Civil infrastructure defect assessment using pixel-wise segmentation based on deep learning
Article Properties
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LanguageEnglish
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DOI (url)
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Publication Date2022/08/25
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Indian UGC (journal)
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Refrences35
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Citations4
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Pierclaudio Savino ORCID (unauthenticated)
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Francesco Tondolo
Abstract
Cite
Savino, Pierclaudio, and Francesco Tondolo. “Civil Infrastructure Defect Assessment Using Pixel-Wise Segmentation Based on Deep Learning”. Journal of Civil Structural Health Monitoring, vol. 13, no. 1, 2022, pp. 35-48, https://doi.org/10.1007/s13349-022-00618-9.
Savino, P., & Tondolo, F. (2022). Civil infrastructure defect assessment using pixel-wise segmentation based on deep learning. Journal of Civil Structural Health Monitoring, 13(1), 35-48. https://doi.org/10.1007/s13349-022-00618-9
Savino, Pierclaudio, and Francesco Tondolo. “Civil Infrastructure Defect Assessment Using Pixel-Wise Segmentation Based on Deep Learning”. Journal of Civil Structural Health Monitoring 13, no. 1 (2022): 35-48. https://doi.org/10.1007/s13349-022-00618-9.
Savino P, Tondolo F. Civil infrastructure defect assessment using pixel-wise segmentation based on deep learning. Journal of Civil Structural Health Monitoring. 2022;13(1):35-48.
Journal Category
Technology
Engineering (General)
Civil engineering (General)
Refrences
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
A review of computer vision–based structural health monitoring at local and global levels | Structural Health Monitoring |
| 285 | 2020 |
Automatic Detection of Concrete Spalling Using Piecewise Linear Stochastic Gradient Descent Logistic Regression and Image Texture Analysis | Complexity |
| 27 | 2019 |
Surface fatigue crack identification in steel box girder of bridges by a deep fusion convolutional neural network based on consumer-grade camera images | Structural Health Monitoring |
| 173 | 2019 |
| Advances in Computer Vision-Based Civil Infrastructure Inspection and Monitoring | Engineering |
| 520 | 2019 |
Automatic Pixel‐Level Crack Detection and Measurement Using Fully Convolutional Network | Computer-Aided Civil and Infrastructure Engineering |
| 428 | 2018 |
Refrences Analysis
The category
Technology: Engineering (General). Civil engineering (General) 16
is the most frequently represented among the references in this article. It primarily includes studies from Journal of Civil Structural Health Monitoring and Sensors. 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 |
|---|---|---|---|---|
| Concrete forensic analysis using deep learning-based coarse aggregate segmentation | Automation in Construction |
| 2024 | |
Image-based automatic multiple-defect detection of urban utility tunnel using UUTNet | Advances in Structural Engineering |
| 2024 | |
Automatic non-destructive UAV-based structural health monitoring of steel container cranes | Applied Geomatics |
| 2023 | |
Deep Learning-Based Steel Bridge Corrosion Segmentation and Condition Rating Using Mask RCNN and YOLOv8 | Infrastructures |
| 1 | 2023 |
Citations Analysis
The category
Technology: Building construction: Architectural engineering. Structural engineering of buildings 3
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Deep Learning-Based Steel Bridge Corrosion Segmentation and Condition Rating Using Mask RCNN and YOLOv8 and was published in 2023. The most recent citation comes from a 2024 study titled Image-based automatic multiple-defect detection of urban utility tunnel using UUTNet. This article reached its peak citation in 2024, with 2 citations. It has been cited in 4 different journals, 25% of which are open access. Among related journals, the Advances in Structural Engineering cited this research the most, with 1 citations. The chart below illustrates the annual citation trends for this article.