A Review on Road Distress Detection Methods

Nadhirah Hani Mohd Nasir, Wan Mazlina Wan Mohamed, Khairul Nizam Tahar


This paper reviews techniques on identification of road distress. The main key of this paper is to identify road distress and types of road distress. A literature reviews on the types of distress, detection techniques and methods used to identify road distress are presented. Hence, the advantages and disadvantages of current techniques such as laser scan, mobile mapping, stereo camera and uses of UAV used for road distress identification were to be out and gap in their techniques was identified. For many years, broad research has been conducted on road distress detection in order to maintain road surface to a high standard. Quick and accurate detection is required to maintain the road maintenance. A method using 2D images with supported 3D information is one of the best ways to get accurate results on identifying road distress. To keep up with the current technology, UAV with mounted camera is recommended to be used in order to provide quick and effective data capturing.


road distress; detection; literature review; maintenance; UAV

Full Text:



Aldea, E. (2015). Robust crack detection for unmanned aerial vehicles inspection in an a-contrario decision framework. Journal of Electronic Imaging. https://doi.org/10.1117/1.JEI.24.6.061119

Bao, G. (2010). Road Distress Analysis using 2D and 3D Information. International Conference on Recent Advances in 3-D Digital Imaging and Modeling.

Battiato, S., Rizzo, L., Stanco, F., Cafiso, S., & Graziano, A. Di. (2006). Pavement Surface Distress by Using Non-linear Image Analysis Techniques Preprocessing. Minisymposium: Image Analysis Methods for Industrial Applications Pavement, 1–6.

Christodoulou, S. E. (2016). Automated Detection of Pavement Patches utilizing Support Vector Machine Classification. Proceedings of the 18th Mediterranean Electrotechnical Conference, (April), 18–20.

Garbowski, T., & Gajewski, T. (2017). Semi-automatic inspection tool of pavement condition from three-dimensional profile scans. Procedia Engineering, 172, 310–318. https://doi.org/10.1016/j.proeng.2017.02.004

Hsu, C., Chen, C., Lee, C., Huang, S., Invariant, M., & Network, N. (2001). Airport Pavement Distress Image Classification Using Moment Invariant Neural Network. 22nd Asian Conference on Remote Sensing, (November), 5–9.

Huang, J., Liu, W., & Sun, X. (2013). A Pavement Crack Detection Method Combining 2D with 3D Information Based on Dempster-Shafer Theory. Computer-Aided Civil & Infrustructure Engineering.


Kapela, R., Sniatała, P., Błoch, A., & Atrem, S. A. (2015). Asphalt Surfaced Pavement Cracks Detection Based on Histograms of Oriented Gradients. Proceedings of the 22nd International Conference "Mixed Design of Integrated Circuits and Systems.

Laurent, J., Talbot, M., & Doucet, M. (1997). Road surface inspection using laser scanners adapted for the high precision 3D measurements of large flat surfaces. International Conference on Recent Advances in 3-D Digital Imaging and Modeling, 303–310.

Lee, H. D. (2003). A Robust Position Invariant Artificial Neural Network for Digital Pavement Crack Analysis. Annual Meeting CD-ROM, (319).

Lins, R. G., & Givigi, S. N. (2016). Automatic Crack Detection and Measurement Based on Image Analysis. IEEE Transactions On Instrumentation And Measurement, VoL., 65(3), 583–590.

Mathavan, S., Kamal, K., & Rahman, M. (2015). A Review of Three-Dimensional Imaging Technologies for Pavement Distress Detection and Measurements. IEEE, 1–10. Transactions On Intelligent Transportation Systems

Miller, J. S., & Bellinger, W. Y. (2003). Distress Identification Manual for the Long-Term Pavement Performance Program. Publication of US Department of Transport, Federal Highway Administration, (June), 129. https://doi.org/FHWA-RD-03-031

Oliveira, H., & Correia, P. L. (2013). Automatic Road Crack Detection and Characterization. IEEE Transactions On Intelligent Transportation Systems, 14(1), 155–168.

Rashid, Z. Bin, & Gupta, R. (2017). Study of Defects in Flexible Pavement and Its Maintenance. International Journal of Recent Engineering Research and Development (IJRERD), 02(06), 30–37.

Ryu, S., Kim, T., & Kim, Y. (2015). Image-Based Pothole Detection System for ITS Service and Road Management System. Mathematical Problems in Engineering, 2015. https://doi.org/http://dx.doi.org/10.1155/2015/968361

Salari, E., & Bao, G. (2010). Pavement Distress Detection and Classification using Feature Mapping. 2010 IEEE International Conference on Electro/Information Technology.


Siriborvornratanakul, T. (2018). An Automatic Road Distress Visual Inspection System Using an Onboard In-Car Camera. Advances in Multimedia, 2018, 10. https://doi.org/https://doi.org/10.1155/2018/2561953 Research

Wang, H., Xiong, Z., Finn, A. M., & Chaudhry, Z. (2016). A context-driven approach to image-based crack detection. Journal of Machine Vision and Applications, 27(7), 1103–1114. https://doi.org/10.1007/s00138-016-0779-1

Wang, K. (2011). Automated Survey of Pavement Distress based on 2D and 3D Laser Images. Automated Vehicles Symposium.

Zhang, C., & Elaskher, A. (2012). An unmanned aerial vehicle-based imaging system for 3d measurement of unpaved road surface distress. Computer-Aided Civil & Infrustructure Engineering, Volume 27(2), 118–129. https://doi.org/https://doi.org/10.1111/j.1467-8667.2011.00727.x

DOI: https://doi.org/10.25292/atlr.v1i1.28


  • There are currently no refbacks.

Copyright (c) 2018 Advances in Transportation and Logistics Research

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Advances in Transportation and Logistics Research

ISSN: 2622-5778 (online)
Published by: Institut Transportasi dan Logistik Trisakti, Jakarta - Indonesia

ATLR by http://proceedings.itltrisakti.ac.id/index.php/ATLR is licensed under a Creative Commons Attribution 4.0 International License.