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dc.contributor.authorShees, Anwer
dc.contributor.authorAnsari, Mohammad Samar
dc.contributor.authorVarshney, Akshay
dc.contributor.authorAsghar, Mamoona
dc.contributor.authorKanwal, Nadia
dc.date.accessioned2022-09-23T10:10:52Z
dc.date.available2022-09-23T10:10:52Z
dc.identifierhttps://chesterrep.openrepository.com/bitstream/handle/10034/627190/Anwer___FireNet_v2.pdf?sequence=1
dc.identifier.citationShees, A., Ansari, M. S., Varshney, A., Asghar, M., & Kanwal, N. (2022 - forthcoming). FireNet-v2: Improved lightweight fire detection model for real-time IoT applications. Procedia Computer Science, vol(issue), pp. doien_US
dc.identifier.issn1877-0509
dc.identifier.urihttp://hdl.handle.net/10034/627190
dc.description.abstractFire hazards cause huge ecological, social and economical losses in day to day life. Due to the rapid increase in the prevalence of fire accidents, it has become vital to equip the assets with fire prevention systems. There have been numerous researches to build a fire detection model in order to avert such accidents, with recent approaches leveraging the enormous improvements in computer vision deep learning models. However, most deep learning models have to compromise with their performance and accurate detection to maintain a reasonable inference time and parameter count. In this paper, we present a customized lightweight convolution neural network for early detection of fire. By virtue of low parameter count, the proposed model is amenable to embedded applications in real-time fire monitoring equipment, and even upcoming fire monitoring approaches such as unmanned aerial vehicles (drones). The fire detection results show marked improvement over the predecessor low-parameter-count models, while further reducing the number of trainable parameters. The overall accuracy of FireNet-v2, which has only 318,460 parameters, was found to be 98.43% when tested over Foggia's dataset.en_US
dc.publisherElsevieren_US
dc.relation.urlhttps://www.sciencedirect.com/journal/procedia-computer-scienceen_US
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.subjectFire preventionen_US
dc.subjectFire detectionen_US
dc.titleFireNet-v2: Improved Lightweight Fire Detection Model for Real-Time IoT Applicationsen_US
dc.typeArticleen_US
dc.contributor.departmentAligarh Muslim University; University of Chester; Adobe India; University of Galway; Keele Universityen_US
dc.identifier.journalProcedia Computer Scienceen_US
or.grant.openaccessYesen_US
rioxxterms.funderUnfundeden_US
rioxxterms.identifier.projectUnfundeden_US
rioxxterms.versionAMen_US
rioxxterms.licenseref.startdate2023-12-31
dcterms.dateAccepted2022-08-03
rioxxterms.publicationdate2022
dc.date.deposited2022-09-23en_US


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