Optimizing Road Pavement Assessment Using Advanced Image Processing Techniques

Amir Shtayat (), Mohammed T. Obaidat, Bara’ Al-Mistarehi, Ahmad Bader, Sara Moridpour () and Saja Alahmad
Additional contact information
Amir Shtayat: Department of City Planning and Design, Jordan University of Science and Technology, Irbid 22110, Jordan
Mohammed T. Obaidat: Department of Civil Engineering, Jordan University of Science and Technology, Irbid 22110, Jordan
Bara’ Al-Mistarehi: Department of Civil Engineering, Jordan University of Science and Technology, Irbid 22110, Jordan
Ahmad Bader: Communication and Computer Department, Faculty of Engineering, Jadara University, Irbid 21110, Jordan
Sara Moridpour: School of Engineering, RMIT University, Melbourne 3001, Australia
Saja Alahmad: Department of City Planning and Design, Jordan University of Science and Technology, Irbid 22110, Jordan

Sustainability, 2025, vol. 17, issue 6, 1-22

Abstract: The swift advancement in monitoring and evaluation systems for road pavement conditions highlights the crucial role that this field plays in ensuring the sustainability of roads. This, in turn, contributes to the growth and prosperity of nations and enables users to enjoy the highest levels of luxury and comfort. Despite numerous studies and ongoing research, finding the most precise and efficient monitoring systems to determine the type and severity of road defects, their causes, and appropriate treatments remains a challenge. This study proposes a system that employs a camera to create an application capable of evaluating road conditions with ease by taking images while driving over the road. Based on the results, the application was accurate in identifying road defects of different severity within the same category. The proposed method was compared to the Pavement Condition Index (PCI) method, and a significant match was found in determining the type and severity of each defect on the selected road sections. More clearly, the overall accuracy of detecting and classifying block cracks, alligator cracks, longitudinal cracks, and potholes was significant for detecting and classifying the patches.

Keywords: pavement distress; image processing; PCI; pavement defects; prediction (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/17/6/2473/pdf (application/pdf)
https://www.mdpi.com/2071-1050/17/6/2473/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:17:y:2025:i:6:p:2473-:d:1610195

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().