utomation of disassembly processes in electronic waste recycling is progressing but hindered by the lack of automated procedures for screw detection and removal. Here we specifically address the detection problem and implement a universal, generalizable, and extendable screw detector which can be deployed in automated disassembly lines. 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 a novel integrated model of two deep convolutional neural networks for screw detection. We show that our method outperforms currently existing methods, while maintaining the high speed of computation. Data set and code of this study are made public.

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http://dx.doi.org/10.1109/sitis.2019.00040 under the license cc-by
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Published on 01/01/2019

Volume 2019, 2019
DOI: 10.1109/sitis.2019.00040
Licence: Other

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