Smart Vehicle-Mounted Pothole Detection and Mapping System with Real-Time Database Integration

The Smart Vehicle-Mounted Pothole Detection and Mapping System enhances road safety and maintenance by utilizing the YOLOv4- Tiny algorithm for real-time pothole detection via dash cam footage. Detected potholes are logged with GPS coordinates and transmitted to a Firebase database, which continuously updates a map of road conditions. A mobile app provides drivers with alerts when approaching a pothole within a user-defined radius, aiding in safe driving and helping authorities prioritize road repairs. The system was tested under various road conditions, achieving a mean Average Precision (mAP) of 80.7% for pothole detection. It was evaluated using different pothole sizes and environments, and the YOLOv4-Tiny model showed reliable detection with minimal false positives. The real-time notification feature effectively alerted drivers, while the cloud-based database provided accurate, actionable data for road maintenance. The system demonstrated efficient performance and scalability, making it a practical solution for improving both road safety and infrastructure upkeep.

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Publication Details

Published
2024-12-12
DOI
https://doi.org/10.1109/icuis64676.2024.10867220
Primary Topic
Infrastructure Maintenance and Monitoring
Type
article
Field-Weighted Citation Impact
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article

Smart Vehicle-Mounted Pothole Detection and Mapping System with Real-Time Database Integration

SujayKumar Reddy M, K. Jaspinl, Sethulakshmanan SP
Infrastructure Maintenance and Monitoring
article

Smart Vehicle-Mounted Pothole Detection and Mapping System with Real-Time Database Integration

SujayKumar Reddy M, K. Jaspinl, Sethulakshmanan SP
article en

Abstract

The Smart Vehicle-Mounted Pothole Detection and Mapping System enhances road safety and maintenance by utilizing the YOLOv4- Tiny algorithm for real-time pothole detection via dash cam footage. Detected potholes are logged with GPS coordinates and transmitted to a Firebase database, which continuously updates a map of road conditions. A mobile app provides drivers with alerts when approaching a pothole within a user-defined radius, aiding in safe driving and helping authorities prioritize road repairs. The system was tested under various road conditions, achieving a mean Average Precision (mAP) of 80.7% for pothole detection. It was evaluated using different pothole sizes and environments, and the YOLOv4-Tiny model showed reliable detection with minimal false positives. The real-time notification feature effectively alerted drivers, while the cloud-based database provided accurate, actionable data for road maintenance. The system demonstrated efficient performance and scalability, making it a practical solution for improving both road safety and infrastructure upkeep.

St. Joseph's Institute of Technology (IN)
Affordable and clean energy
Openalex Percentile: Top 69%
Infrastructure Maintenance and Monitoring
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