Road surface detection and differentiation considering surface damages
Author:
Funder
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence
Link
http://link.springer.com/content/pdf/10.1007/s10514-020-09964-3.pdf
Reference60 articles.
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3. Banharnsakun, A. (2017). Hybrid abc-ann for pavement surface distress detection and classification. International Journal of Machine Learning and Cybernetics, 8(2), 699–710. https://doi.org/10.1007/s13042-015-0471-1.
4. Brostow, G. J., Fauqueur, J., & Cipolla, R. (2009). Semantic object classes in video: A high-definition ground truth database. Pattern Recognition Letters, 30(2), 88–97. https://doi.org/10.1016/j.patrec.2008.04.005.
5. Cabral, F. S., Pinto, M., Mouzinho, FALN., Fukai, H., & Tamura, S. (2018) An automatic survey system for paved and unpaved road classification and road anomaly detection using smartphone sensor. In: 2018 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI) (pp. 65–70). https://doi.org/10.1109/SOLI.2018.8476788.
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