Dataset Open Access

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

Arbash, Elias; Fuchs, Margret; Rasti, Behnood; Lorenz, Sandra; Ghamisi, Pedram; Gloaguen, Richard

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    <subfield code="a">&lt;p&gt;&lt;strong&gt;PCB-Vision Dataset&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Description:&lt;/p&gt;

&lt;p&gt;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.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Data Format&lt;/strong&gt;&lt;/p&gt;

&lt;ul&gt;
	&lt;li&gt;RGB Images: .png files&lt;/li&gt;
	&lt;li&gt;PCB Masks: .jpg files&lt;/li&gt;
	&lt;li&gt;HSI Data: Each hyperspectral data cube is accompanied by a data file and a .hdr file.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt;Folder Organization&lt;/strong&gt;&lt;/p&gt;

&lt;ul&gt;
	&lt;li&gt;PCBVision
	&lt;ul&gt;
		&lt;li&gt;HSI/
		&lt;ul&gt;
			&lt;li&gt;53 subfolders (one for each PCB)&lt;/li&gt;
			&lt;li&gt;&amp;#39;General_masks&amp;#39; folder for &amp;#39;General&amp;#39; segmentation ground truth&lt;/li&gt;
			&lt;li&gt;&amp;#39;Monoseg_masks&amp;#39; folder for &amp;#39;Monoseg&amp;#39; segmentation ground truth&lt;/li&gt;
			&lt;li&gt;&amp;#39;PCB_Masks&amp;#39; folder for masks of the 53 PCBs in the hyperspectral cube&lt;/li&gt;
		&lt;/ul&gt;
		&lt;/li&gt;
		&lt;li&gt;RGB/
		&lt;ul&gt;
			&lt;li&gt;53 .jpg images&lt;/li&gt;
			&lt;li&gt;&amp;#39;General&amp;#39;&amp;nbsp;folder for RGB images &amp;#39;General&amp;#39; segmentation ground truth&lt;/li&gt;
			&lt;li&gt;&amp;#39;Monoseg_masks&amp;#39; folder for RGB images &amp;#39;Monoseg&amp;#39; segmentation ground truth&lt;/li&gt;
		&lt;/ul&gt;
		&lt;/li&gt;
	&lt;/ul&gt;
	&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt;Data Classes in Masks&lt;/strong&gt;&lt;/p&gt;

&lt;ul&gt;
	&lt;li&gt;Masks (both &amp;#39;General&amp;#39; and &amp;#39;Monoseg&amp;#39;) contain 1 to 4 segmentation classes:
	&lt;ul&gt;
		&lt;li&gt;0: &amp;quot;Others&amp;quot;&lt;/li&gt;
		&lt;li&gt;1: &amp;quot;IC&amp;quot;&lt;/li&gt;
		&lt;li&gt;2: &amp;quot;Capacitors&amp;quot;&lt;/li&gt;
		&lt;li&gt;3: &amp;quot;Connectors&amp;quot;&lt;/li&gt;
	&lt;/ul&gt;
	&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt;Code Repository&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;To facilitate reading and working with the data, Python codes are available on the GitHub repository:&lt;/p&gt;

&lt;p&gt;https://github.com/hifexplo/PCBVision&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Citation&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;If you use this dataset, please cite the following article:&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Word&lt;/strong&gt;:&lt;/p&gt;

&lt;p&gt;Arbash, Elias, Fuchs, Margret, Rasti, Behnood, Lorenz, Sandra, Ghamisi, Pedram, &amp;amp; Gloaguen, Richard. (2024). PCB-Vision: A Multiscene RGB-Hyperspectral Benchmark Dataset of Printed Circuit Boards (Version 1) [Data set]. Rodare. &lt;a href="http://doi.org/10.14278/rodare.2704"&gt;http://doi.org/10.14278/rodare.2704&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Latex:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;@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} }&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Contact&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;For further information or inquiries, please visit our website:&lt;/p&gt;

&lt;p&gt;https://www.iexplo.space/&lt;/p&gt;

&lt;p&gt;Contact Email: e.arbash@hzdr.de&lt;/p&gt;</subfield>
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Publication date:
January 29, 2024
DOI:
10.14278/rodare.2704

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Keyword(s):
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
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https://www.hzdr.de/publications/Publ-38684
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