DCNN-based Screw Classification in Automated Disassembly Processes

Erenus Yildiz, Florentin Wörgötter

2020

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.

Paper Citation

in Harvard Style

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 - Volume 1: ROBOVIS, ISBN 978-989-758-479-4, pages 61-68. DOI: 10.5220/0009979900610068

in Bibtex Style

@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 - Volume 1: ROBOVIS,},
year={2020},
pages={61-68},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009979900610068},
isbn={978-989-758-479-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the International Conference on Robotics, Computer Vision and Intelligent Systems - Volume 1: 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