Blar i Brage IMR på forfatter "Brautaset, Olav"

Viser treff 1-4 av 4

    • Acoustic classification in multifrequency echosounder data using deep convolutional neural networks 

      Brautaset, Olav; Waldeland, Anders Ueland; Johnsen, Espen; Malde, Ketil; Eikvil, Line; Salberg, Arnt-Børre; Handegard, Nils Olav (Peer reviewed; Journal article, 2020)
      Acoustic target classification is the process of assigning observed acoustic backscattering intensity to an acoustic category. A deep learning strategy for acoustic target classification using a convolutional network is ...

    • Evaluation of echosounder data preparation strategies for modern machine learning models 

      Ordonez, Alba; Utseth, Ingrid; Brautaset, Olav; Korneliussen, Rolf; Handegard, Nils Olav (Peer reviewed; Journal article, 2022)
      Fish stock assessment and management requires accurate estimates of fish abundance, which are typically derived from echosounder observations using acoustic target classification (ATC). Skilled operators are regularly ...

    • Fisheries acoustics and Acoustic Target Classification - Report from the COGMAR/CRIMAC workshop on machine learning methods in fisheries acoustics 

      Handegard, Nils Olav; Andersen, Lars Nonboe; Brautaset, Olav; Choi, Changkyu; Eliassen, Inge Kristian; Heggelund, Yngve; Hestnes, Arne Johan; Malde, Ketil; Osland, Håkon; Ordonez, Alba; Patel, Ruben; Pedersen, Geir; Umar, Ibrahim; Engeland, Tom Van; Vatnehol, Sindre (Rapport fra havforskningen;2021 - 25, Research report, 2021)
      This report documents a workshop organised by the COGMAR and CRIMAC projects. The objective of the workshop was twofold. The first objective was to give an overview of ongoing work using machine learning for Acoustic Target ...

    • Semi-supervised target classification in multi-frequency echosounder data 

      Choi, Changkyu; Kampffmeyer, Michael; Handegard, Nils Olav; Salberg, Arnt Børre; Brautaset, Olav; Eikvil, Line; Jenssen, Robert (Peer reviewed; Journal article, 2021)
      Acoustic target classification in multi-frequency echosounder data is a major interest for the marine ecosystem and fishery management since it can potentially estimate the abundance or biomass of the species. A key problem ...