Visual defect obfuscation based self-supervised anomaly detection-Reference-Cited by-同舟云学术

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Visual defect obfuscation based self-supervised anomaly detection

Published:2024-08-14 Issue:1 Volume:14 Page:
ISSN:2045-2322
Container-title:Scientific Reports
language:en
Short-container-title:Sci Rep

Author:

Park YeongHyeon,Kang Sungho,Kim Myung Jin,Lee Yeonho,Kim Hyeong Seok,Yi Juneho

Funder

This work was supported by the BK21 FOUR Project.

This work was supported by SK Planet Co., Ltd.

Publisher

Springer Science and Business Media LLC

Link

https://www.nature.com/articles/s41598-024-69698-5.pdf

Reference54 articles.

1. Weimer, D., Scholz-Reiter, B. & Shpitalni, M. Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection. CIRP Ann. 65, 417–420 (2016).

2. Agnisarman, S., Lopes, S., Chalil Madathil, K., Piratla, K. & Gramopadhye, A. A survey of automation-enabled human-in-the-loop systems for infrastructure visual inspection. Autom. Constr. 97, 52–76 (2019).

3. Park, Y. et al. Neural network training strategy to enhance anomaly detection performance: A perspective on reconstruction loss amplification. Proc. ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 5165–5169 (2024).

4. Deng, J. et al. Imagenet: A large-scale hierarchical image database. Proc. 2009 IEEE Conference on Computer Vision and Pattern Recognition, 248–255 (2009).

5. Caron, M. et al. Emerging properties in self-supervised vision transformers. Proc. of the IEEE/CVF International Conference on Computer Vision (ICCV), 9650–9660 (2021).

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