Deep learning-based automated optical inspection system for crimp connections

Nguyen HG, Meiners M, Schmidt L, Franke J (2020)

Publication Type: Conference contribution

Publication year: 2020

Conference Proceedings Title: 2020 10th International Electric Drives Production Conference (EDPC)

ISBN: 9781728184586

DOI: 10.1109/EDPC51184.2020.9388203

Abstract

Within the trend of electrification and autonomous driving, the significance of high-quality crimp connectors is increasing as they establish the electrical connection for the energy and information flow in the automotive system. Whereas the manufacturing of crimp connectors is highly automated, the final quality assessment mainly comprises manual optical inspection tasks that are human labor-intensive and time-consuming. Addressing this gap, a computer vision system to automate the final inspection of crimp connectors is proposed and implemented. In this paper, the image processing chain and the deep learning-based model to reason over image data of crimp connectors with regard to different defect classes are outlined. The effectiveness of this system using a dataset collected in the laboratory environment is demonstrated.

Authors with CRIS profile

Huong Giang Nguyen Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik Moritz Meiners Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik Lorenz Schmidt Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik Jörg Franke Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik

How to cite

APA:

Nguyen, H.G., Meiners, M., Schmidt, L., & Franke, J. (2020). Deep learning-based automated optical inspection system for crimp connections. In 2020 10th International Electric Drives Production Conference (EDPC).

MLA:

Nguyen, Huong Giang, et al. "Deep learning-based automated optical inspection system for crimp connections." Proceedings of the 2020 10th International Electric Drives Production Conference (EDPC) 2020.

BibTeX: Download