Repository logo
 
Publication

Enhanced AutomaticWildfire Detection System Using Big Data and EfficientNets

2024Review article Open access
Simple item page
dc.contributor.authorFernandes, Armando
dc.contributor.authorUtkin, Andrei
dc.contributor.authorChaves, Paulo
dc.date.accessioned2025-11-11T10:33:12Z
dc.date.available2025-11-11T10:33:12Z
dc.date.issued2024
dc.description.abstractPrevious works have shown the effectiveness of EfficientNet—a convolutional neural network built upon the concept of compound scaling—in automatically detecting smoke plumes at a distance of several kilometres in visible camera images. Building on these results, we have created enhanced EfficientNet models capable of precisely identifying the smoke location due to the introduction of a mosaic-like output and achieving extremely reduced false positive percentages due to using partial AUROC and applying class imbalance. Our EfficientNets beat InceptionV3 and MobileNetV2 in the same dataset and achieved a true detection percentage of 89.2% and a false positive percentage of only 0.306% across a test set with 17,023 images. The complete dataset used in this study contains 26,204 smoke and 51,075 non-smoke images. This makes it one of the largest, if not the most extensive, datasets reported in the scientific literature for smoke plume imagery. So, the achieved percentages are not only among the best reported for this application but are also among the most reliable due to the extent and representativeness of the dataset.eng
dc.identifier.citationFernandes, A., Utkin, A., & Chaves, P. (2024). Enhanced Automatic Wildfire Detection System Using Big Data and EfficientNets. Fire, 7(8), 286. https://doi.org/10.3390/fire7080286
dc.identifier.doi10.1016/j.jfa.2024.110494
dc.identifier.urihttp://hdl.handle.net/10400.26/59607
dc.language.isoeng
dc.peerreviewedyes
dc.publisherMDPI
dc.relation.hasversionhttps://www.mdpi.com/2571-6255/7/8/286
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectsmoke plume
dc.subjectfalse alarms
dc.subjectmachine learning
dc.subjectpattern recognition
dc.titleEnhanced AutomaticWildfire Detection System Using Big Data and EfficientNetseng
dc.typereview article
dspace.entity.typePublication
oaire.citation.issue8
oaire.citation.startPage286
oaire.citation.titleFire
oaire.citation.volume7
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Enhanced Automatic Wildfire Detection System Using Big Data and EfficientNets.pdf
Size:
6.13 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.85 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

ENIDH – EEM - Artigo Científico