A new algorithm designed with deep learning techniques will enable the detection and quantification of floating plastics in the sea with a reliability over 80 percent, according to a new study.

MARLIT, an open access web app based on an algorithm designed with deep learning techniques, will enable the detection and quantification of floating plastics in the sea with a reliability over 80%, according to a study published in the journal Environmental Pollution and carried out by experts of the Faculty of Biology and the Biodiversity Research Institute of the University of Barcelona (IRBio).

This methodology results from the analysis through artificial intelligence techniques of more than 3,800 aerial images of the Mediterranean coast in Catalonia, and it will allow researchers to make progress in the assessment of the presence, density and distribution of the plastic pollutants in the seas and oceans worldwide. Among the participants in the study, published in the journal Environmental Pollution, are the experts of the Consolidated Research Group on Large Marine Vertebrates of the UB and IRBio, and the Research Group on Biostatistics and Bioinformatics (GRBIO) of the UB, integrated in the Bioinformatics Barcelona platform (BIB).

Litter that floats and pollutes the ocean

Historically, direct observations (boats, planes, etc.) are the base for the common methodology to assess the impact of floating marine macro-litter (FMML). However, the great ocean area and the volume of data make it hard for the researchers to advance with the monitoring studies.

«Automatic aerial photography techniques combined with analytical algorithms are more efficient protocols for the control and study of this kind of pollutants,» notes Odei Garcia-Garin, first author of the article and member of the CRG on Large Marine Mammals, led by Professor Alex Aguilar.

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