Dataset Open Access
Arbash, Elias;
Fuchs, Margret;
Rasti, Behnood;
Lorenz, Sandra;
Ghamisi, Pedram;
Gloaguen, Richard
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"title": "PCB-Vision: A Multiscene RGB-Hyperspectral Benchmark Dataset of Printed Circuit Boards",
"creators": [
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"affiliation": "Helmholtz Institute Freiberg for Resource Technology",
"name": "Arbash, Elias"
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"affiliation": "Helmholtz Institute Freiberg for Resource Technology",
"orcid": "0000-0001-7210-1132",
"name": "Fuchs, Margret"
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{
"name": "Rasti, Behnood"
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"affiliation": "Helmholtz Institute Freiberg for Resource Technology",
"orcid": "0000-0001-8464-2331",
"name": "Lorenz, Sandra"
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"affiliation": "Helmholtz Institute Freiberg for Resource Technology",
"orcid": "0000-0003-1203-741X",
"name": "Ghamisi, Pedram"
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{
"affiliation": "Helmholtz Institute Freiberg for Resource Technology",
"orcid": "0000-0002-4383-473X",
"name": "Gloaguen, Richard"
}
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"version": "1",
"publication_date": "2024-01-29",
"pub_id": "38684",
"keywords": [
"circular economy",
"automated data processing",
"optical sensors",
"recycling",
"e-waste",
"printed circuit board",
"hyperspectral",
"dataset",
"RGB",
"conveyor belt",
"sensors",
"machine learning",
"deep learning",
"PCBVision",
"open-source data",
"digitalization"
],
"description": "<p><strong>PCB-Vision Dataset</strong></p>\n\n<p>Description:</p>\n\n<p>The PCB-Vision dataset is a multiscene RGB-Hyperspectral benchmark dataset comprising 53 Printed Circuit Boards (PCBs). The RGB images are collected using a Teledyne Dalsa C4020 camera on a conveyor belt, while hyperspectral images (HSI) are acquired with a Specim FX10 spectrometer. The HSI data contains 224 bands in the VNIR range [400 - 1000]nm.</p>\n\n<p><strong>Data Format</strong></p>\n\n<ul>\n\t<li>RGB Images: .png files</li>\n\t<li>PCB Masks: .jpg files</li>\n\t<li>HSI Data: Each hyperspectral data cube is accompanied by a data file and a .hdr file.</li>\n</ul>\n\n<p><strong>Folder Organization</strong></p>\n\n<ul>\n\t<li>PCBVision\n\t<ul>\n\t\t<li>HSI/\n\t\t<ul>\n\t\t\t<li>53 subfolders (one for each PCB)</li>\n\t\t\t<li>'General_masks' folder for 'General' segmentation ground truth</li>\n\t\t\t<li>'Monoseg_masks' folder for 'Monoseg' segmentation ground truth</li>\n\t\t\t<li>'PCB_Masks' folder for masks of the 53 PCBs in the hyperspectral cube</li>\n\t\t</ul>\n\t\t</li>\n\t\t<li>RGB/\n\t\t<ul>\n\t\t\t<li>53 .jpg images</li>\n\t\t\t<li>'General' folder for RGB images 'General' segmentation ground truth</li>\n\t\t\t<li>'Monoseg_masks' folder for RGB images 'Monoseg' segmentation ground truth</li>\n\t\t</ul>\n\t\t</li>\n\t</ul>\n\t</li>\n</ul>\n\n<p><strong>Data Classes in Masks</strong></p>\n\n<ul>\n\t<li>Masks (both 'General' and 'Monoseg') contain 1 to 4 segmentation classes:\n\t<ul>\n\t\t<li>0: "Others"</li>\n\t\t<li>1: "IC"</li>\n\t\t<li>2: "Capacitors"</li>\n\t\t<li>3: "Connectors"</li>\n\t</ul>\n\t</li>\n</ul>\n\n<p><strong>Code Repository</strong></p>\n\n<p>To facilitate reading and working with the data, Python codes are available on the GitHub repository:</p>\n\n<p>https://github.com/hifexplo/PCBVision</p>\n\n<p><strong>Citation</strong></p>\n\n<p>If you use this dataset, please cite the following article:</p>\n\n<p><strong>Word</strong>:</p>\n\n<p>Arbash, Elias, Fuchs, Margret, Rasti, Behnood, Lorenz, Sandra, Ghamisi, Pedram, & Gloaguen, Richard. (2024). PCB-Vision: A Multiscene RGB-Hyperspectral Benchmark Dataset of Printed Circuit Boards (Version 1) [Data set]. Rodare. <a href=\"http://doi.org/10.14278/rodare.2704\">http://doi.org/10.14278/rodare.2704</a></p>\n\n<p><strong>Latex:</strong></p>\n\n<p>@article{arbash2024pcb, title={PCB-Vision: A Multiscene RGB-Hyperspectral Benchmark Dataset of Printed Circuit Boards}, author={Arbash, Elias and Fuchs, Margret and Rasti, Behnood and Lorenz, Sandra and Ghamisi, Pedram and Gloaguen, Richard}, journal={arXiv preprint arXiv:2401.06528}, year={2024} }</p>\n\n<p><strong>Contact</strong></p>\n\n<p>For further information or inquiries, please visit our website:</p>\n\n<p>https://www.iexplo.space/</p>\n\n<p>Contact Email: e.arbash@hzdr.de</p>",
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| All versions | This version | |
|---|---|---|
| Views | 1,324 | 1,324 |
| Downloads | 5,351 | 5,351 |
| Data volume | 61.0 TB | 61.0 TB |
| Unique views | 1,161 | 1,161 |
| Unique downloads | 1,196 | 1,196 |