Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection
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In contrast to FCN+CNN, CA-CNN, CNN and other patch-based models, our model does not require finer ground truth.
“In contrast to the FCN+CNN [58], CA-CNN [13], CNN [14] and other patch-based models, our model does not require finer ground truth.”
From A Compact Convolutional Neural Network for Surface Defect Inspection (2020) · cites this paper as [14]
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A Compact Convolutional Neural Network for Surface Defect Inspection
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- Authors
- Weimer, D., Scholz-Reiter, B., & Shpitalni, M.
- Affiliations
- Technion – Israel Institute of Technology (IL)
- ORCID
- Scholz-Reiter, Bernd
- Venue
- CIRP Annals
- Publisher
- Elsevier BV
- Published
- 2016
- Volume
- 65
- Issue
- 1
- Pages
- 417-420
- ISSN
- 0007-8506 (ISSN-L)
- OpenAlex
- W2418691539
- Cited by
- 577
- References
- 19
- Citations by year
- 2026: 43 · 2025: 40 · 2024: 59 · 2023: 78 · 2022: 81 · 2021: 90 · 2020: 85 · 2019: 56 · 2018: 36 · 2017: 7 · 2016: 1 · 2015: 1
- FWCI
- 45.27
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- CIRP Annals (publishedVersion)
- Funders
- DFG
- German Academic Exchange Service
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Mechanical Engineering › Non-Destructive Testing Techniques
- Keywords
- deep convolutional neural networks, automatic feature extraction, defect detection, industrial inspection, CNN architecture design, hyperparameter settings, hierarchical feature learning, false alarm rate, manufacturing quality control
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