•  
  •  
 

The Scientific Journal of Vehicles and Roads

Abstract

V dannoy statye izucheni vozmojnosti razlichnix prilojeniy, ispolzuemix dlya izmereniya rovnosti dorojnogo pokritiya s pomoshyu smartfonov, a takje proanalizirovani vozmojnosti, preimushestva i nedostatki dlya sozdaniya dorojnoy bazi dannix v usloviyax Uzbekistane.

First Page

6-11

References

1. I.S.Sadikov and A.Kh.Urakov. System approach to improvement and arrangement of automotive roads, The Scientific Journal of Vehicles and Roads: Vol. 2018: Iss. 2, Article 14. 2. A.Uroqov, R.Soataliyev. Intellectual compaction. System in construction of roads and possibilities of its use in Uzbekistan. The Scientific Journal of Vehicles and Roads: Vol. 2020: Iss. 2, Article 9. 3. Sodikov J.I. and Silyanov V.V. 2015 Road asset management systems in developing countries: Case study in Uzbekistan Sci. J. of Transportation 6 (Moscow–China–Vietnam) Retrieved from: https://www.researchgate.net/publication/272415247_road_asset_anagement_systems_in_developing_countries_case_study_uzbekistan 4. Sodikov J, Tsunokawa K and Ul-Islam R 2005 Road Survey with Romdas System: A Study in Akita Prefecture Saitama University Departmental Bull. Paper 149–51. 5. R.Soataliyev, IRI measurement results using TotalPave http://dx.doi.org/10.13140/RG.2.2.36715.05928 6. ИКН 05-2011 Правила диагностики и оценки состояния автомобильных дорог Узбекистана. г.Ташкент. 2005-408 с. 7. А.Х. Уроков, Р.Р. Соаталиев. Возможности измерения и визуализации ровности покрытия автомобильных дорог на основе смартфонов в Узбекистане. Сборник международной научно-технической конференции. Транспорт: актуальные задачи и инновации. 2021 г., 301-304 стр. 8. Chatterjee, Sromona; Saeedfar, Pouya; Tofangchi, Schahin; and Kolbe, Lutz, "Intelligent road maintenance: A machine learning approach for surface defect detection (2018). Research Papers. 194. https://aisel.aisnet.org/ecis2018_rp/194 9. Chen, S. Y., Zhang, Y., Zhang, Y. H., Yu, J. J., Zhu, Y. X. Embedded System for Road Damage Detection by Deep Convolutional Neural Network. Mathematical Biosciences and Engineering, Vol. 16, No. 6, 2019, pp. 7982–7994. 10. Chun, C., Ryu, S. K. Road Surface Damage Detection using Fully Convolutional Neural Networks and Semi-Supervised Learning. Sensors, Vol. 19, No. 24, 2019, p. 5501. 11. Wenming cao, qifan liu, and Zhiquan He. Review of pavement defect detection methods January 2020 IEEE Access PP(99):1-1 12. ГОСТ 33180-2014 Дороги автомобильные общего пользования. Требования к уровню летнего содержания 13. https://teachablemachine.withgoogle.com/models/CAL_mr6Lo/ 14. Hiroya Maeda, et al. "Road Damage Detection Using Deep Neural Networks with Images Captured Through a Smartphone", 1801.09454, arXiv, 2018.

Share

COinS