Boosting DEME’s ocean cleanup by identifying waste in the river Scheldt


AI boosts DEME’s river cleanup

Robovision AI identifies waste in the river Scheldt after which an autonomous ship collects it. The cleanup is fully automated and growing increasingly intelligent, a big plus for the environment and a huge win in efficiency for DEME, the dredging company who is responsible.

The problem

We don’t have to convince you that polluted oceans and rivers are a problem. For the animals living in them, for the surrounding ecosystems and basically for the whole planet. Dredging company DEME puts its expertise to work in turning the negative impact of polluted waters around. We’ve tried to help them do just that—but faster.

The solution

The river is split into two parts. In the shallow part, a net ensures passive waste-collection. In the deep, where boats and canoes or suppers float by, we work with active waste collection. Two cameras attached to a bridge over the river have this deep part of the river in sight. Thanks to AI, they can identify whether an object is waste or not. If it is, an autonomous ship collects the object and neatly deposits it in the passive collection net.

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The processing power of the model is so high that the response of the automated ship is almost instantaneous.


Our model can be used in a variety of natural situations like lakes and rivers, without losing any of its speed.


Collecting waste and cleaning up our water is much faster if humans aren’t needed in the process.


With every object it identifies, the model becomes more precise, regardless trying weather conditions. Always learning and improving.


Every time new data is labeled by the model it can be added to the ever growing dataset, that in return improves the model.

Our approach

We faced quite some challenges in this case. There was no off the shelf algorithm for this specific question but there was the high amount of variability. The weather has a big impact on the surface of the water, as well as on the objects floating in it. A detector existed, obviously, but the dataset was missing. But if we didn’t love a challenge now and then we’d be in the wrong business, pioneering in the application of AI! We started off with an unsupervised approach, identifying everything that isn’t waste, and continued to train the model in a supervised manner. Meanwhile, the intelligence on what is and isn’t waste is growing increasingly sturdy.

 Looking for a solution to identify whether an object is waste or not?

“We really enjoyed the collaboration with Robovision, as they proactively came up with solutions, which is for us a key factor in a good partnership.”

Magali Bruggeman, Business Development Manager DEME