Analysing visual biases in coral imagery for bleaching detection

Master thesis (2022)

Authors

J.J.C. Vlekke Electrical Engineering, Mathematics and Computer Science

Contributors

M.J.T. Reinders Pattern Recognition and Bioinformatics - EEMCS (supervisor 1)
S. Pintea Leiden University Medical Center (supervisor 2)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2022 Jimmy Vlekke
More Info
expand_more

Published Date

15-12-2022

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Global warming causes coral bleaching which threatens the health and existence of coral reefs and therefore also the future of a lot of species, including human beings. Efforts to automate coral reef monitoring using annotated coral images to detect coral bleaching are hindered by the lack of a complete dataset that specifies the health and bleaching status of corals. We propose to combine publicly available data into a dataset and train a CNN for coral bleaching detection. This model performs surprisingly well. However, combining data from different sources gives rise to dataset biases which helps classifiers perform better and make them unreliable for unseen data. We try to detect such biases and document themsing several bias detection methods.