dc.contributor.author | Schellewald, Christian | |
dc.contributor.author | Stahl, Annette | |
dc.date.accessioned | 2022-11-21T09:30:58Z | |
dc.date.available | 2022-11-21T09:30:58Z | |
dc.date.created | 2022-11-18T11:31:07Z | |
dc.date.issued | 2022 | |
dc.identifier.issn | 2405-8963 | |
dc.identifier.uri | https://hdl.handle.net/11250/3033076 | |
dc.description.abstract | Protecting the remaining wild salmon stock in Norway is of utmost importance and requires that farmed salmon cannot escape from aquaculture sites. As holes in net-cages are responsible for a large fraction of the escaped salmon the industry has to perform frequent inspections of the _sh cage integrity. In this paper we propose an image processing and computer vision based attention mechanism towards a more automated _sh-cage inspection. The presented algorithm allows to indicate areas in videos showing net-pen locations where potential holes are present. We show the e_ectivity of the approach on video-recordings of holes also in commercial _sh-cages. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Elsevier | en_US |
dc.title | Irregularity Detection in Net Pens Exploiting Computer Vision | en_US |
dc.title.alternative | Irregularity Detection in Net Pens Exploiting Computer Vision | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | submittedVersion | en_US |
dc.source.journal | IFAC-PapersOnLine | en_US |
dc.relation.project | Norges forskningsråd: 304667 | en_US |
dc.relation.project | Norges forskningsråd: 223254 | en_US |
cristin.ispublished | true | |
cristin.fulltext | preprint | |
cristin.qualitycode | 1 | |