Automated vision system for crankshaft inspection using deep learning approaches

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.