Evidence map›Paper›PMID 40431918›Full record

ArticleSensors (Basel, Switzerland)2025

Embedded Vision System for Thermal Face Detection Using Deep Learning.

Isidro Robledo-Vega, Scarllet Osuna-Tostado, Abraham Efraím Rodríguez-Mata, Carmen Leticia García-Mata, Pedro Rafael Acosta-Cano, Rogelio Enrique Baray-Arana

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. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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.

Isidro Robledo-VegaTecnológico Nacional de México/Instituto Tecnológico de Chihuahua, Ave. Tecnológico 2909, Chihuahua 31310, Mexico.ORCID 0000-0002-5180-3124
Scarllet Osuna-TostadoTecnológico Nacional de México/Instituto Tecnológico de Chihuahua, Ave. Tecnológico 2909, Chihuahua 31310, Mexico.
Abraham Efraím Rodríguez-MataTecnológico Nacional de México/Instituto Tecnológico de Chihuahua, Ave. Tecnológico 2909, Chihuahua 31310, Mexico.ORCID 0000-0002-0262-420X
Carmen Leticia García-MataTecnológico Nacional de México/Instituto Tecnológico de Chihuahua, Ave. Tecnológico 2909, Chihuahua 31310, Mexico.ORCID 0000-0002-3290-6638
Pedro Rafael Acosta-CanoTecnológico Nacional de México/Instituto Tecnológico de Chihuahua, Ave. Tecnológico 2909, Chihuahua 31310, Mexico.ORCID 0000-0003-4967-966X
Rogelio Enrique Baray-AranaTecnológico Nacional de México/Instituto Tecnológico de Chihuahua, Ave. Tecnológico 2909, Chihuahua 31310, Mexico.

Funding

Tecnológico Nacional de México 21048.24-P
6 · The paper itself

Abstract

Face detection technology is essential for surveillance and security projects; however, algorithms designed to detect faces in color images often struggle in poor lighting conditions. In this paper, we describe the development of an embedded vision system designed to detect human faces by analyzing images captured with thermal infrared sensors, thereby overcoming the limitations imposed by varying illumination conditions. All variants of the Ultralytics YOLOv8 and YOLO11 models were trained on the Terravic Facial IR database and tested on the Charlotte-ThermalFace database; the YOLO11 model achieved slightly higher performance metrics. We compared the performance of two embedded system boards: the NVIDIA Jetson Orin Nano and the NVIDIA Jetson Xavier NX, while running the trained model in inference mode. The NVIDIA Jetson Orin Nano performed better in terms of inference time. The developed embedded vision system based on these platforms accurately detects faces in thermal images in real-time.

Indexed as

deep learningembedded vision systemsface detectionthermal infrared sensors

Identifiers

PMID40431918
PMCPMC12115711

What OpenQuestion holds

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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.