The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Automatic Road Crack Detection and Characterization
Open accessAbstract
A fully integrated system for the automatic detection and characterization of cracks in road flexible pavement surfaces, which does not require manually labeled samples, is proposed to minimize the human subjectivity resulting from traditional visual surveys. The first task addressed, i.e., crack detection, is based on a learning from samples paradigm, where a subset of the available image database is automatically selected and used for unsupervised training of the system. The system classifies nonoverlapping image blocks as either containing crack pixels or not. The second task deals with crack type characterization, for which another classification system is constructed, to characterize the detected cracks' connect components. Cracks are labeled according to the types defined in the Portuguese Distress Catalog, with each different crack present in a given image receiving the appropriate label. Moreover, a novel methodology for the assignment of crack severity levels is introduced, computing an estimate for the width of each detected crack. Experimental crack detection and characterization results are presented based on images captured during a visual road pavement surface survey over Portuguese roads, with promising results. This is shown by the quantitative evaluation methodology introduced for the evaluation of this type of system, including a comparison with human experts' manual labeling results.
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Cites
A Threshold Selection Method from Gray-Level Histograms
Otsu, N. (1979) · IEEE Transactions on Systems, Man, and Cybernetics · doi:10.1109/tsmc.1979.4310076 · Cited by 43,398
A new method for gray-level picture thresholding using the entropy of the histogram
Kapur, J. N., Sahoo, P. K., & Wong, A. K. C. (1985) · Computer Vision, Graphics, and Image Processing · doi:10.1016/0734-189x(85)90125-2 · Cited by 3,658
Data-Driven Intelligent Transportation Systems: A Survey
Zhang, J., Wang, F.-Y., Wang, K., Lin, W.-H., Xu, X., & Chen, C. (2011) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2011.2158001 · Cited by 1,817
Two denoising methods by wavelet transform
Pan, Q., Zhang, L., Dai, G., & Zhang, H. (1999) · IEEE Transactions on Signal Processing · doi:10.1109/78.806084 · Cited by 313
Automatic Road Pavement Assessment with Image Processing: Review and Comparison
Chambon, S., & Moliard, J.-M. (2011) · International Journal of Geophysics · doi:10.1155/2011/989354 · Cited by 276
Unsupervised, information-theoretic, adaptive image filtering for image restoration
Awate, S. P., & Whitaker, R. T. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.64 · Cited by 268
Evaluating Pavement Cracks with Bidimensional Empirical Mode Decomposition
Ayenu-Prah, A., & Attoh-Okine, N. (2008) · EURASIP Journal on Advances in Signal Processing · doi:10.1155/2008/861701 · Cited by 242
Automatic inspection of pavement cracking distress
Huang, Y., & Xu, B. (2006) · Journal of Electronic Imaging · doi:10.1117/1.2177650 · Cited by 214
Position‐Invariant Neural Network for Digital Pavement Crack Analysis
Lee, B. J., & Lee, H. “. (2004) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/j.1467-8667.2004.00341.x · Cited by 164
Automatic pavement distress detection system
Cheng, H.-D., & Miyojim, M. (1998) · Information Sciences · doi:10.1016/s0020-0255(97)10062-7 · Cited by 114
Cited by
Amhaz, R., Chambon, S., Idier, J., & Baltazart, V. (2016) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2015.2477675 · Cited by 507
Kheradmandi, N., & Mehranfar, V. (2022) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2021.126162 · Cited by 426
Structural crack detection using deep convolutional neural networks
Ali, R., Chuah, J. H., Talip, M. S. A., Mokhtar, N., & Shoaib, M. A. (2022) · Automation in Construction · doi:10.1016/j.autcon.2021.103989 · Cited by 369
Zakeri, H., Nejad, F. M., & Fahimifar, A. (2017) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-016-9194-z · Cited by 304
Xu, Y., Bao, Y., Chen, J., Zuo, W., & Li, H. (2019) · Structural Health Monitoring · doi:10.1177/1475921718764873 · Cited by 292
Metadata
- Authors
- Oliveira, H., & Correia, P. L.
- Venue
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2013-03
- Volume
- 14
- Issue
- 1
- Pages
- 155-168
- ISSN
- 1524-9050 (ISSN-L)
- 1558-0016
- OpenAlex
- W1995130521
- Cited by
- 537
- References
- 20
- FWCI
- 14.10
- Type
- journal article
- Language
- en
- Open access
- green
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