The DOI matches its Crossref record; 46 of the 49 references it cites were found in a registry or an index.
Railway track fastener defect detection based on image processing and deep learning techniques: A comparative study
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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
“However, the above deep learning-based industrial defect detection methods do not specifically optimize the model's ability to extract and fuse features, which is crucial for defect detection.”
From A deep learning model for steel surface defect detection (2024) · cites this paper as [19]
Citations
Cites
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
A Threshold Selection Method from Gray-Level Histograms
Otsu, N. (1979) · IEEE Transactions on Systems, Man and Cybernetics · doi:10.1109/tsmc.1979.4310076 · Cited by 44,708
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684
(2003) · Journal of Machine Learning Research · doi:10.5555/944919.944937 · Cited by 32,337
Girshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105
Research works printed but not linked: 3
- Berry, A., Nejikovsky, B., Gilbert, X., Tajaddini, A., 2008. High speed video inspection of joint bars using advanced image collection and processing techniques. In: Proc. of World Congress on Railway Research, vol. 290. pp. 619–622.
- Jiajia, 2016, Research on Automatic Inspection Algorithm for Railway Fastener Defects Based on Computer Vision, J. China Railway Soc., 38, 73
- Zuwen, 2007, Overall Comments on Track Technology of High- speed Railway, J. Railway Eng. Soc., 41
Cited by
Sorted by citations
Yang, J., Li, S., Wang, Z., Dong, H., Wang, J., & Tang, S. (2020) · Materials · doi:10.3390/ma13245755 · Cited by 539
A literature review of Artificial Intelligence applications in railway systems
Tang, R., De Donato, L., Bes̆inović, N., Flammini, F., Goverde, R. M. P., Lin, Z., Liu, R., Tang, T., Vittorini, V., & Wang, Z. (2022) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2022.103679 · Cited by 324
Deep Learning and Machine Vision Approaches for Posture Detection of Individual Pigs
Nasirahmadi, A., Sturm, B., Edwards, S. A., Jeppsson, K.-H., Olsson, A.-C., Müller, S., & Hensel, O. (2019) · Sensors · doi:10.3390/s19173738 · Cited by 150
Wei, X., Wei, D., Suo, D., Jia, L., & Li, Y. (2020) · IEEE Access · doi:10.1109/access.2020.2984264 · Cited by 136
Self-citationA survey of real-time surface defect inspection methods based on deep learning
Liu, Y., Zhang, C., & Dong, X. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10475-7 · Cited by 136
Self-citation
Metadata
- Authors
- Wei, X., Yang, Z., Liu, Y., Wei, D., Jia, L., & Li, Y.
- Affiliations
- Beijing Jiaotong University (CN)
- Corresponding author
- Wei, Xiukun
- Venue
- Engineering Applications of Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2019-04
- Volume
- 80
- Pages
- 66-81
- ISSN
- 0952-1976 (ISSN-L)
- OpenAlex
- W2912130719
- Cited by
- 338
- References
- 49
- Citations by year
- 2026: 25 · 2025: 48 · 2024: 66 · 2023: 48 · 2022: 65 · 2021: 48 · 2020: 26 · 2019: 12
- FWCI
- 28.24
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Engineering Applications of Artificial Intelligence (publishedVersion)
- Funders
- National Key R&D Program of China (2016YFB1200402)
- State Key Lab of Rail Traffic Control and Safety (RCS2019ZT0046)
- Topics
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- 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
- Keywords
- railway track fasteners, VGG16, Faster R-CNN, track safety
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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