Computer vision segmentation model—deep learning for categorizing microplastic debris

Abstract

The characterization of beached and marine microplastic debris is critical to understanding how plastic litter accumulates across the world’s oceans and identifying hotspots that should be targeted for early cleanup efforts. Currently, the most common monitoring method to quantify microplastics at sea requires physical sampling using surface trawling and sifting for beached microplastics, which are then followed by manual counting and laboratory analysis. The need for manual counting is time-consuming, operator-dependent, and incurs high costs, thereby preventing scalable deployment of consistent marine plastic monitoring worldwide. Here, we describe a workflow combining a simple experimental setup with advanced image processing techniques to conduct both quantitative and qualitative assessments of microplastic (0.05 cm