","pages":"955-967"},"PeriodicalIF":3.8,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10879136","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848871","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":"Semi-Supervised Underwater Image Enhancement Network Boosted by Depth Map Consistency","authors":"Fengqi Xiao;Jiahui Liu;Yifan Huang;En Cheng;Fei Yuan","doi":"10.1109/JOE.2024.3487350","DOIUrl":"https://doi.org/10.1109/JOE.2024.3487350","url":null,"abstract":"Underwater optical images are a crucial information source for autonomous underwater vehicles during underwater development and exploration. When these vehicles are in operation, they need to capture high-quality images for information extraction and analysis and require depth map information from the underwater scene to maintain the vehicle's posture balance, obstacle avoidance, and navigation. However, the absorption and scattering of light in water result in low-quality underwater images, significantly affecting the execution of these tasks. In response to these challenges, this article proposes a physically guided, semi-supervised dual-loop network for underwater image enhancement. This network is designed to accomplish high-quality underwater image enhancement and depth map estimation simultaneously. First, the revised underwater image formation model is employed to guide a two-stage network in decomposing and reconstructing underwater images. The depth map consistency of the scene and piecewise cycle consistency loss are utilized to ensure the reliability of the image transformation process. In another loop, a self-augmentation module based on inherent optical properties is introduced to enhance the robustness of the decomposition network. A multimodal discriminator is incorporated to form piecewise adversarial loss to improve the visual quality of the images. Through extensive experimental evaluation and analysis, the proposed method not only demonstrates outstanding performance in underwater image enhancement and depth map estimation but also reveals the relationships between various physical quantities during the degradation process of underwater images, enhancing the physical interpretability of the neural network.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 2","pages":"795-816"},"PeriodicalIF":3.8,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848854","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":"Hierarchical Wavelet Decomposition Network for Water-Related Optical Image Enhancement","authors":"Jingchun Zhou;Rui Zhou;Zongxin He;Cong Zhang;Qiuping Jiang;Weishi Zhang;Ferdous Sohel","doi":"10.1109/JOE.2024.3458349","DOIUrl":"https://doi.org/10.1109/JOE.2024.3458349","url":null,"abstract":"Enhancing water-related optical images poses a significant challenge due to the complex interplay of direct attenuation and backscattering. Current methods primarily focus on modifying the spatial domain and pay less attention to the heterogeneity of the frequency domain degradation distributions, which limits their effectiveness in solving multiple types of degradation problems simultaneously. To overcome these limitations, we propose a hierarchical wavelet decomposition network (HWD-Net). HWD-Net leverages wavelet transforms to create a compact feature space, enabling the distinct restoration of low and high-frequency degradations through a strategic divide-and-conquer approach, which prevents the interaction of high- and low-frequency information and avoids the generation of incorrect textures. Furthermore, HWD-Net employs a hierarchical decomposition paradigm to progressively extract richer high-frequency information, achieving superior enhancements in a coarse-to-fine manner. Comprehensive evaluations on multiple underwater data sets demonstrate the superiority of HWD-Net over state-of-the-art methods in terms of image quality and inference time.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 2","pages":"776-794"},"PeriodicalIF":3.8,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848841","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":"Inversion of Time-Evolving Sound Speed Profiles by DREAM-zs With QPSO Proposal Distribution","authors":"Ye Wang;Jianlong Li;Jing Chen;Yida Xu;Lingzao Zeng","doi":"10.1109/JOE.2024.3507825","DOIUrl":"https://doi.org/10.1109/JOE.2024.3507825","url":null,"abstract":"Sound speed profile (SSP) inversion using acoustic data is an efficient approach for estimating SSPs, though it presents a nonlinear and non-Gaussian problem. To improve the spatial resolution of SSP inversion, a range-dependent environment is considered, which increases the number of parameters to be estimated. These challenges limit the performance of commonly used methods, such as the ensemble Kalman filter (EnKF) and particle filter (PF). While EnKFs can handle high-dimensional problems, they assume Gaussian probability distributions. PFs are better suited for highly nonlinear and non-Gaussian systems but are generally more computationally