Publication:
Flood detection by using FCN-AlexNet

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dc.contributor.authorSon, Keumyoung
dc.contributor.authorYıldırım, Mustafa Eren
dc.contributor.authorPark, Jangsik
dc.contributor.authorSong, Jongkwan
dc.contributor.editorZhou, J.
dc.contributor.editorVerikas, A.
dc.contributor.editorNikolaev, D.P.
dc.contributor.editorRadeva, P.
dc.contributor.institutionSon, Keumyoung, Department of Electronics Engineering, Kyungsung University, Busan, South Korea
dc.contributor.institutionYıldırım, Mustafa Eren, Department of Electrical and Electronic Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey
dc.contributor.institutionPark, Jangsik, Department of Electronics Engineering, Kyungsung University, Busan, South Korea
dc.contributor.institutionSong, Jongkwan, Department of Electronics Engineering, Kyungsung University, Busan, South Korea
dc.date.accessioned2025-10-05T16:03:48Z
dc.date.issued2019
dc.description.abstractFloods are the natural disasters which can give serious damage to properties, roads, vehicles and even people. These damages bring huge payload both to individuals and governments. Thus, a system which can detect floods at early stage and warn the related offices immediately will be very useful for public. Detecting flooding early can save human lives, time, money for the government, as well as an important step to move towards smarter cities. In this paper, we propose the use of a deep learning architecture to detect floods in certain susceptible areas. We used FCN AlexNet deep learning architecture to train and test our dataset. Images of our dataset are collected from two PTZ cameras with different view angles. According to the experimental results, used system gets above 95% classification accuracy on both cameras. © 2019 Elsevier B.V., All rights reserved.
dc.description.sponsorshipHalmstad University
dc.identifier.conferenceName11th International Conference on Machine Vision, ICMV 2018
dc.identifier.conferencePlaceMunich
dc.identifier.doi10.1117/12.2523028
dc.identifier.issn0277786X
dc.identifier.issn1996756X
dc.identifier.scopus2-s2.0-85063473076
dc.identifier.urihttps://doi.org/10.1117/12.2523028
dc.identifier.urihttps://hdl.handle.net/20.500.14719/11384
dc.identifier.volume11041
dc.language.isoen
dc.publisherSPIE spie@spie.org
dc.relation.sourceProceedings of SPIE - The International Society for Optical Engineering
dc.subject.authorkeywordsCnn
dc.subject.authorkeywordsEmergency Management
dc.subject.authorkeywordsFcn Alexnet
dc.subject.authorkeywordsFlood Detection
dc.subject.authorkeywordsCameras
dc.subject.authorkeywordsComputer Vision
dc.subject.authorkeywordsDeep Learning
dc.subject.authorkeywordsDisasters
dc.subject.authorkeywordsRisk Management
dc.subject.authorkeywordsStatistical Tests
dc.subject.authorkeywordsClassification Accuracy
dc.subject.authorkeywordsEmergency Management
dc.subject.authorkeywordsFcn Alexnet
dc.subject.authorkeywordsFlood Detections
dc.subject.authorkeywordsHuman Lives
dc.subject.authorkeywordsLearning Architectures
dc.subject.authorkeywordsNatural Disasters
dc.subject.authorkeywordsUsed Systems
dc.subject.authorkeywordsFloods
dc.subject.indexkeywordsCameras
dc.subject.indexkeywordsComputer vision
dc.subject.indexkeywordsDeep learning
dc.subject.indexkeywordsDisasters
dc.subject.indexkeywordsRisk management
dc.subject.indexkeywordsStatistical tests
dc.subject.indexkeywordsClassification accuracy
dc.subject.indexkeywordsEmergency management
dc.subject.indexkeywordsFCN AlexNet
dc.subject.indexkeywordsFlood detections
dc.subject.indexkeywordsHuman lives
dc.subject.indexkeywordsLearning architectures
dc.subject.indexkeywordsNatural disasters
dc.subject.indexkeywordsUsed systems
dc.subject.indexkeywordsFloods
dc.titleFlood detection by using FCN-AlexNet
dc.typeConference Paper
dcterms.referencesChien, Steven A., An autonomous earth observing sensorweb, 2006 II, pp. 178-185, (2006), Di Martino, Gerardo, A novel approach for disaster monitoring: Fractal models and tools, IEEE Transactions on Geoscience and Remote Sensing, 45, 6, pp. 1559-1570, (2007), Borges, Paulo Vinicius Koerich, A probabilistic model for flood detection in video sequences, Proceedings - International Conference on Image Processing, ICIP, pp. 13-16, (2008), IEEE Instrumentation and Measurement Technology Conference Proceedings, (2012), Russakovsky, Olga, ImageNet Large Scale Visual Recognition Challenge, International Journal of Computer Vision, 115, 3, pp. 211-252, (2015), Mason, David C., Near real-time flood detection in urban and rural areas using high-resolution synthetic aperture radar images, IEEE Transactions on Geoscience and Remote Sensing, 50, 8, pp. 3041-3052, (2012), Krizhevsky, Alex, ImageNet classification with deep convolutional neural networks, Advances in Neural Information Processing Systems, 2, pp. 1097-1105, (2012), Very Deep Convolutional Networks for Large Scale Image Recognition, (2014), Szegedy, Christian, Going deeper with convolutions, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 07-12-June-2015, pp. 1-9, (2015), He, Kaiming, Deep residual learning for image recognition, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December, pp. 770-778, (2016)
dspace.entity.typePublication
local.indexed.atScopus
person.identifier.scopus-author-id57195940338
person.identifier.scopus-author-id35410613500
person.identifier.scopus-author-id49662072500
person.identifier.scopus-author-id55500997900

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