Indoor fire and smoke detection based on optimized YOLOv5
Indoor fire and smoke detection based on optimized YOLOv5
About this item
Full title
Author / Creator
Publisher
United States: Public Library of Science
Journal title
Language
English
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Publisher
United States: Public Library of Science
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Scope and Contents
Contents
Ensuring safety and safeguarding indoor properties require reliable fire detection methods. Traditional detection techniques that use smoke, heat, or fire sensors often fail due to false positives and slow response time. Existing deep learning-based object detectors fall short of improved accuracy in indoor settings and real-time tracking, consider...
Alternative Titles
Full title
Indoor fire and smoke detection based on optimized YOLOv5
Authors, Artists and Contributors
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Primary Identifiers
Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_3ba813b777d5433eb08518d922482e30
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_3ba813b777d5433eb08518d922482e30
Other Identifiers
ISSN
1932-6203
E-ISSN
1932-6203
DOI
10.1371/journal.pone.0322052
