Automatic Coral Reef Annotation, Localization and Pixel-wise Parsing Using Mask R-CNN

dc.contributor.authorSoukup, Lukáš
dc.date.accessioned2022-03-28T10:00:30Z
dc.date.available2022-03-28T10:00:30Z
dc.date.issued2021
dc.description.abstract-translatedThis paper describes the methods that were used for annotation, localization and pixel-wise parsing of the coral reefs from underwater images. The proposed system achieved competitive results in the third edition of ImageCLEFcoral 2021 challenge. Specifically, in case of annotation and localization task achieved mean average precision with Intersection over Union (IoU) greater that 0.5 (mAP@0.5) 0.121 and in case of pixel-wise parsing task achieved mAP@0.5 0.075 on the test set. The proposed method is based on Mask R-CNN object detection and segmentation framework with online data augmentations.en
dc.format6 s.cs
dc.format.mimetypeapplication/pdf
dc.identifier.citationSOUKUP, L. Automatic Coral Reef Annotation, Localization and Pixel-wise Parsing Using Mask R-CNN. In Working Notes of CLEF 2021 - Conference and Labs of the Evaluation Forum. Bucharest: CEUR-WS, 2021. s. 1359-1364. ISBN: neuvedeno , ISSN: 1613-0073cs
dc.identifier.isbnneuvedeno
dc.identifier.issn1613-0073
dc.identifier.obd43933957
dc.identifier.uri2-s2.0-85113420229
dc.identifier.urihttp://hdl.handle.net/11025/47278
dc.language.isoenen
dc.project.IDSGS-2019-027/Inteligentní metody strojového vnímání a porozumění 4cs
dc.project.ID90140/Velká výzkumná infrastruktura_(J) - e-INFRA CZcs
dc.publisherCEUR-WSen
dc.relation.ispartofseriesWorking Notes of CLEF 2021 - Conference and Labs of the Evaluation Forumen
dc.rights© authorsen
dc.rights.accessopenAccessen
dc.subject.translatedObject detection, Semantic segmentation, Neural networks, Deep learning, Machine learning, Coral reefs detection, Coral reefs segmentationen
dc.titleAutomatic Coral Reef Annotation, Localization and Pixel-wise Parsing Using Mask R-CNNen
dc.typekonferenční příspěvekcs
dc.typeConferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen

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