intensive. DiffeRential evolution adaptive Metropolis (DREAM)-zs, a Markov chain Monte Carlo method, is effective for approximating high-dimensional probability distributions, although its convergence speed decreases rapidly as the dimensionality of the parameter space increases. Quantum-behaved particle swarm optimization (QPSO), which combines principles from quantum mechanics and PSO, is a probabilistic optimization algorithm that has been proven successful in various optimization problems. To enhance the efficiency of DREAM-zs, the QPSO proposal distribution is embedded during the burn-in period, forming the DREAM-zqs method. The proposed method is used to track time-evolving SSPs in a range-dependent environment. The inversion problem is formulated in a state-space form, integrating data from the regional ocean modeling system to improve efficiency by shifting the state vector. Simulations and experimental results demonstrate that both DREAM-zs and DREAM-zqs outperform EnKF and PF, with DREAM-zqs achieving faster convergence than DREAM-zs while maintaining inversion accuracy.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 2","pages":"1403-1418"},"PeriodicalIF":3.8,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852392","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":"UVTD: A Large-Scale Multilabel Data Set for Underwater Vision Tasks","authors":"Zhengyong Wang;Meng Yu;Lei Cao;Pengyu Liu;Linfeng Wang;Xiang Li;Yixiao Hong;Chang He;Liquan Shen","doi":"10.1109/JOE.2024.3503664","DOIUrl":"https://doi.org/10.1109/JOE.2024.3503664","url":null,"abstract":"With the exploration of ocean resources, underwater vision tasks (UVTs) have attracted increasing interest in recent years. However, the advancement of UVTs is hampered by several challenges, prominently the difficulty in acquiring large-scale data sets with accurate annotations, and the absence of a unified multi-label data set for underwater multitask learning (UMTL). Motivated by the critical need for a large-scale, multilabel data set tailored for UVTs, we present a large-scale multilabel data set for underwater vision tasks (UVTD), which offers solutions to several bottlenecks in UVT research: first, supporting diverse applications. With annotations for two underwater low-level vision tasks (i.e., underwater image enhancement and underwater image quality assessment) and three high-level vision tasks (i.e., underwater semantic segmentation, underwater object detection, and underwater salient object detection), UVTD supports a wide range of underwater applications. Second, enhancing model generalization. UVTD comprises 5380 real underwater images, covering diverse scenes and varied degradation characteristics, improving the robustness of vision algorithms. Finally, facilitating multitask learning. Our multilabel data set enables researchers to explore the correlations between tasks and develop robust UMTL algorithms. Based on UVTD, we propose two UMTL networks tailored to the low-level and high-level tasks separately, serving as benchmarks for future research in the UMTL field. Extensive experiments demonstrate UVTD's superiority across multiple UVTs, and the proposed UMTL networks exhibit competitive performance on these tasks, implying the significant implications of UVTD for future research.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 2","pages":"898-918"},"PeriodicalIF":3.8,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848840","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":"Self-Supervised Underwater Intelligent Perception for Deep-Sea Cage Aquaculture","authors":"Shunmin An;Qifeng Liu;Rui Zhang;Lihong Xu;Linling Wang","doi":"10.1109/JOE.2024.3478315","DOIUrl":"https://doi.org/10.1109/JOE.2024.3478315","url":null,"abstract":"In deep-sea net-pen aquaculture, underwater intelligent sensing is performed by an underwater camera for information acquisition, but underwater scattering and absorption effects affect the acquisition of underwater information. It is challenging to use neural networks to process the net tank aquaculture scenarios because the underwater data sets of the net tank aquaculture scenarios are not accessible. In this article, we propose a self-supervised deep-sea scene recovery method utilizing a homology constraint and a fusion strategy. Specifically, the scene radiation maps are derived based on a neural network and a prior extraction architecture, respectively, and two scene radiation maps originate from two different computational regimes. Finally, the perceptual fusion strategy is used to blend two scene radiation maps to obtain better performing results and minimize the error using the homology constraint. Extensive experiments confirm that the approach using perceptual fusion has excellent recovery capabilities. It is demonstrated through extensive experiments that our method outperforms state-of-the-art methods in terms of both visual quality and quantitative metrics.