Improving Automated Visual Fault Detection by Combining a Biologically Plausible Model of Visual Attention with Deep Learning-Reference-Cited by-同舟云学术

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Improving Automated Visual Fault Detection by Combining a Biologically Plausible Model of Visual Attention with Deep Learning

Published:2020-10-18 Issue: Volume: Page:
ISSN:
Container-title:IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society
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Author:

Beuth Frederik,Schlosser Tobias,Friedrich Michael,Kowerko Danny

Funder

Ministry of Education

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9254213/9254215/09255234.pdf?arnumber=9255234

Cited by 5 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. High-speed Optical Detection of Chipping Defects in a Die Bonder;2023 IEEE 25th Electronics Packaging Technology Conference (EPTC);2023-12-05

2. Towards Low-Cost Classification for Novel Fine-Grained Datasets;Electronics;2022-08-29

3. Semiconductor Defect Detection by Hybrid Classical-Quantum Deep Learning;2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR);2022-06

4. Improving automated visual fault inspection for semiconductor manufacturing using a hybrid multistage system of deep neural networks;Journal of Intelligent Manufacturing;2022-01-25

5. A Systematic Review of Deep Learning for Silicon Wafer Defect Recognition;IEEE Access;2021

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