PCB Recognition using Local Features for Recycling Purposes

Christopher Pramerdorfer, Martin Kampel

Abstract

We present a method for detecting and classifying Printed Circuit Boards (PCBs) in waste streams for recycling purposes. Our method employs local feature matching and geometric verification to achieve a high open-set recognition performance under practical conditions. In order to assess the suitability of different local features in this context, we perform a comprehensive evaluation of established (SIFT, SURF) and recent (ORB, BRISK, FREAK, AKAZE) keypoint detectors and descriptors in terms of established performance measures. The results show that SIFT and SURF are outperformed by recent alternatives, and that most descriptors benefit from color information in the form of opponent color space. The presented method achieves a recognition rate of up to 100% and is robust with respect to PCB damage, as verified using a comprehensive public dataset.

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Paper Citation


in Harvard Style

Pramerdorfer C. and Kampel M. (2015). PCB Recognition using Local Features for Recycling Purposes . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-091-8, pages 71-78. DOI: 10.5220/0005289200710078


in Bibtex Style

@conference{visapp15,
author={Christopher Pramerdorfer and Martin Kampel},
title={PCB Recognition using Local Features for Recycling Purposes},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={71-78},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005289200710078},
isbn={978-989-758-091-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2015)
TI - PCB Recognition using Local Features for Recycling Purposes
SN - 978-989-758-091-8
AU - Pramerdorfer C.
AU - Kampel M.
PY - 2015
SP - 71
EP - 78
DO - 10.5220/0005289200710078