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 2","pages":"714-726"},"PeriodicalIF":3.8,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848785","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":"JOE Call for Papers - Special Issue on Maritime Informatics and Robotics: Advances from the IEEE Symposium on Maritime Informatics & Robotics","authors":"","doi":"10.1109/JOE.2025.3527081","DOIUrl":"https://doi.org/10.1109/JOE.2025.3527081","url":null,"abstract":"","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 1","pages":"421-422"},"PeriodicalIF":3.8,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10839488","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142975887","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":"DLink: Introducing a Framework for Link Adaptation in Flooding-Based Underwater Networks","authors":"Elias Strandell Erstorp;Viktor Lidström;Peter Sigray","doi":"10.1109/JOE.2024.3494113","DOIUrl":"https://doi.org/10.1109/JOE.2024.3494113","url":null,"abstract":"The underwater acoustic environment is known for its unpredictability, making it challenging to establish configuration parameters for acoustic modems before network deployment. When the modems are configured for robustness, potential throughput is often sacrificed; meanwhile, opting for high-rate links can result in communication failures in highly dynamic acoustic conditions. Given these challenges, this article presents an adaptation framework for networked underwater acoustic modems. Its primary objective is to let modems adaptively select communication links that balance information rate and reliability. It is assumed that the modems provide a set of preconfigured links with monotonically increasing information rate and decreasing reliability. The framework is developed specifically for flooding-based routing protocols, which efficiently handle sudden changes in network topology. By leveraging existing network traffic and implicit acknowledgments, the framework achieves link adaptation with minimal network overhead, necessitating only the addition of a “previous node” address field in the packet headers. Field experiments were conducted by deploying six acoustic modems in a time-varying acoustic environment. A well-known flooding-based protocol, DFlood, was used for routing in the experiments. The network's throughput with the adaptation framework was compared to that when only robust links were permitted. Results of the framework, using modems configured with four different links, show an increase in the average information per packet by a factor of up to 12, and a reduction in network transmission times of 25%–50%, demonstrating DLink's ability to enhance channel utilization, outperforming configurations that rely solely on robust links.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 2","pages":"1456-1468"},"PeriodicalIF":3.8,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852388","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":"Probabilistic Particle Filter Anchoring (PPFA): A Novel Perspective in Semantic World Modeling for Autonomous Underwater Vehicles With Acoustic and Optical Exteroceptive Sensors","authors":"Alberto Topini;Alessandro Ridolfi","doi":"10.1109/JOE.2024.3492537","DOIUrl":"https://doi.org/10.1109/JOE.2024.3492537","url":null,"abstract":"Creating an accurate world model of the scenario where an autonomous underwater vehicle is navigating can be considered a crucial stage for understanding the surrounding environment. As a result, the targets detected by an automatic target recognition (ATR) architecture alongside their localized positions, must be handled, selected, and filtered to get a symbolic representation of the underwater context. Even though the specific world modeling (WM) architecture may vary, current WM methodologies usually rely on the 3-D localization knowledge of the detected target by introducing a nonnegligible constraint. Motivated by the aforementioned considerations, a novel probabilistic particle filter anchoring (PPFA) approach has been developed. Starting from ATR 2-D results, the PPFA methodology aims at providing a semantic 3-D representation of the subsea environment by merging the upsides of both data association and object tracking, handled by a custom designed particle filter with resampling.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 2","pages":"1065-1086"},"PeriodicalIF":3.8,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10839132","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848829","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":"JOE Call for Papers - Special Issue on the IEEE 2026 AUV Symposium","authors":"","doi":"10.1109/JOE.2025.3527079","DOIUrl":"https://doi.org/10.1109/JOE.2025.3527079","url":null,"abstract":"","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 1","pages":"419-420"},"PeriodicalIF":3.8,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10839495","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142975889","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}