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This is a Experimental Study study.

September 19, 2025Journal of Advanced College of Engineering and ManagementOpen Access

YOLOv11-s-Based Pothole Detection Model with Integrated Traffic Data for Maintenance Decision Support

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Authors

SSS. SivanrajUniversity of Moratuwa DUD.N.L.S UduwageUniversity of Moratuwa RSR.K. ShiwakotiTribhuvan University

Discussion

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Implication

Automated pothole detection model shows strong precision in detecting road issues, suggesting enhanced repair prioritization through integrated traffic data.

Key Points

The model achieved a precision of 0.851, improving efficiency in pothole detection processes.
It integrates traffic data, allowing maintenance authorities to prioritize repairs based on traffic volume.
Pothole characteristics can enhance decision-making for road repair prioritization in the model's future versions.
The dataset utilized for training was obtained from the RoboFlow platform and validated with Sri Lankan road images.

Cite This Study

Sivanraj et al. (2025) studied this question.

synapsesocial.com/papers/68d46cd731b076d99fa694ec https://doi.org/10.3126/jacem.v11i1.84529

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Implication

Automated pothole detection model shows strong precision in detecting road issues, suggesting enhanced repair prioritization through integrated traffic data.

YOLOv11-s-Based Pothole Detection Model with Integrated Traffic Data for Maintenance Decision Support

Discussion

Member takes

Implication

Key Points

Cite This Study

YOLOv11-s-Based Pothole Detection Model with Integrated Traffic Data for Maintenance Decision Support

Discussion

Member takes

Implication

Key Points

Cite This Study

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