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Proceedings Paper

Automated vision system for crankshaft inspection using deep learning approaches
Author(s): Karim Tout; Mohamed Bouabdellah; Christophe Cudel; Jean-Philippe Urban
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Paper Abstract

This paper proposes a fully automated vision system to inspect the whole surface of crankshafts, based on the magnetic particle testing technique. Multiple cameras are needed to ensure the inspection of the whole surface of the crankshaft in real-time. Due to the very textured surface of crankshafts and the variability in defect shapes and types, defect detection methods based on deep learning algorithms, more precisely convolutional neural networks (CNNs), become a more efficient solution than traditional methods. This paper reviews the various approaches of defect detection with CNNs, and presents the advantages and weaknesses of each approach for real-time defect detection on crankshafts. It is important to note that the proposed visual inspection system only replaces the manual inspection of crankshafts conducted by operators at the end of the magnetic particle testing procedure.

Paper Details

Date Published: 16 July 2019
PDF: 6 pages
Proc. SPIE 11172, Fourteenth International Conference on Quality Control by Artificial Vision, 111720N (16 July 2019); doi: 10.1117/12.2521751
Show Author Affiliations
Karim Tout, Univ. de Haute-Alsace (France)
Mohamed Bouabdellah, NT2I (France)
Christophe Cudel, Univ. de Haute Alsace (France)
Jean-Philippe Urban, Univ. de Haute-Alsace (France)

Published in SPIE Proceedings Vol. 11172:
Fourteenth International Conference on Quality Control by Artificial Vision
Christophe Cudel; Stéphane Bazeille; Nicolas Verrier, Editor(s)

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