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dc.contributor.authorJadon, Arpit
dc.contributor.authorOmama, Mohammad
dc.contributor.authorVarshney, Akshay
dc.contributor.authorAnsari, Mohammad Samar
dc.contributor.authorSharma, Rishabh
dc.identifier.citationJadon, A., Omama, M., Varshney, A., Ansari, M. S., Sharma, R. (2018). FireNet: a specialized lightweight fire & smoke detection model for real-time IoT applications. arXiv:1905.11922v2en_US
dc.identifier.otherArticles - Software Research Institute AITen_US
dc.description.abstractFire disasters typically result in lot of loss to life and property. It is therefore imperative that precise, fast, and possibly portable solutions to detect fire be made readily available to the masses at reasonable prices. There have been several research attempts to design effective and appropriately priced fire detection systems with varying degrees of success. However, most of them demonstrate a trade-off between performance and model size (which decides the model’s ability to be installed on portable devices). The work presented in this paper is an attempt to deal with both the performance and model size issues in one design. Toward that end, a ‘designed-from-scratch’ neural network, named FireNet, is proposed which is worthy on both the counts: (i) it has better performance than existing counterparts, and (ii) it is lightweight enough to be deploy-able on embedded platforms like Raspberry Pi. Performance evaluations on a standard dataset, as well as our own newly introduced custom-compiled fire dataset, are extremely encouraging.en_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Ireland*
dc.subjectComputer visionen_US
dc.subjectPattern recognitionen_US
dc.subjectConvolutional neural networksen_US
dc.subjectEmbedded systemsen_US
dc.subjectFire detectionen_US
dc.subjectInternet of thingsen_US
dc.subjectNeural networksen_US
dc.subjectSmoke detectionen_US
dc.titleFireNet: a specialized lightweight fire & smoke detection model for real-time IoT applications.en_US
dc.rights.accessOpen Accessen_US
dc.subject.departmentSoftware Research Institute AITen_US

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Attribution-NonCommercial-NoDerivs 3.0 Ireland
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Ireland