Potholes pose a significant challenge to road safety and vehicle maintenance. This project addresses the issue by developing an efficient pothole detection and mitigation system. The solution leverages advanced computer vision techniques and machine learning algorithms to identify potholes and provides actionable insights for timely repairs.
Objectives
Enhance road safety by identifying and addressing potholes early.
Minimize vehicle damage caused by potholes.
Provide a cost-effective and scalable solution for municipalities and road management authorities.
Methodology
Data Collection:
Images and videos of roads are collected using drones and vehicle-mounted cameras.
Data is annotated to highlight potholes for training purposes.
Model Training:
A convolutional neural network (CNN) is trained on the annotated dataset.
The model is optimized for accuracy and speed to ensure real-time detection.
Mitigation Strategies:
Collaboration with local authorities for prioritizing repairs.
Analysis of recurring patterns to identify root causes.
Key Features
Real-Time Detection: Utilizes edge computing to identify potholes instantly.
Actionable Insights: Generates detailed reports for authorities to streamline repair processes.
Results
Improved detection accuracy of 90% during field tests.
Significant reduction in road accidents and vehicle damage in test regions.
Positive feedback from local authorities and road users.
Challenges and Future Work
Challenges
The model is integrated into a mobile application for on-the-go pothole detection.
GPS integration for precise location tagging of identified potholes.
Limited dataset diversity leading to edge-case inaccuracies.
Hardware constraints for edge computing in low-resource settings.
Future Work
Expand the dataset to include diverse road conditions and regions.
Develop predictive models to prevent pothole formation.
Explore automated repair mechanisms using robotics.
Conclusion
The Pothole Detection and Mitigation Project demonstrates the potential of technology to transform road safety and infrastructure management. By combining cutting-edge AI with practical deployment strategies, this project provides a scalable solution to a pervasive problem.
