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

arXiv: https://arxiv.org/abs/2406.17162 · HTML: https://mlsanity.com/paper/2406.17162/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 27 references that are in the map.
- First posted on arXiv 24 Jun 2024.

## Builds on (most cited first, 2 of 2)

- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [BIRNet: Brain Image Registration Using Dual-Supervised Fully Convolutional Networks](https://mlsanity.com/paper/1802.04692/) (2018)

## Built on by (most cited first, 1 of 1)

- [Real-Time Detection of Electronic Components in Waste Printed Circuit Boards: A Transformer-Based Approach](https://mlsanity.com/paper/2409.16496/) (2024)

## Abstract (arXiv)

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.