CarND-RoadSegmentation

The main goal of the project is to train an artificial neural network for semantic segmentation of a video from a front-facing camera on a car in order to mark road pixels using Tensorflow.

Results

KITTI Road segmentation (main task of the project):

Content of this repo

• Roadseg_Kitti_FCN.ipynb - Jupyter notebook with the main code for the project, using FCN architecture.
• helper.py - python program for images pre- and post- processing.
• runs - directory with processed images
• models - directory with trained models

Note: The repository does not contain any training images. You have to download the image datasetsplace them in appropriate directories on your own.

Architecture

A Fully Convolutional Network (FCN-8 Architecture developed at Berkeley, see paper ) was applied for the project. It uses VGG16 pretrained on ImageNet as an encoder. Decoder is used to upsample features, extracted by the VGG16 model, to the original image size. The decoder is based on transposed convolution layers.

The goal is to assign each pixel of the input image to the appropriate class (road, backgroung, etc). So, it is a classification problem, that is why, cross entropy loss was applied.

Setup

Hyperparameters were chosen by the try-and-error process. Adam optimizer was used as a well-established optimizer. Weights were initialized by a random normal initializer. Some benefits of L2 weights regularization were observed, therefore, it was applied in order to reduce grainy edges of masks.

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Semantic Segmentation

Introduction

In this project, you'll label the pixels of a road in images using a Fully Convolutional Network (FCN).

Setup

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.

Start

Implement

Implement the code in the main.py module indicated by the "TODO" comments. The comments indicated with "OPTIONAL" tag are not required to complete.

Run

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.

Submission

1. Ensure you've passed all the unit tests.
2. Ensure you pass all points on the rubric.
3. Submit the following in a zip file.

• helper.py
• main.py
• project_tests.py
• Newest inference images from runs folder (all images from the most recent run)

Tips

• The link for the frozen VGG16 model is hardcoded into helper.py. The model can be found here
• The model is not vanilla VGG16, but a fully convolutional version, which already contains the 1x1 convolutions to replace the fully connected layers. Please see this forum post for more information. A summary of additional points, follow.
• The original FCN-8s was trained in stages. The authors later uploaded a version that was trained all at once to their GitHub repo. The version in the GitHub repo has one important difference: The outputs of pooling layers 3 and 4 are scaled before they are fed into the 1x1 convolutions. As a result, some students have found that the model learns much better with the scaling layers included. The model may not converge substantially faster, but may reach a higher IoU and accuracy.
• When adding l2-regularization, setting a regularizer in the arguments of the tf.layers is not enough. Regularization loss terms must be manually added to your loss function. otherwise regularization is not implemented.

Using GitHub and Creating Effective READMEs

If you are unfamiliar with GitHub , Udacity has a brief GitHub tutorial to get you started. Udacity also provides a more detailed free course on git and GitHub.

To learn about REAMDE files and Markdown, Udacity provides a free course on READMEs, as well.

GitHub also provides a tutorial about creating Markdown files.