Maritime Search and Rescue Missions with Aerial Images: A Survey
Juan P. Martinez-Esteso, Francisco J. Castellanos, Jorge, Calvo-Zaragoza, Antonio Javier Gallego

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…
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Taxonomy
TopicsUnderwater Vehicles and Communication Systems · Robotics and Sensor-Based Localization
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
