AquaFeat+: an Underwater Vision Learning-based Enhancement Method for Object Detection, Classification, and Tracking
Centro de Ciências Computacionais (C3), Universidade Federal do Rio Grande (FURG), Brazil（巴西里约格兰德联邦大学计算科学研究中心（C3））；Instituto de Ciências Exatas e Tecnologia (ICET), Universidade Federal do Amazonas (UFAM), Brazil（巴西亚马逊联邦大学精确科学与技术研究所（ICET））
Emanuel da Costa Silva, Tatiana Taís Schein, José David García Ramos, Eduardo Lawson da Silva, Stephanie Loi Brião, Felipe Gomes de Oliveira, Paulo Lilles Jorge Drews-Jr
Underwater video analysis is particularly challenging due to factors such as low lighting, color distortion, and turbidity, which compromise visual data quality and directly impact the performance of perception modules in robotic applications. This work proposes AquaFeat+, a plug-and-play pipeline designed to enhance features specifically for automated vision tasks, rather than for human perceptual quality. The architecture includes modules for color correction, hierarchical feature enhancement, and an adaptive residual output, which are trained end-to-end and guided directly by the loss function of the final application. Trained and evaluated in the FishTrack23 dataset, AquaFeat+ achieves significant improvements in object detection, classification, and tracking metrics, validating its effectiveness for enhancing perception tasks in underwater robotic applications.
SVGS-DSGAT: An IoT-Enabled Innovation in Underwater Robotic Object Detection Technology
College of Design and Engineering, National University of Singapore（新加坡国立大学设计与工程学院）；Institute of Semiconductors, Chinese Academy of Sciences（中国科学院半导体研究所）；Beijing Ratu Technology Co., Ltd.（北京瑞途科技有限公司）
Shanghai Jiao Tong University（上海交通大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；CloudWalk Technology Co., Ltd（云walk科技有限公司）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
Design, Implementation and Evaluation of an External Pose-Tracking System for Underwater Cameras
GEOMAR Helmholtz Centre for Ocean Research Kiel（基尔亥姆霍兹海洋研究中心）；Christian-Albrechts-Universität zu Kiel（基尔大学）；University of Applied Sciences Kiel（基尔应用科学大学）
WebUOT-1M: Advancing Deep Underwater Object Tracking with A Million-Scale Benchmark
Cooperative Medianet Innovation Center, Shanghai Jiao Tong University（上海交通大学协同媒体网络创新中心）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；CloudWalk Technology Co., Ltd（云从科技有限公司）；Shanghai AI Laboratory（上海人工智能实验室）
SonarT165: A Large-scale Benchmark and STFTrack Framework for Acoustic Object Tracking
National Key Laboratory Foundation of Autonomous Marine Vehicle Technology（自主水下机动车辆技术国家重点实验室）；National Natural Science Foundation of China（国家自然科学基金委员会）；National Key Research and Development Program of China（国家重点研发计划）
A Survey of Fish Tracking Techniques Based on Computer Vision
College of Information and Electrical Engineering, China Agricultural University（中国农业大学信息与电气工程学院）；National Innovation Center for Digital Fishery, Ministry of Agriculture and Rural Affairs（农业农村部数字渔业创新中心）；College of Engineering, China Agricultural University（中国农业大学工学院）
Algorithmic Design and Implementation of Unobtrusive Multistatic Serial LiDAR Image
University of Florida（佛罗里达大学）
为了充分了解海洋流体动力学（MHK）设备与海洋动物之间的相互作用，需要一个快速有效的监测系统，以便在水下动物出现时捕获相关信息。一种新的自动水下成像系统，由LiDAR（光探测和测距）成像硬件和一个名为Unobtrusive Multistatic Serial LiDAR Imager（UMSLI）的场景理解软件模块组成，用于监督涡轮机附近的动物是否存在。UMSLI集成了前端LiDAR硬件和一系列软件模块，以分层的方式实现图像预处理，检测，跟踪，分割和分类。
