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Computer vision-based statistical crack quantification for concrete structures
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DOI：10.1016/j.measurement.2023.112632.png)
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摘要
En 中文
This paper newly proposed a computer vision-based crack quantification algorithm using a statistical approach. Recently, high-resolution digital images have been effectively utilized for automated crack width and length evaluation on concrete structures. However, cracks are often difficult to accurately measure by randomly distributed and complex shapes. To overcome the technical limitation, a novel statistical crack quantification algorithm is proposed and experimentally validated through concrete structures in this paper. First, cracks on digital images are automatically detected using a deep semantic segmentation network. Then, multi-branched cracks are separated into single cracks through crack map generation. Each separated crack length and width is calculated by the Euclidean distance transform algorithm. Finally, crack width is presented as a representative value with a statistical confidence interval. The quantitative crack evaluation results for width and length were successfully compared with the actual field measurement values by average differences of 18.07% and -26.28%, respectively.
Keyword:
Statistical crack quantification
Automated crack detection
Crack separation
Crack map
Deep learning
Computer vision
期刊
IF:
5.6
论文数:
2.1W
被引数:
6.0W
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