Maritime Search and Rescue Missions with Aerial Images: A Survey

TL;DR

This survey reviews recent advancements in using aerial imagery and deep learning for maritime search and rescue, highlighting methods, challenges, and future directions in automatic person detection at sea.

Contribution

It provides a comprehensive overview of existing techniques, including traditional and machine learning approaches, and discusses the use of synthetic data for maritime rescue applications.

Findings

Deep learning enhances detection accuracy.

Synthetic data helps cover diverse scenarios.

Traditional methods are being complemented by neural networks.

Abstract

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.