Abstract
Recently, the generation of electronic waste (E-waste) has increased significantly due to rapid changes in
consumer demand and advancements in technology. Recycling
E-waste is essential for boosting the economy and advancing
the sustainability of the electronics industry. Printed circuit
boards (PCBs) contribute significantly to E-waste, as they are
widely used in various electronic devices. However, a challenge in
recycling E-waste is the rapidly and diversely changing material
composition. To enhance the efficacy of E-waste recycling, an
automated, non-invasive method is essential for process control and decision-making. By exploiting hyperspectral imaging
(HSI), which offers spectroscopic analysis to accurately identify
materials, this paper presents attention-based deep learning
segmentation models to accurately identify components in PCBs.
This approach allows for the automatic extraction of information
from E-waste, leading to more efficient and optimized recycling
practices.
consumer demand and advancements in technology. Recycling
E-waste is essential for boosting the economy and advancing
the sustainability of the electronics industry. Printed circuit
boards (PCBs) contribute significantly to E-waste, as they are
widely used in various electronic devices. However, a challenge in
recycling E-waste is the rapidly and diversely changing material
composition. To enhance the efficacy of E-waste recycling, an
automated, non-invasive method is essential for process control and decision-making. By exploiting hyperspectral imaging
(HSI), which offers spectroscopic analysis to accurately identify
materials, this paper presents attention-based deep learning
segmentation models to accurately identify components in PCBs.
This approach allows for the automatic extraction of information
from E-waste, leading to more efficient and optimized recycling
practices.
| Original language | English |
|---|---|
| Title of host publication | 2025 IEEE Symposia on Computational Intelligence for Energy, Transport and Environmental Sustainability, CIETES 2025 |
| Subtitle of host publication | 2025 IEEE Symposium on Computational Intelligence for Energy, Transport and Environmental Sustainability |
| Publisher | IEEE |
| Pages | 1-7 |
| Number of pages | 7 |
| ISBN (Electronic) | 979-8-3315-0825-8 |
| ISBN (Print) | 979-8-3315-0825-8, 979-8-3315-0826-5 |
| DOIs | |
| Publication status | Published (in print/issue) - 20 Mar 2025 |
Publication series
| Name | 2025 IEEE Symposia on Computational Intelligence for Energy, Transport and Environmental Sustainability, CIETES 2025 |
|---|
Bibliographical note
Publisher Copyright:© 2025 IEEE.
Data Access Statement
PCB-Vision dataset: https://rodare.hzdr.de/record/2704
Keywords
- Electronic waste (E-waste)
- Hyperspectral Imaging
- Deep learning (DL)
- Automatic visual inspection
- Printed circuit board (PCB)
- Recycling
- Hyperspectral imaging
