  This contribution gives an overview of an ongoing project using machine
learning and computer vision components for improving the electronic waste
recycling process. In circular economy, the "virtual mines" concept refers to
production cycles where interesting raw materials are reclaimed in an efficient
and cost-effective manner from end-of-life items. In particular, the growth of
e-waste, due to the increasingly shorter life cycle of hi-tech goods, is a
global problem. In this paper, we describe a pipeline based on deep learning
model to recycle printed circuit boards at the component level. A pre-trained
YOLOv5 model is used to analyze the results of the locally developed dataset.
With a different distribution of class instances, YOLOv5 managed to achieve
satisfactory precision and recall, with the ability to optimize with large
component instances.
