Pub Date : 2025-03-04DOI: 10.1109/JOE.2025.3529087
Xiaojuan Ma;Yanhui Wang
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.
{"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}
Pub Date : 2025-03-03DOI: 10.1109/JOE.2024.3516096
Qingyu Zhang;Jin Fu;Nan Zou;Bin Qi;Yuan Hang Fan
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.
{"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}
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.
{"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}
Pub Date : 2025-03-03DOI: 10.1109/JOE.2024.3523701
Sebastián Rodríguez-Martínez;Giancarlo Troni
Despite their widespread use in determining system attitude, micro-electro-mechanical systems attitude and heading reference system are limited by sensor measurement biases. This article introduces a method called Magnetometer and Gyroscope Calibration (MAGYC), leveraging three-axis angular rate measurements from an angular rate gyroscope to estimate both the hard- and soft-iron biases of magnetometers as well as the bias of gyroscopes. We present two implementation methods of this approach based on batch and online incremental factor graphs. Our method imposes fewer restrictions on instrument movements required for calibration, eliminates the need for knowledge of the local magnetic field magnitude or instrument's attitude, and facilitates integration into factor graph algorithms for smoothing and mapping frameworks. We validate the proposed methods through numerical simulations and in-field experimental evaluations with a sensor onboard an underwater vehicle. By implementing the proposed method in field data of a seafloor mapping dive, the dead-reckoned-based position estimation error of the underwater vehicle was reduced from 10% to 0.5% of the distance traveled.
{"title":"Full Magnetometer and Gyroscope Bias Estimation Using Angular Rates: Theory and Experimental Evaluation of a Factor Graph-Based Approach","authors":"Sebastián Rodríguez-Martínez;Giancarlo Troni","doi":"10.1109/JOE.2024.3523701","DOIUrl":"https://doi.org/10.1109/JOE.2024.3523701","url":null,"abstract":"Despite their widespread use in determining system attitude, micro-electro-mechanical systems attitude and heading reference system are limited by sensor measurement biases. This article introduces a method called Magnetometer and Gyroscope Calibration (MAGYC), leveraging three-axis angular rate measurements from an angular rate gyroscope to estimate both the hard- and soft-iron biases of magnetometers as well as the bias of gyroscopes. We present two implementation methods of this approach based on batch and online incremental factor graphs. Our method imposes fewer restrictions on instrument movements required for calibration, eliminates the need for knowledge of the local magnetic field magnitude or instrument's attitude, and facilitates integration into factor graph algorithms for smoothing and mapping frameworks. We validate the proposed methods through numerical simulations and in-field experimental evaluations with a sensor onboard an underwater vehicle. By implementing the proposed method in field data of a seafloor mapping dive, the dead-reckoned-based position estimation error of the underwater vehicle was reduced from 10% to 0.5% of the distance traveled.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 3","pages":"1606-1615"},"PeriodicalIF":3.8,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10908715","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144646458","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}
Pub Date : 2025-03-03DOI: 10.1109/JOE.2024.3525150
Yuhao Qing;Liquan Shen;Zhijun Fang;Yueying Wang
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.
{"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}
Pub Date : 2025-02-26DOI: 10.1109/JOE.2024.3494112
Ziqiang Zheng;Haixin Liang;Fong Hei Wut;Yue Him Wong;Apple Pui-Yi Chui;Sai-Kit Yeung
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 ideal and normal 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 practical, challenging, and adverse conditions. To address these issues, in this article, we propose an in-the-wild coral reef data set named HKCoral 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 growth form 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.
{"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}
Pub Date : 2025-02-26DOI: 10.1109/JOE.2024.3519680
Isah A. Jimoh;Hong Yue
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.
{"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}
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$^{3}$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$^{3}$USOD proves to be effective and efficient, establishing its practicality, particularly for underwater applications.
{"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}
The underwater industry and scientific community are actively researching the development of vehicles that combine the functionalities of autonomous underwater vehicles and remotely operated vehicles. An innovative approach to address the challenges posed by underwater exploration is the development of autonomous underwater reconfigurable vehicles (AURVs). These vehicles are designed to adapt their configuration to suit the requirements of the task at hand. The flexibility of AURVs enables them to undertake a variety of underwater missions, ranging from scientific research to deep-sea exploration. The Department of Industrial Engineering at the University of Florence, Italy, has developed and patented an innovative AURV that is able to quickly change its shape to suit different tasks. The reconfigurable underwater vehicle for inspection, free-floating intervention and survey tasks (RUVIFIST) have been equipped with two extreme configurations. The first configuration is a slender one meant for long navigation tasks, while the second configuration is a stocky one designed for tackling complex objectives such as inspection or intervention operations. With the ability to adapt its form to suit the task at hand, the RUVIFIST vehicle represents a significant advancement in underwater vehicle technology. This work provides an overview of the challenges faced and the solutions adopted during the development of this new vehicle. This article presents the results of experimental campaigns to test the reconfigurable system of the vehicle and the strategies developed for the guidance, navigation, and control system of AURVs. Finally, preliminary tests were conducted to explore the integration of machine learning and deep learning algorithms that are compatible with the purpose of automatic target recognition.
