Objectives

Before Bower started to look towards AI to enable them in their work, a human operator had to manually go through each image of recycling stations being sent to them by users, awaiting approval. This was a tedious job, consisting of a few hundred images a day. 

The uploaded images had to be checked so that these were in fact images of a legit recycling station, i.e. so that it was not just an image of a photo of a screen portraying one of these stations, or something entirely different, like a close-up shot of a garbage bag. Once confirmed that the image actually portrayed a recycling station, they also had to make sure it was in fact one of the approved recycling stations (there are several).

Solution

The problem facing Bower was a perfect case for image classification. The input is an image and the output is a category, in this case, the type of recycling station. Additionally, a separate model can be used to filter out images that are not recycling stations at all, as a binary image classification task (yes/no).

A convolutional neural network was trained using examples of the various recycling stations (photos) so that eventually it was able to understand the content of the image and predict with high enough accuracy the correct type of recycling station (if any) present in the picture.

Outcome

Thanks to the model, Bower managed to build a solution that cuts down the time we spend on reviewing these stations by 75% and semi-automating the review process, meaning they are still in control in certain cases. 

Additionally, because this is now an area where less manual work is needed, they can instead focus on more important tasks, enabling their expansion across Europe.