DCNN-based Screw Classification in Automated Disassembly Processes

Erenus Yildiz, Florentin Wörgötter


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.


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