DeepSmoke: Deep learning model for smoke detection and segmentation in outdoor environments

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dc.contributor.authorKhan, Salman
dc.contributor.authorMuhammad, Khan
dc.contributor.authorHussain, Tanveer
dc.contributor.authorSer, Javier Del
dc.contributor.authorCuzzolin, Fabio
dc.contributor.authorBhattacharyya, Siddhartha
dc.contributor.authorAkhtar, Zahid
dc.contributor.authorde Albuquerque, Victor Hugo C.
dc.contributor.institutionIA
dc.date.issued2021-11-15
dc.descriptionPublisher Copyright: © 2021 Elsevier Ltd
dc.description.abstractFire disaster throughout the globe causes social, environmental, and economical damage, making its early detection and instant reporting essential for saving human lives and properties. Smoke detection plays a key role in early fire detection but majority of the existing methods are limited to either indoor or outdoor surveillance environments, with poor performance for hazy scenarios. In this paper, we present a Convolutional Neural Network (CNN)-based smoke detection and segmentation framework for both clear and hazy environments. Unlike existing methods, we employ an efficient CNN architecture, termed EfficientNet, for smoke detection with better accuracy. We also segment the smoke regions using DeepLabv3+, which is supported by effective encoders and decoders along with a pixel-wise classifier for optimum localization. Our smoke detection results evince a noticeable gain up to 3% in accuracy and a decrease of 0.46% in False Alarm Rate (FAR), while segmentation reports a significant increase of 2% and 1% in global accuracy and mean Intersection over Union (IoU) scores, respectively. This makes our method a best fit for smoke detection and segmentation in real-world surveillance settings.en
dc.description.sponsorshipThe work of Salman Khan and Fabio Cuzzolin has received funding from the European Union's Horizon 2020 research and innovation programme, under grant agreement No. 964505 (E-pi). J. Del Ser acknowledges funding support from the Basque Government through the ELKARTEK program (3KIA project, KK-2020/00049) and the consolidated research group MATHMODE (ref. T1294-19). The work of Salman Khan and Fabio Cuzzolin has received funding from the European Union’s Horizon 2020 research and innovation programme, under grant agreement No. 964505 (E-pi). J. Del Ser acknowledges funding support from the Basque Government through the ELKARTEK program (3KIA project, KK-2020/00049) and the consolidated research group MATHMODE (ref. T1294-19).
dc.description.statusPeer reviewed
dc.identifier.citationKhan , S , Muhammad , K , Hussain , T , Ser , J D , Cuzzolin , F , Bhattacharyya , S , Akhtar , Z & de Albuquerque , V H C 2021 , ' DeepSmoke : Deep learning model for smoke detection and segmentation in outdoor environments ' , Expert Systems with Applications , vol. 182 , 115125 . https://doi.org/10.1016/j.eswa.2021.115125
dc.identifier.doi10.1016/j.eswa.2021.115125
dc.identifier.issn0957-4174
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85108109315&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofExpert Systems with Applications
dc.relation.projectIDHorizon 2020 Framework Programme, H2020
dc.relation.projectIDEusko Jaurlaritza, T1294-19-KK-2020/00049
dc.relation.projectIDHorizon 2020, 964505
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subject.keywordsDisaster management
dc.subject.keywordsFoggy surveillance environment
dc.subject.keywordsSemantic segmentation
dc.subject.keywordsSmoke detection and segmentation
dc.subject.keywordsWildfires
dc.subject.keywordsGeneral Engineering
dc.subject.keywordsComputer Science Applications
dc.subject.keywordsArtificial Intelligence
dc.titleDeepSmoke: Deep learning model for smoke detection and segmentation in outdoor environmentsen
dc.typejournal article
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