| Issue |
EPJ Web Conf.
Volume 343, 2025
1st International Conference on Advances and Innovations in Mechanical, Aerospace, and Civil Engineering (AIMACE-2025)
|
|
|---|---|---|
| Article Number | 05012 | |
| Number of page(s) | 9 | |
| Section | Artificial Intelligence & Machine Learning in Engineering | |
| DOI | https://doi.org/10.1051/epjconf/202534305012 | |
| Published online | 19 December 2025 | |
https://doi.org/10.1051/epjconf/202534305012
Smart Pothole Detection and Reporting System: Real-Time Localization and Automated Alerts for Efficient Road Maintenance
1 Department of Computer Science and Engineering, Sandip University, Nashik, Maharashtra, India 422213
2 Department of Computer Science and Engineering, Sandip University, Nashik, Maharashtra, India 422213
Published online: 19 December 2025
Potholes are a significant hazard and cause terrible damages to cars, endangering drivers and pedestrians almost involved. The existing system of pothole detection and reporting has been slow and inefficient and, given the human factors involved, also error-prone, leading to generally behind schedule responses if at all, with an added potential for injuries. With the advance of laptop imaginative and prescient, machine studying, and geolocation, this paper proposes a real-time pothole detection and reporting device which has the capability to automatically identify large potholes on the road. The gadget not only ”cheap” gets a high accuracy detection of big potholes, but also marks their exact location via GPS and displays it on a virtual map. These potholes are automatically flagged to appropriate authorities including Public Works Department (PWD), Road Transport Office (RTO), road safety engineers, and local authorities bodies. It allows for instant motion and maintenance. The solution provided by this essay is to enhance road safety, streamline renovation strategies and minimize the impact of potholes on street customers. The suggested device used the statistics of real time collection and reporting to develop an exceptionally green, scalable and responsive to street renovation.
Key words: Pothole detection / real-time monitoring / computer vision / machine learning / geolocation / road safety / automated reporting
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.

