Scheck A, Hofmann B, Nguyen HG, Franke J (2024)
Publication Type: Conference contribution
Publication year: 2024
Publisher: Institute of Electrical and Electronics Engineers Inc.
Pages Range: 235-237
Conference Proceedings Title: Proceedings of the 2024 10th International Conference on Applied System Innovation, ICASI 2024
Event location: Kyoto, JPN
ISBN: 9798350394924
DOI: 10.1109/ICASI60819.2024.10547861
In the era of digitalization and big data, the evolution of electrical systems and their manufacturing is closely tied to the advancement of machine learning, particularly in the industrial sector. With the rise of electromobility and constant connectivity, there is a growing demand for efficient signal and power networking, where the crimp connection plays a critical role. Against this background, this study introduces an intelligent computer vision pipeline that leverages multiperspective deep learning models for automated and comprehensive quality inspection of crimp connections.
APA:
Scheck, A., Hofmann, B., Nguyen, H.G., & Franke, J. (2024). A multi-perspective deep learning approach for optical inspection of crimp connections. In Shoou-Jinn Chang, Sheng-Joue Young, Artde Donald Kin-Tak Lam, Liang-Wen Ji, Stephen D. Prior (Eds.), Proceedings of the 2024 10th International Conference on Applied System Innovation, ICASI 2024 (pp. 235-237). Kyoto, JPN: Institute of Electrical and Electronics Engineers Inc..
MLA:
Scheck, Albert, et al. "A multi-perspective deep learning approach for optical inspection of crimp connections." Proceedings of the 10th International Conference on Applied System Innovation, ICASI 2024, Kyoto, JPN Ed. Shoou-Jinn Chang, Sheng-Joue Young, Artde Donald Kin-Tak Lam, Liang-Wen Ji, Stephen D. Prior, Institute of Electrical and Electronics Engineers Inc., 2024. 235-237.
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