DCNN-based Screw Classification in Automated Disassembly Processes

Erenus Yildiz; Florentin Wörgötter

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Paper

DCNN-based Screw Classification in Automated Disassembly Processes

Topics: Robot Vision; Scene Analysis and Understanding

In Proceedings of the International Conference on Robotics, Computer Vision and Intelligent Systems ROBOVIS - Volume 1, 61-68, 2020

Authors: Erenus Yildiz and Florentin Wörgötter

Affiliation: III. Physics Institute, Georg-August University of Göttingen, Germany

Keyword(s): Screw Classification, Automation, Disassembly, Recycling, E-Waste.

Abstract: E-waste recycling is thriving yet there are many challenges waiting to be addressed until high-degree, device-independent automation is possible. One of these challenges is to have automated procedures for screw classification. Here we specifically address the problem of classification of the screw heads and implement a universal, generalizable, and extendable screw classifier which can be deployed in automated disassembly routines. We selected the best performing state-of-the-art classifiers and compared their performance to that of our architecture, which combines a Hough transform with the top-performing state-of-the-art deep convolutional neural network proven by our experiments. We show that our classifier outperforms currently existing methods by achieving 97% accuracy while maintaining a high speed of computation. Data set and code of this study are made public.

CC BY-NC-ND 4.0

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Paper citation in several formats:

Yildiz, E. and Wörgötter, F. (2020). DCNN-based Screw Classification in Automated Disassembly Processes. In Proceedings of the International Conference on Robotics, Computer Vision and Intelligent Systems - ROBOVIS; ISBN 978-989-758-479-4, SciTePress, pages 61-68. DOI: 10.5220/0009979900610068

@conference{robovis20,
author={Erenus Yildiz. and Florentin Wörgötter.},
title={DCNN-based Screw Classification in Automated Disassembly Processes},
booktitle={Proceedings of the International Conference on Robotics, Computer Vision and Intelligent Systems - ROBOVIS},
year={2020},
pages={61-68},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009979900610068},
isbn={978-989-758-479-4},
}

TY - CONF

JO - Proceedings of the International Conference on Robotics, Computer Vision and Intelligent Systems - ROBOVIS
TI - DCNN-based Screw Classification in Automated Disassembly Processes
SN - 978-989-758-479-4
AU - Yildiz, E.
AU - Wörgötter, F.
PY - 2020
SP - 61
EP - 68
DO - 10.5220/0009979900610068
PB - SciTePress