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

• Published in: Marine pollution bulletin Vol. 181; p. 113853
• Main Authors: Moorton, Zoe, Kurt, Zeyneb, Woo, Wai Lok
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.08.2022
• Subjects: aquatic organisms; Artificial intelligence; Deep learning; industry; Marine debris; marine pollution; Neural network; neural networks; Ocean pollution; synthetic products; wildlife; Deep learning; Neural network; Artificial intelligence; Ocean pollution; Marine debris
• ISSN: 0025-326X; 1879-3363; 1879-3363
• DOI: 10.1016/j.marpolbul.2022.113853

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 1644 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. •Deep learning to distinguish between synthetic debris and marine life•Original database of underwater imagery•Convolutional neural network binary classification