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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Vydané v:	IEEE sensors journal Ročník 24; číslo 10; s. 17140 - 17158
Hlavní autori:	Arbash, Elias, Fuchs, Margret, Rasti, Behnood, Lorenz, Sandra, Ghamisi, Pedram, Gloaguen, Richard
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 15.05.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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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Abstract	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 .
AbstractList	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 .
Author	Lorenz, Sandra Gloaguen, Richard Ghamisi, Pedram Fuchs, Margret Rasti, Behnood Arbash, Elias
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SubjectTerms	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
Title	PCB-Vision: A Multiscene RGB-Hyperspectral Benchmark Dataset of Printed Circuit Boards
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