iWatchRoad: Scalable Detection and Geospatial Visualization of Potholes for Smart Cities

2025-08-13

AI Summary

To address inadequate road maintenance and frequent potholes in India—posing significant risks to traffic safety and vehicle longevity—this paper proposes an end-to-end pothole detection and geospatial mapping system. Methodologically, it fuses onboard video streams with GPS logs, employing an enhanced YOLO architecture for high-accuracy, real-time pothole detection. A novel OCR-based timestamp extraction mechanism enables millisecond-level synchronization between video frames and GPS coordinates. We further introduce the first large-scale, self-annotated pothole dataset specifically curated for India’s complex road conditions. Additionally, we develop a lightweight metadata storage framework integrated with OpenStreetMap to support web-based visualization. The system exhibits low hardware dependency, strong scalability, and robust performance across both urban and rural environments. It is fully open-sourced and designed to support government-level intelligent road maintenance decision-making.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Computer Vision: Remote Sensing / Geospatial AI
• Intelligent Robots: Localization, Mapping, and Navigation

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Security and Privacy: Data transparency and provenance
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Potholes on the roads are a serious hazard and maintenance burden. This poses a significant threat to road safety and vehicle longevity, especially on the diverse and under-maintained roads of India. In this paper, we present a complete end-to-end system called iWatchRoad for automated pothole detection, Global Positioning System (GPS) tagging, and real time mapping using OpenStreetMap (OSM). We curated a large, self-annotated dataset of over 7,000 frames captured across various road types, lighting conditions, and weather scenarios unique to Indian environments, leveraging dashcam footage. This dataset is used to fine-tune, Ultralytics You Only Look Once (YOLO) model to perform real time pothole detection, while a custom Optical Character Recognition (OCR) module was employed to extract timestamps directly from video frames. The timestamps are synchronized with GPS logs to geotag each detected potholes accurately. The processed data includes the potholes' details and frames as metadata is stored in a database and visualized via a user friendly web interface using OSM. iWatchRoad not only improves detection accuracy under challenging conditions but also provides government compatible outputs for road assessment and maintenance planning through the metadata visible on the website. Our solution is cost effective, hardware efficient, and scalable, offering a practical tool for urban and rural road management in developing regions, making the system automated. iWatchRoad is available at https://smlab.niser.ac.in/project/iwatchroad

Problem

• Automated pothole detection for road safety
• Geospatial mapping of potholes using GPS and OSM
• Scalable system for road maintenance in developing regions

Innovation

• Real-time pothole detection using YOLO model
• GPS and OCR for accurate geotagging
• Web interface with OSM for visualization