Reduced Featured Based Projective Integral for Road Cracks Detection and Classification
Author:
Publisher
Pleiades Publishing Ltd
Subject
Computer Graphics and Computer-Aided Design,Computer Vision and Pattern Recognition
Link
https://link.springer.com/content/pdf/10.1134/S1054661820020029.pdf
Reference19 articles.
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2. E. Schnebele, B. F. Tanyu, G. Cervone, and N. Waters, “Review of remote sensing methodologies for pavement management and assessment,” Eur. Transp. Res. Rev. 7, Article 7 (2015). https://doi.org/10.1007/s12544-015-0156-6
3. S. Zhang and S. M. Bogus, “Use of low-cost remote sensing for infrastructure management,” in Construction Research Congress 2014: Construction in a Global Network (Atlanta, GA, 2014), Ed. by D. Castro-Lacouture, J. Irizarry, and B. Ashuri (ASCE, 2014), pp. 1299–1308. https://doi.org/10.1061/9780784413517.133
4. A. Cubero-Fernandez, F. J. Rodriguez-Lozano, R. Villatoro, J. Olivares, and J. M. Palomares, “Efficient pavement crack detection and classification,” EURASIP J. Image Video Process. 2017, Article 39 (2017). https://doi.org/10.1186/s13640-017-0187-0
5. B. Peng, Y.-S. Jiang, and Y. Pu, “Review on automatic pavement crack image recognition algorithms,” J. Highw. Transp. Res. Dev. 9 (2), 13–20 (2015). https://doi.org/10.1061/JHTRCQ.0000435
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