Is the use of deep learning an appropriate means to locate debris in the ocean without harming aquatic wildlife?

Moorton, Zoe, Kurt, Zeyneb and Woo, Wai Lok (2022) Is the use of deep learning an appropriate means to locate debris in the ocean without harming aquatic wildlife? Marine Pollution Bulletin. ISSN 0025-326X (In Press)

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Abstract

With the global issue of marine debris ever expanding, it is imperative that the technology industry steps in. The aim is to find if deep learning can successfully distinguish between marine life and synthetic debris underwater. This study assesses whether we could safely clean up our oceans with Artificial Intelligence without disrupting the delicate balance of aquatic ecosystems.Our research compares a simple convolutional neural network with a VGG-16 model using an original database of 1,644 underwater images and a binary classification to sort synthetic material from aquatic life. Our results show first insights to safely distinguishing between debris and life.

Item Type: Article
Uncontrolled Keywords: deep learning, artificial intelligence, neural network, ocean pollution, marine debris
Subjects: F700 Ocean Sciences
G600 Software Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Related URLs:
Depositing User: Elena Carlaw
Date Deposited: 21 Jun 2022 15:53
Last Modified: 21 Jun 2022 16:00
URI: http://nrl.northumbria.ac.uk/id/eprint/49387

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