Computerized image analysis in manufacturing industry anomaly detection using artificial intelligence techniques-Reference-Cited by-同舟云学术

Computerized image analysis in manufacturing industry anomaly detection using artificial intelligence techniques

Published:2024-02-08 Issue: Volume: Page:
ISSN:0268-3768
Container-title:The International Journal of Advanced Manufacturing Technology
language:en
Short-container-title:Int J Adv Manuf Technol

Author:

Chen Chen,Zhang Ning,Nie Zhe,Yuan Kan,Liang Xiaoyue

Funder

Characteristic innovation project of ordinary universities in Guangdong Province

Supporting project of Shenzhen Polytechnic

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s00170-024-13124-7.pdf

Reference20 articles.

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2. Le KH, Tran TV, Pham HH, Nguyen HT, Le TT, Nguyen HQ (2023) Learning from multiple expert annotators for enhancing anomaly detection in medical image analysis. IEEE Access 11:14105–14114

3. Lu B, Xu D, Huang B (2022) Deep-learning-based anomaly detection for lace defect inspection employing videos in production line. Adv Eng Inform 51:101471

4. Zeiser A, Özcan B, Kracke C, van Stein B, Bäck T (2023) A data-centric approach to anomaly detection in layer-based additive manufacturing. at-Automatisierungstechnik 71(1):81–89

5. Viswanathan, V., Murali, S., & Veeraraghavan, V. (2023, May). Anomaly detection and classification in agricultural produce using image processing and CNN assisted by a robotic arm. In International Conference “New Technologies, Development and Applications” (pp. 557-561). Cham: Springer Nature Switzerland.

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