Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.17162 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916757985296384 |
|---|---|
| author | Mohsin, Muhammad Rovetta, Stefano Masulli, Francesco Cabri, Alberto |
| author_facet | Mohsin, Muhammad Rovetta, Stefano Masulli, Francesco Cabri, Alberto |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_17162 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Virtual Mines -- Component-level recycling of printed circuit boards using deep learning Mohsin, Muhammad Rovetta, Stefano Masulli, Francesco Cabri, Alberto Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning 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. |
| title | Virtual Mines -- Component-level recycling of printed circuit boards using deep learning |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2406.17162 |