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Image-Based Fire Detection in Industrial Environments with YOLOv4
Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research (CAISR).
Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research (CAISR).
Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research (CAISR).ORCID iD: 0000-0002-9696-7843
Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research (CAISR).ORCID iD: 0000-0002-1400-346X
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2023 (English)In: Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods ICPRAM / [ed] Maria De Marsico; Gabriella Sanniti di Baja; Ana Fred, Setúbal: SciTePress, 2023, Vol. 1, p. 379-386Conference paper, Published paper (Refereed)
Abstract [en]

Fires have destructive power when they break out and affect their surroundings on a devastatingly large scale. The best way to minimize their damage is to detect the fire as quickly as possible before it has a chance to grow. Accordingly, this work looks into the potential of AI to detect and recognize fires and reduce detection time using object detection on an image stream. Object detection has made giant leaps in speed and accuracy over the last six years, making real-time detection feasible. To our end, we collected and labeled appropriate data from several public sources, which have been used to train and evaluate several models based on the popular YOLOv4 object detector. Our focus, driven by a collaborating industrial partner, is to implement our system in an industrial warehouse setting, which is characterized by high ceilings. A drawback of traditional smoke detectors in this setup is that the smoke has to rise to a sufficient height. The AI models brought forward in this research managed to outperform these detectors by a significant amount of time, providing precious anticipation that could help to minimize the effects of fires further.

Place, publisher, year, edition, pages
Setúbal: SciTePress, 2023. Vol. 1, p. 379-386
Series
International Conference on Pattern Recognition Applications and Methods (ICPRAM), ISSN 2184-4313
Keywords [en]
Fire detection, Smoke Detection, Machine learning, Computer Vision, YOLOv4
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:hh:diva-48793DOI: 10.5220/0011689400003411ISBN: 978-989-758-626-2 (print)OAI: oai:DiVA.org:hh-48793DiVA, id: diva2:1717734
Conference
12th International Conference on Pattern Recognition Applications and Methods, ICPRAM, Lisbon, Portugal, February 22-24, 2023
Projects
2021-05038 Vinnova DIFFUSE Disentanglement of Features For Utilization in Systematic Evaluation
Part of project
Facial Analysis in the Era of Mobile Devices and Face Masks, Swedish Research Council
Funder
Swedish Research CouncilVinnovaAvailable from: 2022-12-09 Created: 2022-12-09 Last updated: 2024-06-17Bibliographically approved

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fulltext(8795 kB)38 downloads
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Hernandez-Diaz, KevinAlonso-Fernandez, Fernando

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Zell, OttoPåsson, JoelHernandez-Diaz, KevinAlonso-Fernandez, Fernando
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