https://drive.google.com/file/d/1omhTFMfhzgu4le86FKLhsIaxe3-heUHk/view?usp=sharing
https://trinit-abc-c.github.io/Road_Damage_Detection/
This repository contains code and resources for a road damage detection model using YOLOv5. The model is trained to identify potholes and cracks in road images, contributing to infrastructure maintenance and safety efforts.
The model is designed to detect and localize potholes and cracks in road images.
The model is implemented using YOLOv5, a state-of-the-art object detection framework.
Road Repair Planning: Governments and municipalities can use the model to identify areas with significant road damage, helping prioritize and plan road repair and maintenance projects efficiently.
Accident Prevention: Timely identification of potholes and cracks can contribute to accident prevention by addressing potential hazards on the road, improving overall public safety. Smart Cities:
Integrating the model with smart city infrastructure can enhance traffic management systems by providing real-time information on road conditions and potential obstacles.
Long-Term Planning: The model's insights can assist in long-term asset management by predicting the lifespan of road surfaces, aiding in budget allocation and resource planning.
(This Project was developed within a Hackathon conducted by Nit Trichy named TriNIT Hackathon 3.0 from 8th March 2024 to 10th March 2024)
[email protected] / [email protected] / [email protected]
Happy detecting! 🚗🛣️