系统梳理无人船视觉数据集与深度学习技术，指出现有挑战与方向。
A comprehensive review of datasets and deep learning techniques for vision in Unmanned Surface Vehicles
- 综述无人船视觉任务相关数据集与深度学习方法
- 分析现有数据集在场景多样性与标注质量上的不足
- 适合关注无人船视觉算法研究的科研人员参考
无人船（USVs）已成为海上作业的重要平台，可支持多种应用，有助于降低人力成本、提升安全性、节约能源，并在恶劣海况下完成复杂任务。随着无人船快速发展，目标检测、语义分割等视觉任务日益重要。高质量数据集对推动可靠视觉算法的研发至关重要。近年来，大量研究聚焦于发布无人船视觉数据集，同时深度学习技术也得到广泛应用。然而，目前缺乏对无人船视觉数据集与深度学习技术的系统性综述，难以全面反映当前研究进展、局限与趋势。本文系统回顾了现有无人船视觉数据集及深度学习方法，基于大量数据集分析，深入探讨了无人船视觉研究中的挑战与潜在发展机遇。
原文摘要 · Abstract (English)
Unmanned Surface Vehicles (USVs) have emerged as a major platform in maritime operations, capable of supporting a wide range of applications. USVs can help reduce labor costs, increase safety, save energy, and 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.
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