PCB-Vision: A Multiscene RGB-Hyperspectral Benchmark Dataset of Printed
  Circuit Boards
Elias Arbash Margret Fuchs Behnood Rasti Sandra Lorenz Pedram Ghamisi Richard Gloaguen
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 non-invasive analysis methods
utilizing RGB and hyperspectral imaging data to provide both quantitative and
qualitative insights into the E-waste stream composition for optimizing
recycling efficiency. In this paper, we introduce 'PCB-Vision'; a pioneering
RGB-hyperspectral printed circuit board (PCB) benchmark dataset, comprising 53
RGB images of high spatial resolution paired with their corresponding high
spectral resolution hyperspectral 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 (IC), 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 multi-scene 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.