Roads and Bridges - Drogi i Mosty
22, 4, 2023, 331-345

Assessment of the use of unmanned aerial vehicles for road pavement condition surveying

Anna Małek Mail
https://orcid.org/0000-0002-2960-0902
Poznan University of Technology, Faculty of Civil and Transport Engineering, Civil Engineering Institute, Division of Geotechnics, Engineering Geology and Geodesy, 5 Piotrowo St., 60-965 Poznań
Published: 2023-12-21

Abstract

The article presents evaluation of road pavement surface diagnostics performed using an unmanned aerial vehicle (UAV). The work encompasses analysis of the potential for use of UAVs in pavement condition diagnostics, the methodology of field surveys and use of photogrammetry software. The experimental part includes a comparison of the results obtained for chosen types of pavement distress using orthophotomaps (created from images captured with UAV during flights at four different altitudes) and data obtained in field using a measuring tape and a total station. The results indicated that the measurement accuracy for chosen types of pavement distress (potholes, patches, cracks) obtained using typical surveying methods was similar to that obtained using aerial imaging technology (the difference does not exceed 1 cm). Using an unmanned aerial vehicle with an 1/2" image sensor, focal length of 24 mm and flight altitude of 5 m, it is possible to detect cracks from 1 mm in size; in the case of flight altitude of 30 m it is possible to detect cracks from 4 mm. The presented analyses indicate that UAVs may be successfully used in road surface feature diagnostics as an independent early damage detection system or as an extension of traditional surveying methods.

Keywords


3D modeling, assessment of pavement damage, pavement diagnostics, photogrammetry, UAV.

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References


Cebon D.: Vehicle-generated road damage: a review. Vehicle system dynamics, 18, 1-3, 1989, 107-150, DOI: 10.1080/00423118908968916

Hassan Y., Abd El Halim A.O., Razaqpur A.G., Bekheet W., Farha M.H.: Effects of runway deicers on pavement materials and mixes: comparison with road salt. Journal of Transportation Engineering, 128, 4, 2002, 385-391, DOI: 10.1061/(ASCE)0733-947X(2002)128:4(385)

Chlipalski K.: Błędy popełniane podczas budowy i eksploatacji dróg. Drogownictwo, 10, 2007, 120-124

Leonovich I., Melnikova I.: Influence of temperature on the formation of damages in asphalt concrete pavements under climatic conditions of the Republic of Belarus. The Baltic Journal of Road and Bridge Engineering, 7, 1, 2012, 42-47, DOI: 10.3846/bjrbe.2012.06

Talvik O., Aavik A.: Use of FWD deflection basin parameters (SCI, BDI, BCI) for pavement condition assessment. The Baltic Journal of Road and Bridge Engineering, 4, 4, 2009, 196-202, DOI: 10.3846/1822-427X.2009.4.196-202

Loizos A., Al-Qadi I., Scarpas T.: Bearing Capacity of Roads, Railways and Airfields, CRC Press, Londyn, 2017

Pożarycki A., Górnaś P., Wanatowski D.: The influence of frequency normalisation of FWD pavement measurements on backcalculated values of stiffness moduli. Road Materials and Pavement Design, 20, 1, 2019, 1-19, DOI: 10.1080/14680629.2017.1374991

Yu S., Sukumar S.R., Koschan A.F., Page D.L., Abidi M.A.: 3D reconstruction of road surfaces using an integrated multi-sensory approach. Optics and Lasers in Engineering, 45, 7, 2007, 808-818, DOI: 10.1016/j.optlaseng.2006.12.007

Soilán M., Sánchez-Rodríguez A., del Río-Barral P., Perez-Collazo C., Arias P., Riveiro B.: Review of laser scanning technologies and their applications for road and railway infrastructure monitoring. Infrastructures, 4, 4, 2019, ID articles: 58, DOI: 10.3390/infrastructures4040058

Zhou Y., Guo X., Hou F., Wu J.: Review of Intelligent Road Defects Detection Technology. Sustainability, 14, 10, 2022, ID article: 6306, DOI: 10.3390/su14106306

Maeda H., Sekimoto S., Seto T., Kashiyama T., Omata H.: Road damage detection and classification using deep neural networks with smartphone images. Computer-Aided Civil and Infrastructure Engineering, 33, 12, 2018, 1127-1141, DOI: 10.1111/mice.12387

Ranyal E., Sadhu A., Jain K.: Road condition monitoring using smart sensing and artificial intelligence: A review. Sensors, 22, 8, 2022, 3044, DOI: 10.3390/s22083044

Heller S., Mechowski T., Harasim P.: Wykorzystanie badań diagnostycznych stanu nawierzchni do rozpoznania miejsc niebezpiecznych dla użytkowników drogi. Roads and Bridges - Drogi i Mosty, 9, 1, 2010, 57-75

