Round Corner
Department of Computer and Information Science


Deep Learning to combat with micro-plastic pollution

An estimated 275 million tonnes of plastic waste was produced on a global scale in 2010, with 8 million of those tonnes being introduced to the oceans - about 3% of global annual plastics waste. Once the plastic reaches the oceans, it is broken down into smaller particles(micro-plastic) by being exposed to ultra violet (UV) radiation and mechanical abrasion from wave actions [1].The quantity of plastic waste floating at the ocean surface in 2013 was estimated to be approximately 269,000 tonnes (small macro- to micro-plastic), this estimate does not include plastic in-depth or at the seafloor). The plastic debris can affect the wildlife in multiple ways, such as entanglement- entrapping, encircling, or constricting,ingestion- accidental ingestion or ingestion of prey containing plastic, and interaction- being in contact with plastic debris [1].It is therefore important to be able to detect and collect the plastic waste in nature,before it reaches the oceans. Once plastic waste has reaches a micro-stadium, it is near impossible to collect it and remove it from the water. An analysis on deep sea locations(range from 1176 to 4843m) showed that there was an average abundance of 1 micro-plastic per 25cm3(particle sizes ranging from 75 to 161μm) [2]

The AI-task will support the observation and monitoring of plastic items​ at consumer side, such as cities, beaches..... We will monitor the geographic distribution of different types of plastic items in these areas. The data will be gathered by citizens in form of taking photos of plastic objects. The photos will be taken using an application on smart phones. The application will have an artificial intelligence (deep learning) algorithm specially tailored for recognition of the material type of the plastic in the photos. The combined information about the geographic distribution, plastic type, and the object type (e.g., bottle, bag) pertinent to plastic debris will be collected in a database which in turn will be statistically analyzed and visualized. The smart application will serve as a tool for empowering citizen science and will contribute to the increase of the awareness and collaboration of citizens towards mitigation of plastic pollution problem.

Co-supervisor: Associate Prof. Pinar Øzturk.


[1] Hannah Ritchie and Max Roser.Plastic pollution.Our World in Data, 2019.
[2] Lisbeth Van Cauwenberghe, Ann Vanreusel, Jan Mees, and Colin R. Janssen.Microplastic pollution in deep-sea sediments.Environmental Pollution, 182:495– 499, 2013.ISSN 0269-7491.doi: URL



Hai Thanh Nguyen Hai Thanh Nguyen
Adjunct Associate Professor
261 IT-bygget
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