Summary of Virtual Mines — Component-level Recycling Of Printed Circuit Boards Using Deep Learning, by Muhammad Mohsin et al.
Virtual Mines – Component-level recycling of printed circuit boards using deep learning
by Muhammad Mohsin, Stefano Rovetta, Francesco Masulli, Alberto Cabri
First submitted to arxiv on: 24 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The ongoing project aims to improve electronic waste recycling using machine learning and computer vision components. The paper describes a pipeline based on deep learning models to recycle printed circuit boards at the component level. A pre-trained YOLOv5 model is used to analyze the results of a locally developed dataset, achieving satisfactory precision and recall with large component instances. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This project focuses on improving electronic waste recycling by using machine learning and computer vision. Electronic waste is growing because of shortening life cycles of high-tech goods. The paper shows how a deep learning model can help recycle printed circuit boards at the component level. It uses a YOLOv5 model to analyze results from its own dataset. |
Keywords
» Artificial intelligence » Deep learning » Machine learning » Precision » Recall
