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dc.contributor.advisorMyrvoll-Nilsen, Eirik
dc.contributor.advisorDølven, Knut Ola
dc.contributor.advisorGodtliebsen, Fred
dc.contributor.advisorNgo, Phuong
dc.contributor.advisorHernandez, Miguel
dc.contributor.authorSkotnes, Teodor Lynghaug
dc.date.accessioned2024-03-05T09:02:50Z
dc.date.available2024-03-05T09:02:50Z
dc.date.issued2024-01-18en
dc.description.abstractThis thesis introduces the first study of instance segmentation applied to gas flares in single beam echo sounder data. We develop a comprehensive dataset consisting of 1,414 images, featuring 5,142 segmented objects identified as gas flare. A key contribution is the adaptation of the Brier score specifically for instance segmentation. Further, we show how to adapt the Weighted Box Fusion (WBF) algorithm for instance segmentation. Using the newly developed Brier metric for instance segmentation, as well as the mAP metric, we show that our ensemble models fused with WBF are quantitatively as good as the average human expert. However, our qualitative analysis identifies critical areas where these models fall short, indicating the need for further refinement to reach human-level performance. The thesis concludes by proposing potential improvements and future research directions. We remark that if implemented, these could bridge the gap between human and machine-level performance.en_US
dc.identifier.urihttps://hdl.handle.net/10037/33111
dc.language.isoengen_US
dc.publisherUiT Norges arktiske universitetno
dc.publisherUiT The Arctic University of Norwayen
dc.rights.holderCopyright 2024 The Author(s)
dc.rights.urihttps://creativecommons.org/licenses/by-nc-sa/4.0en_US
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)en_US
dc.subject.courseIDSTA-3941
dc.subjectYolo, yolov5, ensemble, weighted box fusion, instance segmentation, gasflares, methane, echosounder, single beam, deep learning, seabed seepage, object detection,en_US
dc.titleDeep Learning Based Automatic Segmentation of Gas Flares in Single Beam Echo Sounder Dataen_US
dc.typeMastergradsoppgavenor
dc.typeMaster thesiseng


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