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.

本文针对海上搜索与救援团队的反应速度对生存的重要性，综述了近年来无人机及深度学习技术在利用空中图像检测海上遇险人员的应用。研究通过分析现有方法及采用合成数据的策略，提供了适用于不同场景的最佳方法，并为未来研究方向提出了深入的讨论。

海上搜索与救援任务中的空中图像：综述