{"title":"Design, Development, and Testing of an Innovative Autonomous Underwater Reconfigurable Vehicle for Versatile Applications","authors":"Mirco Vangi;Edoardo Topini;Gherardo Liverani;Alberto Topini;Alessandro Ridolfi;Benedetto Allotta","doi":"10.1109/JOE.2024.3511709","DOIUrl":"https://doi.org/10.1109/JOE.2024.3511709","url":null,"abstract":"The underwater industry and scientific community are actively researching the development of vehicles that combine the functionalities of autonomous underwater vehicles and remotely operated vehicles. An innovative approach to address the challenges posed by underwater exploration is the development of autonomous underwater reconfigurable vehicles (AURVs). These vehicles are designed to adapt their configuration to suit the requirements of the task at hand. The flexibility of AURVs enables them to undertake a variety of underwater missions, ranging from scientific research to deep-sea exploration. The Department of Industrial Engineering at the University of Florence, Italy, has developed and patented an innovative AURV that is able to quickly change its shape to suit different tasks. The reconfigurable underwater vehicle for inspection, free-floating intervention and survey tasks (RUVIFIST) have been equipped with two extreme configurations. The first configuration is a slender one meant for long navigation tasks, while the second configuration is a stocky one designed for tackling complex objectives such as inspection or intervention operations. With the ability to adapt its form to suit the task at hand, the RUVIFIST vehicle represents a significant advancement in underwater vehicle technology. This work provides an overview of the challenges faced and the solutions adopted during the development of this new vehicle. This article presents the results of experimental campaigns to test the reconfigurable system of the vehicle and the strategies developed for the guidance, navigation, and control system of AURVs. Finally, preliminary tests were conducted to explore the integration of machine learning and deep learning algorithms that are compatible with the purpose of automatic target recognition.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 2","pages":"509-526"},"PeriodicalIF":3.8,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10904475","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852373","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}
Pub Date : 2025-02-26DOI: 10.1109/JOE.2025.3529210
Seung-Jae Lee
In this study, an adaptive refocusing scheme for moving ships in satellite synthetic aperture radar (SAR) images is proposed to cope with various types of motions of ship targets. To decide the type of ship's motion, the phase signals of principal scatterers are analyzed based on the inverse SAR (ISAR) signal model with the help of a joint time–frequency transform and deep learning model. Then, proper ISAR-based refocusing algorithms are used to generate a well-focused image considering the ship's motion. The design of the adaptive refocusing concept enables us to select appropriate algorithms to retrieve the exact scattering mechanisms of ship targets. In addition, to cope with defocusing due to the complex 3-D motion of the ship, an efficient reconstruction strategy based on compressive sensing is devised. It is a concept different from conventional optimal time windowing, which deals with the complex motion of the ship target, and it yields a well-focused image that retains the spatial resolution of the original ship image. In experiments using simulated and real SAR images, the proposed method shows reliable refocusing results for various ship targets compared to traditional methods.
{"title":"Adaptive Refocusing Chain for Moving Ships in Satellite SAR Images","authors":"Seung-Jae Lee","doi":"10.1109/JOE.2025.3529210","DOIUrl":"https://doi.org/10.1109/JOE.2025.3529210","url":null,"abstract":"In this study, an adaptive refocusing scheme for moving ships in satellite synthetic aperture radar (SAR) images is proposed to cope with various types of motions of ship targets. To decide the type of ship's motion, the phase signals of principal scatterers are analyzed based on the inverse SAR (ISAR) signal model with the help of a joint time–frequency transform and deep learning model. Then, proper ISAR-based refocusing algorithms are used to generate a well-focused image considering the ship's motion. The design of the adaptive refocusing concept enables us to select appropriate algorithms to retrieve the exact scattering mechanisms of ship targets. In addition, to cope with defocusing due to the complex 3-D motion of the ship, an efficient reconstruction strategy based on compressive sensing is devised. It is a concept different from conventional optimal time windowing, which deals with the complex motion of the ship target, and it yields a well-focused image that retains the spatial resolution of the original ship image. In experiments using simulated and real SAR images, the proposed method shows reliable refocusing results for various ship targets compared to traditional methods.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 2","pages":"1290-1308"},"PeriodicalIF":3.8,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10904822","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852451","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}
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