Virtual Mines -- Component-level recycling of printed circuit boards using deep learning

TL;DR

This paper presents a deep learning-based pipeline utilizing YOLOv5 for component-level recycling of printed circuit boards, aiming to improve electronic waste processing within the circular economy.

Contribution

It introduces a novel application of YOLOv5 for component detection on PCBs, enhancing e-waste recycling efficiency at the component level.

Findings

YOLOv5 achieved satisfactory precision and recall on PCB component detection.

The pipeline effectively handles different class distributions and optimizes for large components.

The approach supports more efficient and cost-effective e-waste recycling processes.

Abstract

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.