Pothole Detection Computer Vision Project
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Intel Unnati Training Program
Pothole Detection in Indian Roads
For reliable and effective transportation networks, it is crucial to maintain the road infrastructure. One of the most prevalent types of road faults that can cause accidents, damage to vehicles, and traffic congestion are potholes. This study focuses on a proposal to create an automated system for accurately and quickly detecting potholes using object detection methods. This system analyzes real-time video footage taken by cameras mounted on cars or other roadside infrastructure using image processing algorithms and machine learning techniques like Roboflow and YOLO object detection algorithms. It then provides timely and accurate information to the relevant authorities in charge of road maintenance, enabling them to take proactive measures to fix potholes and ensure road safety.
Datasets Used:
--> Indian Driving Dataset (IDD) --> Kaggle: Annotated Potholes Image Dataset --> Research Paper: An annotated water-filled, and dry potholes dataset for deep learning applications --> Roboflow Universe: Drains test Computer Vision Project --> Roboflow Universe: yolo Computer Vision Project
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Cite This Project
If you use this dataset in a research paper, please cite it using the following BibTeX:
@misc{
pothole-detection-bqu6s_dataset,
title = { Pothole Detection Dataset },
type = { Open Source Dataset },
author = { Intel Unnati Training Program },
howpublished = { \url{ https://universe.roboflow.com/intel-unnati-training-program/pothole-detection-bqu6s } },
url = { https://universe.roboflow.com/intel-unnati-training-program/pothole-detection-bqu6s },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2023 },
month = { aug },
note = { visited on 2024-11-24 },
}