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
Overview
Integrity signals
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Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
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
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336
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
Fukushima, K. (1980) · Biological Cybernetics · doi:10.1007/bf00344251 · Cited by 5,759
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
Items 301–310 of 310
Rastegar, H., & Khotanlou, H. (2026) · Multimedia Tools and Applications · doi:10.1007/s11042-026-21321-7
Super-resolution-guided dense enhancement segmentation network for defect detection of strip steel
Deng, Y., Liu, Y., Li, X., Zhao, F., Yang, H., & Xu, C. (2026) · Engineering Research Express · doi:10.1088/2631-8695/ae4676
DualCA-DETR: A Lightweight Detector for Smartphone Screen Glass Defect Detection
Wu, P., Fan, X., Yu, Y., Yang, H., Yang, Z., He, G., Liu, J., & Gao, J. (2026) · IEEE Sensors Journal · doi:10.1109/jsen.2026.3681720
James, S., & Ambigai, R. (2026) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2026.105533
Gabal, M. T. A., El-Desouky, M. A., Hanna, D. H., & Attia, M. S. (2026) · RSC Advances · doi:10.1039/d6ra01637e
A Comatic Metasurface Hologram for Ultrawide Light Angle Measurement
Jiang, S., Dong, S., Wang, Y., Huang, L., Zeng, Z., Zheng, K., Xu, F., Huang, H., Shi, Y., & Zhu, W. (2026) · Laser & Photonics Review · doi:10.1002/lpor.71428
Digital Twin Robot System for Ceramic Defect Detection and Localization
Zhang, J., Ding, W., & ZHANG, H. Q. (2026) · Conference on Fully Actuated System Theory and Applications (FASTA) · doi:10.1109/fasta70174.2026.11549015
Computer Vision-Based CNN Model Automated Defect Detection in Concrete Through Cellphone Camera
Ahmad, A., Ullah, M., Salman, M., & Noman, M. (2026) · Structural integrity · doi:10.1007/978-3-032-19744-3_20
TriFusion-DETR: A Light-weight Real-time Detector for Smartphone Screen Glass Defects
Fan, X., Wu, P., Yu, Y., Kandel, N., Yang, Z., & He, G. (2026) · IEEE Data Driven Control and Learning Systems Conference (DDCLS) · doi:10.1109/ddcls71227.2026.11610057
AUC optimization with the Elastic-Net penalty
Lee, E., & Kim, H. (2026) · Journal of Statistical Computation and Simulation · doi:10.1080/00949655.2026.2750188
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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