Attention based CNN model for fire detection and localization in real-world images

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dc.authorid0000-0002-1399-7722en_US
dc.authorid0000-0003-1840-9958en_US
dc.authorid0000-0002-4099-1254en_US
dc.authorid0000-0002-4915-9325en_US
dc.authorid0000-0003-2675-3484en_US
dc.contributor.authorMajid, Saima
dc.contributor.authorAlenezi, Fayadh
dc.contributor.authorMasood, Sarfaraz
dc.contributor.authorAhmad, Musheer
dc.contributor.authorGündüz, Emine Selda
dc.contributor.authorPolat, Kemal
dc.date.accessioned2023-06-20T11:12:39Z
dc.date.available2023-06-20T11:12:39Z
dc.date.issued2022en_US
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractFire is a severe natural calamity that causes significant harm to human lives and the environment. Recent works have proposed the use of computer vision for developing a cost-effective automated fire detection system. This paper presents a custom framework for detecting fire using transfer learning with state-of-the-art CNNs trained over real-world fire breakout images. The framework also uses the Grad-CAM method for the visualization and localization of fire in the images. The model also uses an attention mechanism that has significantly assisted the network in achieving better performances. It was observed through Grad-CAM results that the proposed use of attention led the model towards better localization of fire in the images. Among the plethora of models explored, the EfficientNetB0 emerged as the best-suited network choice for the problem. For the selected real-world fire image dataset, a test accuracy of 95.40% strongly supports the model's efficiency in detecting fire from the presented image samples. Also, a very high recall of 97.61 highlights that the model has negligible false negatives, suggesting the network to be reliable for fire detection.en_US
dc.identifier.citationMajid, S., Alenezi, F., Masood, S., Ahmad, M., Gündüz, E. S., & Polat, K. (2022). Attention based CNN model for fire detection and localization in real-world images. Expert Systems with Applications, 189, 116114.en_US
dc.identifier.doi10.1016/j.eswa.2021.116114
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.scopus2-s2.0-85118146897en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.eswa.2021.116114
dc.identifier.urihttps://hdl.handle.net/20.500.12491/11152
dc.identifier.volume189en_US
dc.identifier.wosWOS:000714414800002en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorPolat, Kemal
dc.language.isoenen_US
dc.publisherPERGAMON-ELSEVIER SCIENCE LTDen_US
dc.relation.ispartofExpert Systems with Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectFire Detectionen_US
dc.subjectCNNen_US
dc.subjectAttention Mechanismen_US
dc.subjectTransfer Learningen_US
dc.subjectGrad-CAMen_US
dc.subjectSmoke Detectionen_US
dc.titleAttention based CNN model for fire detection and localization in real-world imagesen_US
dc.typeArticleen_US

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