GDXray: The Database of X-ray Images for Nondestructive Testing
Article Properties
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LanguageEnglish
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DOI (url)
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Publication Date2015/11/13
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Indian UGC (journal)
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Refrences31
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Citations195
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Domingo Mery
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Vladimir Riffo
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Uwe Zscherpel
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German Mondragón
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Iván Lillo
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Irene Zuccar
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Hans Lobel
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Miguel Carrasco
Cite
Mery, Domingo, et al. “GDXray: The Database of X-Ray Images for Nondestructive Testing”. Journal of Nondestructive Evaluation, vol. 34, no. 4, 2015, https://doi.org/10.1007/s10921-015-0315-7.
Mery, D., Riffo, V., Zscherpel, U., Mondragón, G., Lillo, I., Zuccar, I., Lobel, H., & Carrasco, M. (2015). GDXray: The Database of X-ray Images for Nondestructive Testing. Journal of Nondestructive Evaluation, 34(4). https://doi.org/10.1007/s10921-015-0315-7
Mery D, Riffo V, Zscherpel U, Mondragón G, Lillo I, Zuccar I, et al. GDXray: The Database of X-ray Images for Nondestructive Testing. Journal of Nondestructive Evaluation. 2015;34(4).
Journal Category
Technology
Electrical engineering
Electronics
Nuclear engineering
Materials of engineering and construction
Mechanics of materials
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Refrences
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| 10.1109/TMECH.2014.2311032 | IEEE/ASME Transactions on Mechatronics |
| 2015 | |
| Detection of flaws in aluminium castings: a comparative study between generative and discriminant approaches | Insight - Non-Destructive Testing and Condition Monitoring |
| 5 | 2013 |
| Detection of regular objects in baggage using multiple X-ray views | Insight - Non-Destructive Testing and Condition Monitoring |
| 16 | 2013 |
| Automated Design of a Computer Vision System for Visual Food Quality Evaluation | Food and Bioprocess Technology |
| 27 | 2013 |
| Active X-ray testing of complex objects | Insight - Non-Destructive Testing and Condition Monitoring |
| 13 | 2012 |
Refrences Analysis
The category
Technology: Electrical engineering. Electronics. Nuclear engineering: Materials of engineering and construction. Mechanics of materials 9
is the most frequently represented among the references in this article. It primarily includes studies from Materials Evaluation The chart below illustrates the number of referenced publications per year.
Refrences used by this article by year
Citations
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
Deploying Machine Learning for Radiography of Aerospace Welds | Journal of Nondestructive Evaluation |
| 2024 | |
| Enhancing Weld Inspection Through Comparative Analysis of Traditional Algorithms and Deep Learning Approaches | Journal of Nondestructive Evaluation |
| 2024 | |
| Improving Scattered Defect Grading in Castings Digital Radiographs via Smoothing the One-Hot Encoding | International Journal of Metalcasting |
| 2024 | |
| Cooperative distillation with X-ray images classifiers for prohibited items detection | Engineering Applications of Artificial Intelligence |
| 1 | 2024 |
| Research on X-ray weld seam defect detection and size measurement method based on neural network self-optimization | Engineering Applications of Artificial Intelligence |
| 2024 |
Citations Analysis
The category
Technology: Engineering (General). Civil engineering (General) 75
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Object Recognition in X-ray Testing Using Adaptive Sparse Representations and was published in 2016. The most recent citation comes from a 2024 study titled Improving Scattered Defect Grading in Castings Digital Radiographs via Smoothing the One-Hot Encoding. This article reached its peak citation in 2022, with 48 citations. It has been cited in 94 different journals, 20% of which are open access. Among related journals, the Journal of Nondestructive Evaluation cited this research the most, with 17 citations. The chart below illustrates the annual citation trends for this article.