Analysis of U-Net Based Image Segmentation Model on Underwater Images of Different Species of Fishes

Image segmentation is one of the key tasks for image processing in robotic vision, navigation, virtual reality, and augmented reality. Segmentation in underwater images can help to overcome many challenges in exploring vast marine biological resources and gene banks. This work compares the performan...

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Bibliographic Details
Published in2021 International Symposium on Ocean Technology (SYMPOL) pp. 1 - 5
Main Authors Thampi, Lidiya, Thomas, Rosemol, Kamal, Suraj, Balakrishnan, Arun A., Mithun Haridas, T. P., Supriya, M. H.
Format Conference Proceeding
LanguageEnglish
Published IEEE 09.12.2021
Subjects
Analytical models
deep learning
Image segmentation
Marine technology
Navigation
Refining
semantic segmentation
Semantics
Solid modeling
U-Net
underwater images
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Summary:Image segmentation is one of the key tasks for image processing in robotic vision, navigation, virtual reality, and augmented reality. Segmentation in underwater images can help to overcome many challenges in exploring vast marine biological resources and gene banks. This work compares the performance of image segmentation using the U-Net architecture based model for underwater semantic image segmentation on selected five different fish species from the Fish4Knowledge image dataset. Analysis on the effect of different threshold values on refining the predicted segmented mask was also performed.
ISSN:2326-5566
DOI:10.1109/SYMPOL53555.2021.9689415