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2022 | OriginalPaper | Buchkapitel

Human Detection in Drone Images Using YOLO for Search-and-Rescue Operations

verfasst von : Sergio Caputo, Giovanna Castellano, Francesco Greco, Corrado Mencar, Niccolò Petti, Gennaro Vessio

Erschienen in: AIxIA 2021 – Advances in Artificial Intelligence

Verlag: Springer International Publishing

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Abstract

Today, unmanned aerial vehicles, more commonly known as drones, can be equipped with high-resolution cameras and embedded GPUs powerful enough to provide effective and efficient aid to Search-and-Rescue (SAR) operations in remote and hostile environments. Locating victims, who may be unconscious or injured, as quickly as possible is critical to improving their chance of survival. Therefore, using drones as flying machines for computer vision can increase the detection rate while reducing rescue time. In this paper, we present the results of an experimental evaluation in which we used the latest, lightweight version of the YOLO detection algorithm, namely YOLOv5, to detect humans in danger using two new benchmark datasets specifically designed for SAR with drones. The results obtained are encouraging, as they are competitive with respect to the state-of-the-art in terms of detection accuracy, but with much faster detection time.

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Titel: Human Detection in Drone Images Using YOLO for Search-and-Rescue Operations
verfasst von: Sergio Caputo
Giovanna Castellano
Francesco Greco
Corrado Mencar
Niccolò Petti
Gennaro Vessio
Verlag: Springer International Publishing
Buch: AIxIA 2021 – Advances in Artificial Intelligence
Print ISBN: 978-3-031-08420-1

Electronic ISBN: 978-3-031-08421-8

Copyright-Jahr: 2022
DOI: https://doi.org/10.1007/978-3-031-08421-8_22

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