What question did this study set out to answer?

The aim is to develop a low-cost, automated system for road-surface defect detection using dashcam footage.

Real-Time Road Condition Detection and Mapping Using YOLOv11 and Built-In CarDashcam

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Randomized trial demonstrates real-time pavement monitoring using YOLOv11 in a mobile framework, suggesting efficient maintenance workflows.

The aim is to develop a low-cost, automated system for road-surface defect detection using dashcam footage.
Created a manually annotated dataset at the University of the Fraser Valley campus.
Utilized YOLOv11 for detecting and classifying road surface conditions, applying augmentation techniques to improve model performance.
Implemented geolocation tagging for mapping and visualizing spatial data of road conditions.
Achieved a mean average precision above 90% for all tested categories of road surface conditions.
Demonstrated a scalable and practical approach for municipalities seeking to modernize road assessments.
Future work includes developing a dashboard for real-time monitoring and expanding datasets to improve accuracy.

Josan et al. (2026) studied this question.

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Randomized trial demonstrates real-time pavement monitoring using YOLOv11 in a mobile framework, suggesting efficient maintenance workflows.