Integrating machine vision for road condition assessment: A case of the City of Johannesburg

Abstract

Background: The City of Johannesburg (CoJ) is grappling with deteriorating road infrastructure marked by potholes, surface cracks, faded markings and blocked drainage. Traditional visual inspections are reactive, subjective and labour-intensive.
Aim: This study explores the use of machine vision technologies to enhance road condition assessment in CoJ. The aim is also to analyse regional road damage patterns; evaluate the effectiveness of machine vision in detecting and classifying defects; develop and validate an automated framework for defect detection and maintenance planning and recommend adoption of advanced technologies to improve inspection accuracy.
Setting: The study was conducted within CoJ Metropolitan Municipality, South Africa.
Methods: A mixed methods approach was used using Chi-square tests, analysis of variation (ANOVA), correlation and structural equation modelling (SEM), complemented by qualitative insights.
Results: Significant regional variations were observed. Potholes were prevalent in the Regions A, E and G, while surface cracks dominated in the Regions B and C. A machine vision framework was developed to automate road damage detection.
Conclusion: The study concludes that machine vision improves the accuracy and efficiency of road condition assessments.
Contribution: The study delivers a scalable framework for municipal road management and informs smart city policy and investment.