Evidence map›Paper›PMID 41228902›Full record

ArticleSensors (Basel, Switzerland)2025

Perimeter Security Utilizing Thermal Object Detection.

Georgios Orfanidis, Konstantinos Ioannidis, Stefanos Vrochidis, Ioannis Kompatsiaris

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Georgios OrfanidisCentre for Research and Technology Hellas-CERTH, Thermi, GR 57001 Thessaloniki, Greece.ORCID 0000-0003-2908-7843
Konstantinos IoannidisCentre for Research and Technology Hellas-CERTH, Thermi, GR 57001 Thessaloniki, Greece.ORCID 0000-0001-6767-8762
Stefanos VrochidisCentre for Research and Technology Hellas-CERTH, Thermi, GR 57001 Thessaloniki, Greece.ORCID 0000-0002-2505-9178
Ioannis KompatsiarisCentre for Research and Technology Hellas-CERTH, Thermi, GR 57001 Thessaloniki, Greece.ORCID 0000-0001-6447-9020

Funding

European Union's Horizon Europe 101121258
6 · The paper itself

Abstract

In recent years, an increasing interest in artificial intelligence applications in a widespread spectrum of fields which include, among others, robotics, communications, artistic creations, security and protection technologies, etc., has been observed. Of the latter categories, one field which has largely benefitted is surveillance and security technologies. This fact is combined with an increase in omnipresent automatic surveillance system installations which pave the way to new technologies. Technologies that are being promoted are the ones offering uninterrupted, robust, efficient and reliable operation. In this work, we examine the ability of thermal automatic detection systems to fulfill their role as an essential part of such a mechanism. The primary advantage of thermal detection is the potential to provide a 24-h uninterrupted detection service exploiting its innate robustness against environmental or weather changes and shifts in illumination conditions. For providing a reliable security mechanism, a second requirement is considered sine qua non: the efficiency of the system in order to provide timely alerts for potential threats and incidents. In this work, we evaluate various efficient object detection models operating solely in the thermal/infrared spectrum to examine their role as potential backbone detectors in surveillance systems.

Indexed as

critical infrastructureInfraredobject detectionsurveillancethermal

Identifiers

PMID41228902
PMCPMC12610692

What OpenQuestion holds

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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.