The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Automatic Crack Detection on Two-Dimensional Pavement Images: An Algorithm Based on Minimal Path Selection
Open accessAbstract
This paper proposes a new algorithm for automatic crack detection from 2D pavement images. It strongly relies on the localization of minimal paths within each image, a path being a series of neighboring pixels and its score being the sum of their intensities. The originality of the approach stems from the proposed way to select a set of minimal paths and the two postprocessing steps introduced to improve the quality of the detection. Such an approach is a natural way to take account of both the photometric and geometric characteristics of pavement images. An intensive validation is performed on both synthetic and real images (from five different acquisition systems), with comparisons to five existing methods. The proposed algorithm provides very robust and precise results in a wide range of situations, in a fully unsupervised manner, which is beyond the current state of the art.
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Cites
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CrackTree: Automatic crack detection from pavement images
Zou, Q., Cao, Y., Li, Q., Mao, Q., & Wang, S. (2012) · Pattern Recognition Letters · doi:10.1016/j.patrec.2011.11.004 · Cited by 1,124
Complex Wavelet Structural Similarity: A New Image Similarity Index
Sampat, M. P., Wang, Z., Gupta, S., Bovik, A. C., & Markey, M. K. (2009) · IEEE Transactions on Image Processing · doi:10.1109/tip.2009.2025923 · Cited by 656
Global Minimum for Active Contour Models: A Minimal Path Approach
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Automatic Road Crack Detection and Characterization
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Cited by
Machine Learning for Crack Detection: Review and Model Performance Comparison
Hsieh, Y.-A., & Tsai, Y. J. (2020) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)cp.1943-5487.0000918 · Cited by 420
A cost effective solution for pavement crack inspection using cameras and deep neural networks
Mei, Q., & Gül, M. (2020) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2020.119397 · Cited by 253
A Comprehensive Review of Deep Learning-Based Crack Detection Approaches
Hamishebahar, Y., Guan, H., So, S., & Jo, J. (2022) · Applied Sciences · doi:10.3390/app12031374 · Cited by 188
A simulation-based few samples learning method for surface defect segmentation
Wei, T., Cao, D., Zheng, C., & Yang, Q. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.06.090 · Cited by 21
Metadata
- Authors
- Amhaz, R., Chambon, S., Idier, J., & Baltazart, V.
- Venue
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2016-10
- Volume
- 17
- Issue
- 10
- Pages
- 2718-2729
- ISSN
- 1524-9050 (ISSN-L)
- 1558-0016
- OpenAlex
- W2523358814
- Cited by
- 507
- References
- 26
- FWCI
- 37.60
- Type
- journal article
- Language
- en
- Open access
- green
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- Amhaz, R., Chambon, S., Idier, J., & Baltazart, V. (2016). Automatic Crack Detection on Two-Dimensional Pavement Images: An Algorithm Based on Minimal Path Selection. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/tits.2015.2477675
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