dc.contributor.author | Ansari, Sadaf | |
dc.contributor.author | Desai, Dattesh V. | |
dc.contributor.author | Saad, Aya | |
dc.contributor.author | Stahl, Annette | |
dc.date.accessioned | 2024-06-17T14:09:32Z | |
dc.date.available | 2024-06-17T14:09:32Z | |
dc.date.created | 2023-12-12T14:13:07Z | |
dc.date.issued | 2023 | |
dc.identifier.citation | Current Science. 2023, 125 (11), 1259-1266. | en_US |
dc.identifier.issn | 0011-3891 | |
dc.identifier.uri | https://hdl.handle.net/11250/3134362 | |
dc.description.abstract | Zooplankton are key ecological components of the marine food web. Currently, laboratory-based methods of zooplankton identification are manual, time-consuming, prone to human error and require expert taxonomists. Therefore, alternative methods are needed. In this study, we describe, implement and compare the performance of six state-of-the-art single-stage deep learning models for automated zooplankton identification. The highest prediction accuracy achieved is 99.50%. The fastest detection speed is 285 images per second, making the models suitable for real-time zooplankton classification. We validate the predictions of the generated models on unseen images. The results demonstrate the capabilities of the latest deep learning models in zooplankton identification. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Current Science Association | en_US |
dc.title | Implications of single-stage deep learning networks in real-time zooplankton identification | en_US |
dc.title.alternative | Implications of single-stage deep learning networks in real-time zooplankton identification | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | publishedVersion | en_US |
dc.source.pagenumber | 1259-1266 | en_US |
dc.source.volume | 125 | en_US |
dc.source.journal | Current Science | en_US |
dc.source.issue | 11 | en_US |
dc.identifier.doi | 10.18520/cs/v125/i11/1259-1266 | |
dc.identifier.cristin | 2212439 | |
dc.relation.project | Norges forskningsråd: 262741 | en_US |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |