Maritime Search and Rescue Missions with Aerial Images: A Survey

2024-11-12

arXiv.org

AI Summary

Maritime search and rescue (SAR) faces critical challenges in aerial human detection, including slow response times, high miss rates for small targets, and poor robustness under low visibility (e.g., fog) and dynamic sea conditions (e.g., wave surge). Method: This paper presents a systematic review and methodological innovation, introducing the first evaluation framework integrating real-world and synthetic maritime-specific imagery—generated via GANs and NeRF—to enable scenario-adaptive model selection (e.g., YOLO, Faster R-CNN, traditional detectors) across fog, surge, and long-range settings. It constructs a structured technical taxonomy covering 2014–2024, quantitatively characterizing performance boundaries with respect to visibility, target scale, and sea-state perturbations. Contribution/Results: The work establishes lightweight deployment and cross-domain generalization as key future research directions, providing a reproducible, scenario-aware methodology to enhance SAR response speed and survivor survival rates.

Technology Category

• Search and Optimization: Evaluation and Analysis
• Computer Vision: Adversarial Attacks & Robustness
• Humans and AI: Crowd Sourcing and Human Computation

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs

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.

Problem

• Enhancing maritime search and rescue speed
• Developing UAV-based detection systems
• Improving deep learning for aerial image analysis

Innovation

• UAVs with cameras for maritime rescue
• Deep learning for aerial image analysis
• Synthetic data for scenario simulation