基于航空影像的海上搜救任务：综述
Maritime Search and Rescue Missions with Aerial Images: A Survey
AI总结：
本文综述基于航空影像的海上搜救相关研究，涵盖传统与深度学习方法，分析合成数据的应用价值，帮助读者识别场景适配方法并探讨未来趋势。
摘要：
海上搜救团队的响应速度至关重要，因为生存可能取决于此。近期技术进步推动了更高效的海上遇险人员定位系统的发展，例如使用配备摄像头和其他集成传感器的无人机（Unmanned Aerial Vehicles, UAVs）。过去十年中，若干研究人员致力于开发能够利用航空影像检测人员的自动系统，尤其借助深度学习的优势。本文对该主题的现有文献进行全面综述，分析了迄今为止提出的方法，包括传统技术以及基于机器学习和神经网络的更先进方法。此外，考虑到使用合成数据覆盖更广泛场景，无需部署团队收集数据——这是此类系统的主要障碍之一。总体而言，本文通过快速识别每种场景最适用的方法，帮助读者了解基于航空影像的海上人员检测领域，并深入讨论未来趋势及方向。
英文摘要：
The speed of response by search and rescue teams at sea is of vital importance, as survival may depend on it. Recent technological advancements have led to the development of more efficient systems for locating individuals involved in a maritime incident, such as the use of Unmanned Aerial Vehicles (UAVs) equipped with cameras and other integrated sensors. Over the past decade, several researchers have contributed to the development of automatic systems capable of detecting people using aerial images, particularly by leveraging the advantages of deep learning. In this article, we provide a comprehensive review of the existing literature on this topic. We analyze the methods proposed to date, including both traditional techniques and more advanced approaches based on machine learning and neural networks. Additionally, we take into account the use of synthetic data to cover a wider range of scenarios without the need to deploy a team to collect data, which is one of the major obstacles for these systems. Overall, this paper situates the reader in the field of detecting people at sea using aerial images by quickly identifying the most suitable methodology for each scenario, as well as providing an in-depth discussion and direction for future trends.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
