A research on an improved Unet-based concrete crack detection algorithm
Article Properties
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LanguageEnglish
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DOI (url)
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Publication Date2020/07/21
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Journal
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Indian UGC (journal)
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Refrences42
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Citations102
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Lingxin Zhang Key Laboratory of Earthquake Engineering and Engineering Vibration, Institute of Engineering Mechanics, China Earthquake Administration, Harbin, China
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Junkai Shen Key Laboratory of Earthquake Engineering and Engineering Vibration, Institute of Engineering Mechanics, China Earthquake Administration, Harbin, China ORCID (unauthenticated)
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Baijie Zhu Key Laboratory of Earthquake Engineering and Engineering Vibration, Institute of Engineering Mechanics, China Earthquake Administration, Harbin, China
Abstract
Cite
Zhang, Lingxin, et al. “A Research on an Improved Unet-Based Concrete Crack Detection Algorithm”. Structural Health Monitoring, vol. 20, no. 4, 2020, pp. 1864-79, https://doi.org/10.1177/1475921720940068.
Zhang, L., Shen, J., & Zhu, B. (2020). A research on an improved Unet-based concrete crack detection algorithm. Structural Health Monitoring, 20(4), 1864-1879. https://doi.org/10.1177/1475921720940068
Zhang, Lingxin, Junkai Shen, and Baijie Zhu. “A Research on an Improved Unet-Based Concrete Crack Detection Algorithm”. Structural Health Monitoring 20, no. 4 (2020): 1864-79. https://doi.org/10.1177/1475921720940068.
Zhang L, Shen J, Zhu B. A research on an improved Unet-based concrete crack detection algorithm. Structural Health Monitoring. 2020;20(4):1864-79.
Journal Categories
Science
Mathematics
Instruments and machines
Technology
Engineering (General)
Civil engineering (General)
Refrences
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| Title | 2014 | |||
| 10.1109/IJCNN.2001.939002 | ||||
| Proceedings of the IEEE 18th international conference on pattern recognition (ICPR 2006), Hong Kong, China | ||||
| Proceedings of the international conference on medical image computing and computer-assisted intervention, Munich | ||||
| Proceedings of the international conference on machine learning (ICML 2015), Lille |
Citations
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
Evaluation of strength and stiffness degradation of RC shear walls: An integrated image processing and deep learning approach | Earthquake Engineering & Structural Dynamics |
| 2024 | |
| Autonomous damage segmentation of post-fire reinforced concrete structural components | Advanced Engineering Informatics |
| 2024 | |
| Efficient semi-supervised surface crack segmentation with small datasets based on consistency regularisation and pseudo-labelling | Automation in Construction |
| 2024 | |
| Crack detection of masonry structure based on thermal and visible image fusion and semantic segmentation | Automation in Construction |
| 2 | 2024 |
| An average pooling designed Transformer for robust crack segmentation | Automation in Construction |
| 2024 |
Citations Analysis
The category
Technology: Engineering (General). Civil engineering (General) 69
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Efficient attention-based deep encoder and decoder for automatic crack segmentation and was published in 2021. The most recent citation comes from a 2024 study titled Evaluation of strength and stiffness degradation of RC shear walls: An integrated image processing and deep learning approach. This article reached its peak citation in 2023, with 47 citations. It has been cited in 47 different journals, 25% of which are open access. Among related journals, the Automation in Construction cited this research the most, with 13 citations. The chart below illustrates the annual citation trends for this article.