Machine Learning to monitor and quantity marine litter

This research project aims to combine forensic techniques of fibre analysis, preventing contamination, evidence tracking and evaluation with machine learning and computer vision to create a fully automated method for characterising, quantifying and characterising macro and microplastic pollution. The project is split into two main parts:

1) Macro-mal: using machine learning to monitor and quantify macro-litter in costal environments so that a predictive model can be produced to predict the quantify and movements of litter in the environment.

2) Micro-mal: using machine learning to standardise and automate microfibre pollution monitoring. Machine learning will allow the automation of the identification and characterisation of

these microfibres by comparing to a reference database. Providing insights into cross-comparison of location, time, polymer type and morphology.

Through completing this research project, we aim to produce models so that a greater understanding of the sources, movement, and impact of marine litter, as well as potentially proposing strategies to combat marine pollution

Staffordshire University Plastic pollution research: http://blogs.staffs.ac.uk/plastic-pollution-research/ Microplastics: Dr Claire Gwinnett- c.gwinnett@staffs.ac.uk Machine learning & AVA tech: Dr Mohamed Sedky- m.h.sedky@staffs.ac.uk