Efficient Pavement Monitoring for South Korea Using Unmanned Aerial Vehicles
Publication: International Conference on Transportation and Development 2022
ABSTRACT
The present study is aimed at using unmanned aerial vehicles (UAVs) for pavement monitoring. It was taken up as an initial pilot study to develop a network level asset management framework for South Korean conditions. A varying pavement stretch containing a bridge, culvert, streams, and merging traffic junctions was selected for the study. Using a quadcopter UAV, 139 overlapping images were acquired and processed using a structure from motion (SfM) program to develop a digital twin of the pavement. The generated orthomosaic and 3D digital twin were used to identify pavement damage and other infrastructural assets. UAV-based image acquisition was found to provide sufficient resolution, exposure, and key point matches, enabling an accurate 3D model generation with detailed feature extraction. Geometric measurements of various features depicted the potential and efficiency of UAV surveys. The research work is expected to aid in effective contactless pavement monitoring and asset management during regular surveys as well as disasters.
Get full access to this article
View all available purchase options and get full access to this chapter.
REFERENCES
Barmpounakis, E. N., Vlahogianni, E. I., and Golias, J. C. (2016). “Unmanned Aerial Aircraft Systems for transportation engineering: Current practice and future challenges.” International Journal of Transportation Science and Technology, 5(3), 111–122.
Congress, S. S. C., and Puppala, A. J. (2020). “Evaluation of UAV–CRP data for monitoring transportation infrastructure constructed over expansive soils.” Indian Geotechnical Journal, 50(2), 159–171.
Congress, S. S. C., and Puppala, A. J. (2021). “Digital Twinning Approach for Transportation Infrastructure Asset Management Using UAV Data.” In International Conference on Transportation and Development 2021 (pp. 321–331).
Congress, S. S. C., Puppala, A. J., Kumar, P., and Patil, U. D. (2021). “Assessment of Pavement Geometric Characteristics Using UAV-CRP Data.” In International Conference on Transportation and Development 2021 (pp. 332–343).
Congress, S. S., Puppala, A. J., and Lundberg, C. L. (2018). “Total system error analysis of UAV-CRP technology for monitoring transportation infrastructure assets.” Engineering Geology, 247, 104–116.
Davis, B. J. (2018). Development of the MASW Method for Pavement Evaluation. Theses and Dissertations. 1468, University of Arkansas, USA. http://scholarworks.uark.edu/etd/1468.
Dobson, R. J., Brooks, C., Roussi, C., and Colling, T. (2013, May). “Developing an unpaved road assessment system for practical deployment with high-resolution optical data collection using a helicopter UAV.” In 2013 International Conference on Unmanned Aircraft Systems (ICUAS) (pp. 235–243). IEEE.
Du Tertre, A. (2010). Non-destructive evaluation of asphalt pavement joints using LWD and MASW tests. Master’s thesis, University of Waterloo, Ontario, Canada.
Ferrer-Espinoza, D., Atencio, E., Muñoz-La Rivera, F., and Herrera, R. F. (2021). “Evaluation of the use of Cascade Detection algorithms based on Machine Learning for Crack Detection in asphalt Pavements.” Solid State Technology, 64(2), 5588–5605.
Gucunski, N., and National Research Council. (2013). Nondestructive testing to identify concrete bridge deck deterioration. Transportation Research Board 2013.
Hong, Z., Yang, F., Pan, H., Zhou, R., Zhang, Y., Han, Y., Wang, J., Yang, S., Chen, P., Tong, X., and Liu, J. (2021). “Highway Crack Segmentation from Unmanned Aerial Vehicle Images Using Deep Learning.” IEEE Geoscience and Remote Sensing Letters.
Inzerillo, L., Di Mino, G., and Roberts, R. (2018). “Image-based 3D reconstruction using traditional and UAV datasets for analysis of road pavement distress.” Automation in Construction, 96, 457–469.
Knyaz, V. A., and Chibunichev, A. G. (2016). “Photogrammetric techniques for road surface analysis.” Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci, 41(B5), 515–520.
KOCA (Korea office of civil aviation). (2020). Guidelines for drone usage. <www.koca.co.kr>(accessed on 15th September, 2021).
Lee, J. K. (2019). UAV-based Pothole Identification: A Photogrammetric Approach (Doctoral dissertation).
Outay, F., Mengash, H. A., and Adnan, M. (2020). “Applications of unmanned aerial vehicle (UAV) in road safety, traffic and highway infrastructure management: Recent advances and challenges.” Transportation research part A: policy and practice, 141, 116–129.
Pan, Y., Chen, X., Sun, Q., and Zhang, X. (2021). “Monitoring Asphalt Pavement Aging and Damage Conditions from Low-Altitude UAV Imagery Based on a CNN Approach.” Canadian Journal of Remote Sensing, 47(3), 432–449.
Pan, Y., Zhang, X., Cervone, G., and Yang, L. (2018). “Detection of asphalt pavement potholes and cracks based on the unmanned aerial vehicle multispectral imagery.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(10), 3701–3712.
Puppala, A. J., and Congress, S. S. C. (2019, September). “A holistic approach for visualization of transportation infrastructure assets using UAV-CRP technology.” In International Conference on Information technology in Geo-Engineering (pp. 3–17). Springer, Cham.
Saad, A. M., and Tahar, K. N. (2019). “Identification of rut and pothole by using multirotor unmanned aerial vehicle (UAV).” Measurement, 137, 647–654.
Shanghlil, N., and Khalafallah, A. (2018, March). “Automating highway infrastructure maintenance using unmanned aerial vehicles.” In Construction Research Congress (pp. 2–4).
Silva, L. A., Sanchez San Blas, H., Peral García, D., Sales Mendes, A., and Villarubia González, G. (2020). “An architectural multi-agent system for a pavement monitoring system with pothole recognition in UAV images.” Sensors, 20(21), 6205.
Tan, Y., and Li, Y. (2019). “UAV photogrammetry-based 3D road distress detection.” ISPRS International Journal of Geo-Information, 8(9), 409.
Wu, W., Qurishee, M. A., Owino, J., Fomunung, I., Onyango, M., and Atolagbe, B. (2018, September). “Coupling deep learning and UAV for infrastructure condition assessment automation.” In 2018 IEEE International Smart Cities Conference (ISC2) (pp. 1–7). IEEE.
Zhang, C. (2008, July). “Development of a UAV-based remote sensing system for unpaved road condition assessment.” In Proceedings of American Society for Photogrammetry and Remote Sensing Annual Conference. Portland, OR.
Zhang, C., and Elaksher, A. (2012). “An unmanned aerial vehicle‐based imaging system for 3D measurement of unpaved road surface distresses.” Computer‐Aided Civil and Infrastructure Engineering, 27(2), 118–129.
Zhu, J., Zhong, J., Ma, T., Huang, X., Zhang, W., and Zhou, Y. (2022). “Pavement distress detection using convolutional neural networks with images captured via UAV.” Automation in Construction, 133, 103991.
Information & Authors
Information
Published In
International Conference on Transportation and Development 2022
Pages: 61 - 72
History
Published online: Aug 31, 2022
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.