Cited by 577Industrial Vision Systems and Defect DetectionAdvanced Neural Network ApplicationsNon-Destructive Testing Techniques

Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection

Weimer, Daniel, Scholz-Reiter, Bernd, Shpitalni, Moshe·2016·CIRP Annals

Integrity signals

Registry

Crossref · 10.1016/j.cirp.2016.04.072

Cites

19 of 19 references verified0 citation errors · 0 not checkable

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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.”

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Authors
Weimer, D., Scholz-Reiter, B., & Shpitalni, M.
Affiliations
Technion – Israel Institute of Technology (IL)
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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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