Inzerillo L., Di Mino G., Roberts R.: Image-based 3D reconstruction using traditional and UAV datasets for analysis of road pavement distress. Automation in Construction, 96, 2018, 457-469, DOI: 10.1016/j.autcon.2018.10.010

Kubišta J., Surový P.: Spatial resolution of unmanned aerial vehicles acquired imagery as a result of different processing conditions. Central European Forestry Journal, 67, 3, 2021, 148-154, DOI: 10.2478/forj-2021-0011

Höhle J.: Oblique aerial images and their use in cultural heritage documentation. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-5/W2, 2013, 349-354, DOI: 10.5194/isprsarchives-XL-5-W2-349-2013

Cai C., Gao Y., Pan L., Jian-Jun Z.: Precise point positioning with quad-constellations: GPS, BeiDou, GLONASS and Galileo. Advances in Space Research, 56, 1, 2015, 133-143, DOI: 10.1016/j.asr.2015.04.001

Tang R., Fritsch D., Cramer M.: New rigorous and flexible Fourier self-calibration models for airborne camera calibration. ISPRS Journal of Photogrammetry and Remote Sensing, 71, 2012, 76-85, DOI: 10.1016/j.isprsjprs.2012.05.004

Ferrer-González E., Agüera-Vega F., Carvajal-Ramírez F., Martínez-Carricondo P.: UAV photogrammetry accuracy assessment for corridor mapping based on the number and distribution of ground control points. Remote Sensing, 12, 15, 2020, 2447, DOI: 10.3390/rs12152447

Roberts R., Inzerillo L., Mino G.: Using UAV based 3D modelling to provide smart monitoring of road pavement conditions. Information, 11, 12, 2020, ID article: 568, DOI: 10.3390/info11120568

Zeybek M., Bicici S.: Road distress measurements using UAV. Turkish Journal of Remote Sensing and GIS, 1, 1, 2020, 13-23

Liu Y., Zheng X., Ai G., Zhang Y., Zuo Y.: Generating a high-precision true digital orthophoto map based on UAV images. ISPRS International Journal of Geo-Information, 7, 9, 2018, 333, DOI: 10.3390/ijgi7090333

Mackiewicz P., Mączka E.: Wykorzystanie drona w identyfikacji uszkodzeń nawierzchni. Przegląd komunikacyjny, 2-3, 2022, 32-39

Cardenal J., Fernández T., Pérez-García J.L., Gómez- -López J.M.: Measurement of road surface deformation using images captured from UAVs. Remote Sensing, 11, 12, 2019, ID article: 1507, DOI: 10.3390/rs11121507

Barrile V., Bernardo E., Fotia A., Bilotta G.: Road safety: road degradation survey through images by UAV. WSEAS Transactions on Environment and Development, 16, 2020, 649-659, DOI: 10.37394/232015.2020.16.67

Coenen T.B.J., Golroo A., Lo Presti D.: A review on automated pavement distress detection methods. Cogent Engineering, 4, 1, 2017, ID article: 1374822, DOI: 10.1080/23311916.2017.1374822

Karaca Y., Cicek M., Tatli O., Sahin A., Pasli S., Beser M.F., Turedi S.: The potential use of unmanned aircraft systems (drones) in mountain search and rescue operations. American Journal of Emergency Medicine, 36, 4, 2018, 583-588, DOI: 10.1016/j.ajem.2017.09.025

Siebert S., Teizer J.: Mobile 3D mapping for surveying earthwork projects using an Unmanned Aerial Vehicle (UAV) system. Automation in Construction, 41, 2014, 1-14, DOI: 10.1016/j.autcon.2014.01.004

Rozporządzenie Wykonawcze Komisji Unii Europejskiej 2019/947 z dnia 24 maja 2019 r. w sprawie przepisów i procedur dotyczących eksploatacji bezzałogowych statków powietrznych. Dz. Urz. UE L 152 z 11.06.2019

Katalog typowych uszkodzeń nawierzchni bitumicznych dla potrzeb ciągłego obmiaru uszkodzeń metodą oceny wizualnej w systemie oceny stanu nawierzchni SOSN. Generalna Dyrekcja Dróg Krajowych i Autostrad, Warszawa, 2002

Zhang A., Wang K.C.P., Fei Y., Liu Y., Chen C., Yang G., Li J.Q., Yang E., Qiu S.: Automated pixel-level pavement crack detection on 3D asphalt surfaces with a recurrent neural network. Computer-Aided Civil and Infrastructure Engineering, 34, 3, 2019, 213-229, DOI: 10.1111/mice.12409


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Assessment of the use of unmanned aerial vehicles for road pavement condition surveying

  
Małek, Anna. Assessment of the use of unmanned aerial vehicles for road pavement condition surveying. Roads and Bridges - Drogi i Mosty, [S.l.], v. 22, n. 4, p. 331-345, dec. 2023. ISSN 2449-769X. Available at: <>. Date accessed: 24 Feb. 2024. doi:http://dx.doi.org/10.7409/rabdim.023.017.