Automated vision system for crankshaft inspection using deep learning approaches

Karim Tout; Mohamed Bouabdellah; Christophe Cudel; Jean-Philippe Urban

doi:10.1117/12.2521751

16 July 2019 Automated vision system for crankshaft inspection using deep learning approaches

Karim Tout, Mohamed Bouabdellah, Christophe Cudel, Jean-Philippe Urban

Author Affiliations +

Proceedings Volume 11172, Fourteenth International Conference on Quality Control by Artificial Vision; 111720N (2019) https://doi.org/10.1117/12.2521751
Event: Fourteenth International Conference on Quality Control by Artificial Vision, 2019, Mulhouse, France

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.

Citation Download Citation

Karim Tout, Mohamed Bouabdellah, Christophe Cudel, and Jean-Philippe Urban "Automated vision system for crankshaft inspection using deep learning approaches", Proc. SPIE 11172, Fourteenth International Conference on Quality Control by Artificial Vision, 111720N (16 July 2019); https://doi.org/10.1117/12.2521751

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