Welcome to the Road Segmentation project! This repository contains code for segmenting roads in images using a Fully Convolutional Network (FCN).
The dataset used for this project consists of several road images and their corresponding masks. The images are collected from various sources.
To get started with the project, follow these steps:
- Clone this repository:
git clone https://github.com/your_username/road-segmentation.git - Install the required dependencies:
pip install -r requirements.txt - Download the dataset and place it in the appropriate directory.
The FCN model architecture used for road segmentation includes multiple convolutional and upsampling layers. It leverages the power of deep learning to learn intricate features and perform pixel-wise segmentation.
To train and evaluate the model, follow these steps:
- Open the
Road_Segmentation.ipynbnotebook. - Set the appropriate paths for the dataset and other configurations.
- Run the notebook cells to execute the code.
You can modify the hyperparameters and dataset paths within the notebook according to your needs.
After training the model, it achieves an accuracy of XX% on the test set. The notebook provides visualizations of example predictions.
Road segmentation has various applications, including:
- Autonomous Driving: Accurate road segmentation is crucial for self-driving cars to perceive and navigate the road environment, enabling them to make informed decisions.
- Traffic Analysis: Road segmentation helps in analyzing traffic flow, identifying congested areas, and optimizing traffic management in real-time.
- Infrastructure Planning: Segmenting roads assists in urban planning, determining road conditions, and improving infrastructure development.
Contributions are welcome! If you want to contribute to this project, please open an issue or submit a pull request.
Enjoy road segmentation! 🚗🔍🔍