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

A Robotic Approach towards Quantifying Epipelagic Bound Plastic Using Deep Visual Models