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Automated Water Segmentation and River Level Detection on Camera Images Using Transfer Learning

Lookup NU author(s): Dr Varun OjhaORCiD

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Abstract

© 2021, Springer Nature Switzerland AG. We investigate a deep transfer learning methodology to perform water segmentation and water level prediction on river camera images. Starting from pre-trained segmentation networks that provided state-of-the-art results on general purpose semantic image segmentation datasets ADE20k and COCO-stuff, we show that we can apply transfer learning methods for semantic water segmentation. Our transfer learning approach improves the current segmentation results of two water segmentation datasets available in the literature. We also investigate the usage of the water segmentation networks in combination with on-site ground surveys to automate the process of water level estimation on river camera images. Our methodology has the potential to impact the study and modelling of flood-related events.


Publication metadata

Author(s): Vandaele R, Dance SL, Ojha V

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 42nd DAGM German Conference on Pattern Recognition (DAGM GCPR 2020)

Year of Conference: 2021

Pages: 232-245

Print publication date: 17/03/2021

Online publication date: 16/03/2021

Acceptance date: 02/04/2018

ISSN: 0302-9743

Publisher: Springer

URL: https://doi.org/10.1007/978-3-030-71278-5_17

DOI: 10.1007/978-3-030-71278-5_17

Library holdings: Search Newcastle University Library for this item

Series Title: Lecture Notes in Computer Science

ISBN: 9783030712778


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