Hurricane-Faster R-CNN-JS: Hurricane detection with faster R-CNN using artificial Jellyfish Search (JS) optimizer

dc.authorid0000-0002-0381-9631en_US
dc.authorid0000-0002-8611-701Xen_US
dc.contributor.authorKızıloluk, Soner
dc.contributor.authorSert, Eser
dc.date.accessioned2022-05-05T12:42:42Z
dc.date.available2022-05-05T12:42:42Z
dc.date.issued2022en_US
dc.departmentMTÖ Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractA hurricane is a type of storm called tropical cyclone (TC) and is likely to lead to severe storms and heavy rains. An early detection of hurricanes using satellite images can alarm people about upcoming disasters and thus minimize any casualties and material losses. Faster R-CNN is one of the most popular and recent object detection approaches. In the present study, AlexNet hyperparameters, which is a CNN model used as a feature extractor in Faster R-CNN, were optimized using artificial Jellyfish Search (JS), which is a recent algorithm, in order to propose a Faster R-CNN with a higher performance. The proposed approach is called Hurricane-Faster R-CNN-JS, since it is used as an early hurricane detection approach on satellite images before these hurricanes reach the land. The results of the present study demonstrated that hyperparameter optimization increased the detection performance of the proposed approach by 10% compared to AlexNet without optimized hyperparameters. As feature extractors of Faster R-CNN, the present study benefited from various architectures such as MobileNet-V2, GoogLeNet, AlexNet, ResNet 18, ResNet 50, VGG-16 and VGG-19 without any optimized hyperparameters to compare them with the proposed approach. It was observed that Average Precision (AP) of Hurricane-Faster R-CNN-JS was 97.39%, which was a remarkably higher AP level compared to other approaches.en_US
dc.identifier.citationKızıloluk, S., & Sert, E. (2022). Hurricane-Faster R-CNN-JS: Hurricane detection with faster R-CNN using artificial Jellyfish Search (JS) optimizer. Multimedia Tools and Applications, 1-19.en_US
dc.identifier.doi10.1007/s11042-022-13156-9
dc.identifier.endpage19en_US
dc.identifier.issn1380-7501en_US
dc.identifier.issn1573-7721en_US
dc.identifier.scopus2-s2.0-85128742128en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1en_US
dc.identifier.urihttps://doi.org/10.1007/s11042-022-13156-9
dc.identifier.urihttps://hdl.handle.net/20.500.12899/1059
dc.identifier.wosWOS:000785933700011en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorKızıloluk, Soner
dc.institutionauthorSert, Eser
dc.language.isoenen_US
dc.publisherSPRINGERen_US
dc.relation.ispartofMultimedia Tools and Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDeep learningen_US
dc.subjectHyperparameter optimizationen_US
dc.subjectJellyfish searchen_US
dc.subjectAlexNeten_US
dc.subjectFaster R-CNNen_US
dc.titleHurricane-Faster R-CNN-JS: Hurricane detection with faster R-CNN using artificial Jellyfish Search (JS) optimizeren_US
dc.typeArticleen_US

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