We’re delighted to announce that Martin Kampel has won the Most Influential Paper over the Decade Award for his paper “A dataset for computer-vision-based PCB analysis”! The 19th International Conference on Machine Vision Application (MVA) took place in Kyoto last year.

Martin Kampel is a Senior Scientist at the Research Unit Computer Vision at TU Wien Informatics. He obtained his PhD and habilitation from TU Wien. Martin leads applied research at the intersection of machine vision, machine learning, and societal needs—specializing in Ambient Assisted Living, Cultural Heritage, surveillance, and robotics. He actively drives technology transfer through entrepreneurial ventures like CogVis GmbH. A prolific author with over 200 peer-reviewed publications, Martin has advanced topics like WiFi-based human activity recognition, egocentric hand-pose estimation, and animal re-identification using computer vision. He is a respected reviewer, editor, and committee member in leading computing organizations (AAPR/ÖAGM, IAPR, IEEE), and serves as a legally sworn computer vision expert and chartered engineering consultant.

Congratulations to Martin Kampel on this outstanding achievement!

Abstract

We present a public dataset with the aim to facilitate research on computer-vision-based Printed Circuit Board (PCB) analysis, with a focus on recycling-related applications. The dataset contains 748 images of PCBs from a recycling facility, captured under representative conditions using a professional DSLR camera. For all these images, we provide accurate segmentation information for the depicted PCBs as well as bounding box information for all Integrated Circuit (IC) chips (9313 samples). Furthermore, we provide textual information of the labels for a subset of 1740 IC samples. By including these different aspects of information, our dataset is useful for designing and testing a variety of methods for PCB analysis, from PCB classification, over IC chip localization, to the detection of specific chips. We discuss the benefits of PCB analysis for recycling, present dataset statistics, and use the dataset to evaluate two example methods, one for detecting specific PCBs and one for recognizing mainboards.

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