Early wildfire smoke detection based on motion-based geometric image transformation and deep convolutional generative adversarial networks

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buir.contributor.authorAslan, Süleyman
buir.contributor.authorGüdükbay, Uğur
buir.contributor.authorÇetin, A. Enis
buir.contributor.orcidÇetin, A. Enis|0000-0002-3449-1958
dc.citation.epage8319en_US
dc.citation.spage8315en_US
dc.contributor.authorAslan, Süleymanen_US
dc.contributor.authorGüdükbay, Uğuren_US
dc.contributor.authorTöreyin, B. U.en_US
dc.contributor.authorÇetin, A. Enisen_US
dc.coverage.spatialBrighton, United Kingdomen_US
dc.date.accessioned2020-01-28T12:47:35Z
dc.date.available2020-01-28T12:47:35Z
dc.date.issued2019
dc.departmentDepartment of Computer Engineeringen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.descriptionDate of Conference: 12-17 May 2019en_US
dc.descriptionConference Name: 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019en_US
dc.description.abstractEarly detection of wildfire smoke in real-time is essentially important in forest surveillance and monitoring systems. We propose a vision-based method to detect smoke using Deep Convolutional Generative Adversarial Neural Networks (DC-GANs). Many existing supervised learning approaches using convolutional neural networks require substantial amount of labeled data. In order to have a robust representation of sequences with and without smoke, we propose a two-stage training of a DCGAN. Our training framework includes, the regular training of a DCGAN with real images and noise vectors, and training the discriminator separately using the smoke images without the generator. Before training the networks, the temporal evolution of smoke is also integrated with a motion-based transformation of images as a pre-processing step. Experimental results show that the proposed method effectively detects the smoke images with negligible false positive rates in real-time.en_US
dc.description.provenanceSubmitted by Zeynep Aykut (zeynepay@bilkent.edu.tr) on 2020-01-28T12:47:35Z No. of bitstreams: 1 Early_wildfire_smoke_detection_based_on_motion_based_geometric_image_transformation_and_deep_convolutional_generative_adversarial_networks.pdf: 1413065 bytes, checksum: 1d3a7cd4a1531a67ce102f85a3a9abe4 (MD5)en
dc.description.provenanceMade available in DSpace on 2020-01-28T12:47:35Z (GMT). No. of bitstreams: 1 Early_wildfire_smoke_detection_based_on_motion_based_geometric_image_transformation_and_deep_convolutional_generative_adversarial_networks.pdf: 1413065 bytes, checksum: 1d3a7cd4a1531a67ce102f85a3a9abe4 (MD5) Previous issue date: 2019en
dc.description.sponsorshipThe Institute of Electrical and Electronics Engineers Signal Processing Societyen_US
dc.identifier.doi10.1109/ICASSP.2019.8683629en_US
dc.identifier.eisbn9781479981311
dc.identifier.eissn2379-190X
dc.identifier.isbn9781479981328
dc.identifier.issn1520-6149
dc.identifier.urihttp://hdl.handle.net/11693/52879
dc.language.isoEnglishen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.isversionofhttps://dx.doi.org/10.1109/ICASSP.2019.8683629en_US
dc.source.titleProceedings of the 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019en_US
dc.subjectWildfiresen_US
dc.subjectSmoke detectionen_US
dc.subjectDeep Convolutional Generative Adversarial Networks (DCGAN)en_US
dc.titleEarly wildfire smoke detection based on motion-based geometric image transformation and deep convolutional generative adversarial networksen_US
dc.typeConference Paperen_US

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