Cristin-resultat-ID: 1952309
Sist endret: 8. november 2021, 12:50
Resultat
Vitenskapelig foredrag
2021

Semi-supervised Semantic Segmentation in Multi-frequency Echosounder Data

Bidragsytere:
  • Changkyu Choi

Presentasjon

Navn på arrangementet: Northern Lights Deep Learning conference 2021 (NLDL)
Sted: UiT The Arctic University of Norway
Dato fra: 18. januar 2021
Dato til: 20. januar 2021

Arrangør:

Arrangørnavn: Michael Kampffmeyer, Robert Jenssen

Om resultatet

Vitenskapelig foredrag
Publiseringsår: 2021

Beskrivelse Beskrivelse

Tittel

Semi-supervised Semantic Segmentation in Multi-frequency Echosounder Data

Sammendrag

Multi-frequency echosounder data has been considered as a valuable source of information for the investigation of the underwater environment, such as the benthic habitat observation, and the marine organism detection of various species of fish and plankton. Recent studies show that analysis models of the echosounder data based on supervised deep learning outperform the conventional methods by applying semantic segmentation to the echosounder data. However, there is still a severe limitation on these methods since they heavily depend on the annotation of the data, where the annotation process is manual and requires domain knowledge so that it is extremely expensive in time and cost. To tackle the limitation, we propose a novel semi-supervised deep learning model designed for semantic segmentation that leverages a few annotated data samples together with vast amounts of unannotated data samples, all in a single model. Specifically, two inter-connected losses, namely a self-supervised loss and a supervised loss, optimize one shared convolutional neural network in an alternating manner, where the self-supervised loss exploits the underlying structure of the data, while the supervised loss enforces a certain consistency to the given classes using the labeled data samples. We also provide the analysis based on the preliminary results of our method acquired with a down-scaled dataset, and discuss the insight based on the analysis. The presentation is also available online via YouTube channel of UiT Machine Learning Group (https://youtu.be/LxH8qV15AbQ).

Bidragsytere

Changkyu Choi

  • Tilknyttet:
    Forfatter
    ved Institutt for fysikk og teknologi ved UiT Norges arktiske universitet
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