View in EDS

A comprehensive review of datasets and deep learning techniques for vision in unmanned surface vehicles.

Saved in:
Bibliographic Details
Title: A comprehensive review of datasets and deep learning techniques for vision in unmanned surface vehicles.
Authors: Trinh, Linh1 (AUTHOR), Mercelis, Siegfried1 (AUTHOR), Anwar, Ali1 (AUTHOR)
Source: Ocean Engineering. Aug2025, Vol. 334, pN.PAG-N.PAG. 1p.
Subjects: Deep learning, Computer vision, Autonomous vehicles, LIDAR, Research & development
Abstract: • A background of maritime vision and unmanned surface vehicles • A comprehensive analysis of a large number of recent datasets for USV vision collected in real-world scenarios using a variety of vision sensors such as cameras, LiDAR, radar, and so on. • Analysis of 38 public datasets on a variety of characteristics. • Discussion of recent deep learning techniques for USV vision tasks • Discussion of the challenges, and potential future perspectives in USVs' vision. Unmanned Surface Vehicles (USVs) have emerged as a major platform in maritime operations, capable of supporting a wide range of applications. USVs allow for difficult unmanned tasks in harsh maritime environments. With the rapid development of USVs, many vision tasks such as detection and segmentation become increasingly important. Datasets play an important role in encouraging and improving the research and development of reliable vision algorithms for USVs. In this regard, a large number of recent studies have focused on the release of vision datasets for USVs. Along with the development of datasets, a variety of deep learning techniques have also been studied, with a focus on USVs. However, there is a lack of a systematic review of recent studies in both datasets and vision techniques to provide a comprehensive picture of the current development of vision on USVs, including limitations and trends. In this study, we provide a comprehensive review of both USV datasets and deep learning techniques for vision tasks. Our review was conducted using a large number of vision datasets from USVs. We elaborate several challenges and potential opportunities for research and development in USV vision based on a thorough analysis of current datasets and deep learning techniques. [ABSTRACT FROM AUTHOR]
Copyright of Ocean Engineering is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Description
Abstract:• A background of maritime vision and unmanned surface vehicles • A comprehensive analysis of a large number of recent datasets for USV vision collected in real-world scenarios using a variety of vision sensors such as cameras, LiDAR, radar, and so on. • Analysis of 38 public datasets on a variety of characteristics. • Discussion of recent deep learning techniques for USV vision tasks • Discussion of the challenges, and potential future perspectives in USVs' vision. Unmanned Surface Vehicles (USVs) have emerged as a major platform in maritime operations, capable of supporting a wide range of applications. USVs allow for difficult unmanned tasks in harsh maritime environments. With the rapid development of USVs, many vision tasks such as detection and segmentation become increasingly important. Datasets play an important role in encouraging and improving the research and development of reliable vision algorithms for USVs. In this regard, a large number of recent studies have focused on the release of vision datasets for USVs. Along with the development of datasets, a variety of deep learning techniques have also been studied, with a focus on USVs. However, there is a lack of a systematic review of recent studies in both datasets and vision techniques to provide a comprehensive picture of the current development of vision on USVs, including limitations and trends. In this study, we provide a comprehensive review of both USV datasets and deep learning techniques for vision tasks. Our review was conducted using a large number of vision datasets from USVs. We elaborate several challenges and potential opportunities for research and development in USV vision based on a thorough analysis of current datasets and deep learning techniques. [ABSTRACT FROM AUTHOR]
ISSN:00298018
DOI:10.1016/j.oceaneng.2025.121501