Semantic segmentation attempts to partition the image into semantically meaningful parts, and to classify each part into one of the pre-determined classes.
In this project the goal is to label the pixels of a road in images using a Fully Convolutional Network (FCN) i.e. to train segmentation networks, which paint each pixel of the image a different color, based on its class. Use segmented images to find free space on the road.
Results:
Make sure you have the following is installed:
Download the Kitti Road dataset from here. Extract the dataset in the data folder. This will create the folder data_road with all the training a test images.
Run the following command to run the project:
python main.py
Note If running this in Jupyter Notebook system messages, such as those regarding test status, may appear in the terminal rather than the notebook.