  The quantification of positively buoyant marine plastic debris is critical to
understanding how plastic litter accumulates across the world's oceans and is
also crucial to identifying hotspots for targeted cleanup efforts. Currently,
the most common method to quantify marine plastic is using manta trawls for
manual sampling. However, this method is cost-intensive and requires human
labor. This study removes the need for manual sampling by using an autonomous
method using neural networks and computer vision models, which trained on
images captured from various layers of the ocean column to perform real-time
plastic quantification. The best performing model has a Mean Average Precision
of 85% and an F1-Score of 0.89 while maintaining near real-time processing
speeds ~2 ms/img.
