{"conceptdoi":"10.14278/rodare.2703","conceptrecid":"2703","created":"2024-01-29T15:42:20.914128+00:00","doi":"10.14278/rodare.2704","files":[{"bucket":"671920b9-68a5-4a82-98d1-c696f25e497c","checksum":"md5:caeee8bafb7857e9bef3479d5ab3f9b4","key":"PCBDataset.zip","links":{"self":"https://rodare.hzdr.de/api/files/671920b9-68a5-4a82-98d1-c696f25e497c/PCBDataset.zip"},"size":11392398155,"type":"zip"}],"id":2704,"links":{"badge":"https://rodare.hzdr.de/badge/doi/10.14278/rodare.2704.svg","bucket":"https://rodare.hzdr.de/api/files/671920b9-68a5-4a82-98d1-c696f25e497c","conceptbadge":"https://rodare.hzdr.de/badge/doi/10.14278/rodare.2703.svg","conceptdoi":"https://doi.org/10.14278/rodare.2703","doi":"https://doi.org/10.14278/rodare.2704","html":"https://rodare.hzdr.de/record/2704","latest":"https://rodare.hzdr.de/api/records/2704","latest_html":"https://rodare.hzdr.de/record/2704","self":"https://rodare.hzdr.de/api/records/2704"},"metadata":{"access_right":"open","access_right_category":"success","communities":[{"id":"hzdr"},{"id":"rodare"}],"creators":[{"affiliation":"Helmholtz Institute Freiberg for Resource Technology","name":"Arbash, Elias"},{"affiliation":"Helmholtz Institute Freiberg for Resource Technology","name":"Fuchs, Margret","orcid":"0000-0001-7210-1132"},{"name":"Rasti, Behnood"},{"affiliation":"Helmholtz Institute Freiberg for Resource Technology","name":"Lorenz, Sandra","orcid":"0000-0001-8464-2331"},{"affiliation":"Helmholtz Institute Freiberg for Resource Technology","name":"Ghamisi, Pedram","orcid":"0000-0003-1203-741X"},{"affiliation":"Helmholtz Institute Freiberg for Resource Technology","name":"Gloaguen, Richard","orcid":"0000-0002-4383-473X"}],"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>&#39;General_masks&#39; folder for &#39;General&#39; segmentation ground truth</li>\n\t\t\t<li>&#39;Monoseg_masks&#39; folder for &#39;Monoseg&#39; segmentation ground truth</li>\n\t\t\t<li>&#39;PCB_Masks&#39; 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>&#39;General&#39;&nbsp;folder for RGB images &#39;General&#39; segmentation ground truth</li>\n\t\t\t<li>&#39;Monoseg_masks&#39; folder for RGB images &#39;Monoseg&#39; 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 &#39;General&#39; and &#39;Monoseg&#39;) contain 1 to 4 segmentation classes:\n\t<ul>\n\t\t<li>0: &quot;Others&quot;</li>\n\t\t<li>1: &quot;IC&quot;</li>\n\t\t<li>2: &quot;Capacitors&quot;</li>\n\t\t<li>3: &quot;Connectors&quot;</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, &amp; 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>","doc_id":"1","doi":"10.14278/rodare.2704","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"],"license":{"id":"CC-BY-4.0"},"pub_id":"38684","publication_date":"2024-01-29","related_identifiers":[{"identifier":"10.48550/arXiv.2401.06528","relation":"isSupplementTo","scheme":"doi"},{"identifier":"https://www.hzdr.de/publications/Publ-38529","relation":"isPartOf","scheme":"url"},{"identifier":"https://www.hzdr.de/publications/Publ-38684","relation":"isIdenticalTo","scheme":"url"},{"identifier":"https://www.hzdr.de/publications/Publ-38529","relation":"isReferencedBy","scheme":"url"},{"identifier":"10.14278/rodare.2703","relation":"isVersionOf","scheme":"doi"}],"relations":{"version":[{"count":1,"index":0,"is_last":true,"last_child":{"pid_type":"recid","pid_value":"2704"},"parent":{"pid_type":"recid","pid_value":"2703"}}]},"resource_type":{"title":"Dataset","type":"dataset"},"title":"PCB-Vision: A Multiscene RGB-Hyperspectral Benchmark Dataset of Printed Circuit Boards","version":"1"},"owners":[840],"revision":11,"stats":{"downloads":8189.0,"unique_downloads":1766.0,"unique_views":2597.0,"version_downloads":8189.0,"version_unique_downloads":1766.0,"version_unique_views":2597.0,"version_views":2860.0,"version_volume":93292348491295.0,"views":2860.0,"volume":93292348491295.0},"updated":"2024-05-15T08:30:41.173019+00:00"}