Keeping the streets clean in a dynamic urban environment is one of the major challenges facing the Amsterdam City Council. Amsterdam Business School researcher Maarten Sukel developed a system that can recognize waste bags and other unwanted objects lying around in the street. This system can spot the object in real-time and is based on machine learning.
The system, which is currently being tested, offers a cheap and generic solution that can easily be used in other cities as well. Maarten Sukel: ‘We use smartphones linked to vehicles with an application that generates pictures. These are sent on to our server for object recognition. We use the real-time object recognition system YOLO (You Only Look Once), which can process images very fast. Then we look if the results agree with reports from the neighborhood itself.”
The system solves a huge problem. The Amsterdam City Council has as many as 15.000 underground containers. ‘You can’t continuously drive past to see if there’s something lying next to them. For some things, like bulk waste, you need a special vehicle. By doing it properly from the outset, we not only keep the city clean but we also reduce the mileage.’ Privacy, however, is an important point. ‘We drive around with cameras and every citizen of Amsterdam should be able to move through the city without being followed. We only save images if they’re necessary for further research. Faces and distinguishing marks are removed.’
The system can be applied beyond the recognition and removal of waste. It can also be used to recognize dangerous situations such as a knocked-down bollard or a crooked paving stone. The UvA wants to focus on useful applications for urban areas and we certainly wanted to incorporate that into this study. The researchers initially chose the waste disposal problem as their focus. From a technical perspective, this could be realized fastest. To learn, AI requires huge amounts of data and there were many images of rubbish containers and waste available.
Source: UVA/University of Amsterdam