Evidence map›Paper›PMID 31416131›Full record

ArticleSensors (Basel, Switzerland)2019

Unsupervised Human Detection with an Embedded Vision System on a Fully Autonomous UAV for Search and Rescue Operations.

Eleftherios Lygouras, Nicholas Santavas, Anastasios Taitzoglou, Konstantinos Tarchanidis, Athanasios Mitropoulos, Antonios Gasteratos

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 1 pooled it
11.0field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

24 citing papers in PubMed, 1 synthesis or guideline pooled it, 240 citations in OpenAlex.

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  19. Towards Fully Autonomous UAVs: A Survey.Sensors (Basel, Switzerland) · 2021
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 1 country.

Eleftherios LygourasDepartment of Production and Management Engineering, Democritus University of Thrace, 67100 Xanthi, Greece. elygoura@ee.duth.gr.
Nicholas SantavasDepartment of Production and Management Engineering, Democritus University of Thrace, 67100 Xanthi, Greece.ORCID 0000-0002-4235-454X
Anastasios TaitzoglouDepartment of Production and Management Engineering, Democritus University of Thrace, 67100 Xanthi, Greece.ORCID 0000-0002-4425-3136
Konstantinos TarchanidisDepartment of Petroleum, Natural Gas and Mechanical Engineering, Eastern Macedonia and Thrace Institute of Technology, 65404 Kavala, Greece.
Athanasios MitropoulosDepartment of Petroleum, Natural Gas and Mechanical Engineering, Eastern Macedonia and Thrace Institute of Technology, 65404 Kavala, Greece.
Antonios GasteratosDepartment of Production and Management Engineering, Democritus University of Thrace, 67100 Xanthi, Greece.ORCID 0000-0002-5421-0332
Democritus University of Thrace · GRTechnological Educational Institute of Eastern Macedonia and Thrace · GR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Unmanned aerial vehicles (UAVs) play a primary role in a plethora of technical and scientific fields owing to their wide range of applications. In particular, the provision of emergency services during the occurrence of a crisis event is a vital application domain where such aerial robots can contribute, sending out valuable assistance to both distressed humans and rescue teams. Bearing in mind that time constraints constitute a crucial parameter in search and rescue (SAR) missions, the punctual and precise detection of humans in peril is of paramount importance. The paper in hand deals with real-time human detection onboard a fully autonomous rescue UAV. Using deep learning techniques, the implemented embedded system was capable of detecting open water swimmers. This allowed the UAV to provide assistance accurately in a fully unsupervised manner, thus enhancing first responder operational capabilities. The novelty of the proposed system is the combination of global navigation satellite system (GNSS) techniques and computer vision algorithms for both precise human detection and rescue apparatus release. Details about hardware configuration as well as the system's performance evaluation are fully discussed.

Indexed as

Deep LearningGeographic Information SystemsHumansNeural Networks, ComputerRescue Workdeep learninghuman detectionsearch and rescue (SAR) missionsunmanned aerial vehicles (UAVs)

Identifiers

PMID31416131
PMCPMC6720834
OpenAlexW2968405839

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.