What question did this study set out to answer?

The research aims to develop an AI framework for automated pavement condition assessment using YOLOv8 and validate it against manual inspections and PASER scores.

June 20, 2026Open Access

AI-Driven Pavement Condition Assessment from Dash-Cam Imagery: A Comparative Analysis of YOLOv8-Based PCI Estimation, Manual Inspections, and Automated PASER Ratings in Urban Networks

Key Points

The research aims to develop an AI framework for automated pavement condition assessment using YOLOv8 and validate it against manual inspections and PASER scores.
Developed a YOLOv8 object-detection model implemented in Python on the N-RDD2024 dataset.
Collected dash-cam imagery from 6.3 km of urban flexible pavements.
Performed statistical validation of automated PCI values against manual inspections and PASER ratings across 23 pavement sections.
Automated PCI values showed an R-squared of 0.925 with manual inspections, indicating strong correlation.
Automated model achieved 84.6% accuracy, precision of 89.6%, and recall of 86.3%.
PASER ratings generally overestimated pavement conditions compared to manual assessments.

Abstract

This study presents an AI-enabled framework for automated pavement condition assessment in urban environments by integrating YOLOv8-based distress detection, computational Pavement Condition Index (PCI) estimation, and comparative validation against manual PCI inspections and Pavement Surface Evaluation and Rating (PASER) scores. A YOLOv8 object-detection model, implemented in Python and trained on the publicly available N-RDD2024 dataset, was developed to identify longitudinal cracks, transverse cracks, alligator cracking, and potholes. The model achieved an accuracy of 84.6%, a precision of 89.6%, and a recall of 86.3%, demonstrating robust detection performance under heterogeneous environmental conditions. Dash-cam imagery collected along 6.3 km of urban flexible pavements was processed through an automated workflow that detects pavement distresses, estimates their severity and extent, and computes PCI values according to ASTM D6433-20 procedures. Automated PCI values were compared with manual PCI inspections and PASER ratings generated by the Blyncsy platform across 23 pavement sections. Statistical validation between automated and manual PCI assessments returned an R-squared of 0.925, a Pearson correlation coefficient of 0.962, a Spearman correlation coefficient of 0.955, a Mean Absolute Error of 5.0 PCI points, and a Root Mean Square Error of 6.1 PCI points. Compared with the proposed framework, PASER ratings exhibited lower agreement with manual PCI assessments and generally overestimated the pavement condition. The results demonstrate the potential of low-cost AI-based systems for large-scale pavement monitoring. Nevertheless, performance degradation was observed under challenging environmental conditions and in heavily deteriorated sections, highlighting the need for improved distress quantification, dataset balancing, and multimodal sensing integration.

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Cite This Study

Serrone et al. (Thu,) studied this question.

synapsesocial.com/papers/6a36306fdb0793dc1a537a4b https://doi.org/https://doi.org/10.3390/infrastructures11060207

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