Underwater Vehicles have become more sophisticated, driven by the off-shore sector and the scientific community's rapid advancements in underwater operations. Notably, many underwater tasks, including the assessment of subsea infrastructure, are performed with the assistance of Autonomous Underwater Vehicles (AUVs). There have been recent breakthroughs in Artificial Intelligence (AI) and, notably, Deep Learning (DL) models and applications, which have widespread usage in a variety of fields, including aerial unmanned vehicles, autonomous car navigation, and other applications. However, they are not as prevalent in underwater applications due to the difficulty of obtaining underwater datasets for a specific application. In this sense, the current study utilises recent advancements in the area of DL to construct a bespoke dataset generated from photographs of items captured in a laboratory environment. Generative Adversarial Networks (GANs) were utilised to translate the laboratory object dataset into the underwater domain by combining the collected images with photographs containing the underwater environment. The findings demonstrated the feasibility of creating such a dataset, since the resulting images closely resembled the real underwater environment when compared with real-world underwater ship hull images. Therefore, the artificial datasets of the underwater environment can overcome the difficulties arising from the limited access to real-world underwater images and are used to enhance underwater operations through underwater object image classification and detection.
Establishing Reality-Virtuality Interconnections in Urban Digital Twins for Superior Intelligent Road Inspection and Simulation
College of Electronics & Information Engineering, Shanghai Research Institute for Intelligent Autonomous Systems, the State Key Laboratory of Intelligent Autonomous Systems, and Frontiers Science Center for Intelligent Autonomous Systems, Tongji University, Shanghai 201804, China（电子与信息工程学院，上海智能自主系统研究院，智能自主系统国家重点实验室，智能自主系统前沿科学中心，同济大学，上海）
Closer to Ground Truth: Realistic Shape and Appearance Labeled Data Generation for Unsupervised Underwater Image Segmentation
NORCE Norwegian Research Center AS（NORCE挪威研究中心）；National University of Science and Technology "Politehnica" Bucharest（布加勒斯特理工科学技术大学）；"Simion Stoilow" Institute of Mathematics of the Romanian Academy（罗马尼亚科学院西米翁斯托伊洛夫数学研究所）
Explainable AI for Time Series via Virtual Inspection Layers
Fraunhofer Heinrich Hertz Institute（弗劳恩霍夫海因里希·赫兹研究所）；Technische Universität Berlin（柏林工业大学）；Freie Universität Berlin（柏林自由大学）；BIFOLD – Berlin Institute for the Foundations of Learning and Data（BIFOLD——柏林学习与数据基础研究所）
A Novel Approach for Defect Detection of Wind Turbine Blade Using Virtual Reality and Deep Learning
The University of Texas at El Paso（德克萨斯大学埃尔帕索分校）；Khulna University of Engineering and Technology (KUET)（库尔纳工程技术大学）；University of Rajshahi（拉杰沙希大学）；National Chi Nan University（国立暨南国际大学）
YOLO-Based Pipeline Monitoring in Challenging Visual Environments
Department of Electrical Electronics and Telecommunication Engineering and Naval Architecture (DITEN), University of Genoa（电子电气与航海工程系（DITEN），热那亚大学）；Department of Bio-systems Engineering, Gyeongsang National University（生物系统工程系，全州国立大学）；Department of Mechanical Engineering and Maritime Studies, Western Norway University of Applied Sciences（机械工程与航海研究系，西挪威应用科学大学）
Dense 3D Reconstruction for Visual Tunnel Inspection using Unmanned Aerial Vehicle
Institute for Infocomm Research (IR), A*STAR（新加坡科技研究局资讯通信研究院）；Nanyang Technological University (NTU)（南洋理工大学）；University of Illinois at Urbana-Champaign (UIUC)（伊利诺伊大学厄巴纳-香槟分校）；Singapore University of Technology and Design (SUTD)（新加坡科技设计大学）
