wambugunm / semantic-segmentation-using-fully-convolutional-networks

In this project, we'll construct a fully convolutional neural network based on the VGGNet-16 architecture to perform semantic segmentation on a video captured from a front facing camera mounted on a vehicle dashboard to identify the drivable surface area.

Semantic Segmentation using Fully Convolutional Networks

In this project, we'll construct a fully convolutional neural network based on the VGGNet-16 architecture to perform semantic segmentation on a video captured from a front facing camera mounted on a vehicle dashboard to identify the drivable surface area. To achieve this task, we'll implement Fully Convolutional Networks for Semantic Segmentation, a paper published by Jonathan Long, Evan Shelhamer and Trevor Darrell from UC Berkeley that adapt contemporary classification network VGGNet into fully convolutional network and transfers its learned representations by fine-tuning to the segmentation task.

The Model

A pre-trained VGG-16 network was converted to a fully convolutional network by converting the final fully connected layer to a 1x1 convolution and setting the depth equal to the number of desired classes (in this case, two: road and not-road).

Results

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.

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Setup

GPU

main.py will check to make sure you are using GPU - if you don't have a GPU on your system, you can use AWS or another cloud computing platform.

Frameworks and Packages

Make sure you have the following is installed:

• Python 3
• TensorFlow
• NumPy
• SciPy

Dataset

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.