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 DEME GROUP
Boosting DEME's ocean cleanup
The DEME Group partnered with Robovision to develop an AI-based automated system that can identify waste in the river Scheldt.
AI boosts DEME’s river cleanup
Figure 1: the autonomous ship that collects waste based on the insight provided by AI
The challenge
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.
Figure 2: Magali Bruggeman, Business Development Manager at DEME Group, talked about the challenge faced by river and ocean pollution
The solution
“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 Group
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.
Figure 3: The DEME Group is a world leader in providing dredging solutions
The 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.
Figure 4: Laurens Wauters, Project Engineer at Robovision, explains how Robovision approaches the challenge using vision AI
Reaching Desirable Outcomes
SPEED
The processing power of the model is so high that the response of the automated ship is almost instantaneous.
SCALABILITY
Our model can be used in a variety of natural situations like lakes and rivers, without losing any of its speed.
SUSTAINABILITY
Collecting waste and cleaning up our water is much faster if humans aren’t needed in the process.
ACCURACY
With every object it identifies, the model becomes more precise, regardless trying weather conditions. Always learning and improving.
RE-TRAINABILITY
Every time new data is labeled by the model it can be added to the ever growing dataset, that in return improves the model.