Is the use of Deep Learning and Artificial Intelligence an appropriate means to locate debris in the ocean without harming aquatic wildlife?

TL;DR

This study evaluates whether deep learning, specifically CNNs, can effectively identify ocean debris without harming marine life, aiming to enable eco-friendly ocean cleanup efforts.

Contribution

It introduces a custom deep learning model with a unique underwater image database to distinguish debris from marine life, emphasizing ecosystem protection.

Findings

Successful binary classification between debris and marine life

Potential for improved accuracy with larger datasets

Deep learning can aid in eco-friendly ocean cleanup

Abstract

With the global issue of plastic debris ever expanding, it is about time that the technology industry stepped in. This study aims to assess whether deep learning can successfully distinguish between marine life and man-made debris underwater. The aim is to find if we are safely able to clean up our oceans with Artificial Intelligence without disrupting the delicate balance of the aquatic ecosystems. The research explores the use of Convolutional Neural Networks from the perspective of protecting the ecosystem, rather than primarily collecting rubbish. We did this by building a custom-built, deep learning model, with an original database including 1,644 underwater images and used a binary classification to sort synthesised material from aquatic life. We concluded that although it is possible to safely distinguish between debris and life, further exploration with a larger database and stronger CNN structure has the potential for much more promising results.