The DOI matches its Crossref record; 20 of the 21 references it cites were found in a registry or an index.
Machine learning-based imaging system for surface defect inspection
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Thus, CNN-based architectures are widely applied on automatic feature extraction [103] as well as on image defect detection [104][105][106][107][108] in industrial inspection.”
From Automated Visual Defect Detection for Flat Steel Surface: A Survey (2020) · cites this paper as [104]
Citations
Cites
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A fast learning algorithm for deep belief nets
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Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A.-R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., et al. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2205597 · Cited by 10,260
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A unified architecture for natural language processing: deep neural networks with multitask learning
Collobert, R., & Weston, J. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390177 · Cited by 5,058
Image Processing, Analysis and Machine Vision
Sonka, M., Hlaváč, V., & Boyle, R. (1993) · Springer eBooks · doi:10.1007/978-1-4899-3216-7 · Cited by 3,731
A New Approach to Visual Servoing in Robotics
Espiau, B., Chaumette, F., & Rives, P. (1992) · IEEE Transactions on Robotics and Automation · doi:10.1109/70.143350 · Cited by 1,384
A new approach to visual servoing in robotics
Espiau, B., Chaumette, F., & Rives, P. (1993) · Lecture notes in computer science · doi:10.1007/3-540-57132-9_8 · Cited by 1,350
Defect detection in textured materials using Gabor filters
Kumar, A., & Pang, G. K. H. (2002) · IEEE Transactions on Industry Applications · doi:10.1109/28.993164 · Cited by 475
Convolutional Neural Networks for Steel Surface Defect Detection from Photometric Stereo Images
Soukup, D., & Huber-Mörk, R. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-14249-4_64 · Cited by 302
Cited by
Sorted by citations
Deep learning for smart manufacturing: Methods and applications
Wang, J., Ma, Y., Zhang, L., Gao, R. X., & Wu, D. (2018) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2018.01.003 · Cited by 1,740
State of the Art in Defect Detection Based on Machine Vision
Ren, Z., Fang, F., Yan, N., & Wu, Y. (2021) · International Journal of Precision Engineering and Manufacturing-Green Technology · doi:10.1007/s40684-021-00343-6 · Cited by 896
Automated Visual Defect Detection for Flat Steel Surface: A Survey
Luo, Q., Fang, X., Liu, L., Yang, C., & Sun, Y. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2019.2963555 · Cited by 559
Yang, J., Li, S., Wang, Z., Dong, H., Wang, J., & Tang, S. (2020) · Materials · doi:10.3390/ma13245755 · Cited by 539
Artificial intelligence and internet of things in small and medium-sized enterprises: A survey
Hansen, E. B., & Bøgh, S. (2020) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2020.08.009 · Cited by 442
Metadata
- Authors
- Park, J.-K., Kwon, B.-K., Park, J.-H., & Kang, D.-J.
- Affiliations
- Tongmyong University (KR)
- LG (South Korea) (KR)
- Pusan National University (KR)
- Corresponding author
- Kang, Dong-Joong
- Venue
- International Journal of Precision Engineering and Manufacturing-Green Technology
- Published
- 2016-07
- Volume
- 3
- Issue
- 3
- Pages
- 303-310
- ISSN
- 2198-0810 (ISSN-L)
- 2288-6206
- OpenAlex
- W2468676150
- Cited by
- 311
- References
- 21
- Citations by year
- 2026: 14 · 2025: 31 · 2024: 39 · 2023: 48 · 2022: 44 · 2021: 36 · 2020: 43 · 2019: 39 · 2018: 12 · 2017: 5
- FWCI
- 25.35
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Precision Engineering and Manufacturing-Green Technology (publishedVersion)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Engineering › Mechanical Engineering › Non-Destructive Testing Techniques
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Keywords
- surface defect detection, automated visual inspection, convolutional neural networks, defect detection, Scratch, burrs, wear, smart sensors, machine vision, network architecture comparison, textured surfaces
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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