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

<strong>PCB-Vision Dataset</strong>

Description:

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.

<strong>Data Format</strong>

RGB Images: .png files
PCB Masks: .jpg files
HSI Data: Each hyperspectral data cube is accompanied by a data file and a .hdr file.

<strong>Folder Organization</strong>

PCBVision

HSI/

53 subfolders (one for each PCB)
'General_masks' folder for 'General' segmentation ground truth
'Monoseg_masks' folder for 'Monoseg' segmentation ground truth
'PCB_Masks' folder for masks of the 53 PCBs in the hyperspectral cube

RGB/

53 .jpg images
'General' folder for RGB images 'General' segmentation ground truth
'Monoseg_masks' folder for RGB images 'Monoseg' segmentation ground truth

<strong>Data Classes in Masks</strong>

Masks (both 'General' and 'Monoseg') contain 1 to 4 segmentation classes:

0: "Others"
1: "IC"
2: "Capacitors"
3: "Connectors"

<strong>Code Repository</strong>

To facilitate reading and working with the data, Python codes are available on the GitHub repository:

https://github.com/hifexplo/PCBVision

<strong>Citation</strong>

If you use this dataset, please cite the following article:

<strong>Word</strong>:

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. http://doi.org/10.14278/rodare.2704

<strong>Latex:</strong>

@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} }

<strong>Contact</strong>

For further information or inquiries, please visit our website:

https://www.iexplo.space/

Contact Email: [e-mail]