PCB-Vision: A Multiscene RGB-Hyperspectral Benchmark Dataset of Printed Circuit Boards

Addressing the critical theme of recycling electronic waste (E-waste), this contribution is dedicated to developing advanced automated data processing pipelines as a basis for decision-making and process control. Aligning with the broader goals of the circular economy and the United Nations (UN) sus...

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Bibliographic Details
Published in:IEEE sensors journal Vol. 24; no. 10; pp. 17140 - 17158
Main Authors: Arbash, Elias, Fuchs, Margret, Rasti, Behnood, Lorenz, Sandra, Ghamisi, Pedram, Gloaguen, Richard
Format: Journal Article
Language:English
Published: New York IEEE 15.05.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Automated data processing
Benchmarks
Circuit boards
circular economy
Color imagery
Computer vision
Connectors
conveyor belt
Cubes
Data processing
dataset
Datasets
deep learning (DL)
digitalization
Electronic waste
electronic waste (E-waste)
hyperspectral (HS)
Hyperspectral imaging
Inspection
Integrated circuits
machine learning (ML)
open-source data
Optical sensors
PCBVision
printed circuit board (PCB)
Printed circuits
Process controls
Qualitative analysis
Recycling
Remote sensing
RGB
Sensors
Spatial resolution
Spectral resolution
Sustainable development
Waste management
ISSN:1530-437X, 1558-1748
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Summary:Addressing the critical theme of recycling electronic waste (E-waste), this contribution is dedicated to developing advanced automated data processing pipelines as a basis for decision-making and process control. Aligning with the broader goals of the circular economy and the United Nations (UN) sustainable development goals (SDG), our work leverages noninvasive analysis methods utilizing RGB and hyperspectral (HS) imaging data to provide both quantitative and qualitative insights into the E-waste stream composition for optimizing recycling efficiency. In this article, we introduce "PCB-Vision," a pioneering RGB-HS printed circuit board (PCB) benchmark dataset, comprising 53 RGB images of high spatial resolution paired with their corresponding high spectral resolution HS data cubes in the visible and near-infrared (VNIR) range. Grounded in open science principles, our dataset provides a comprehensive resource for researchers through high-quality ground truths, focusing on three primary PCB components: integrated circuits (ICs), capacitors, and connectors. We provide extensive statistical investigations on the proposed dataset together with the performance of several state-of-the-art (SOTA) models, including U-Net, Attention U-Net, Residual U-Net, LinkNet, and DeepLabv3+. By openly sharing this multiscene benchmark dataset along with the baseline codes, we hope to foster transparent, traceable, and comparable developments of advanced data processing across various scientific communities, including, but not limited to, computer vision and remote sensing. Emphasizing our commitment to supporting a collaborative and inclusive scientific community, all materials, including code, data, ground truth, and masks, will be accessible at https://github.com/hifexplo/PCBVision .
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ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2024.3380826