tayebiarasteh / lane-detection

Weakly-supervised road-lane markings detection for autonomous driving, mitigating the lack of training data

Weakly-supervised road-lane markings detection for autonomous driving, mitigating the lack of training data

By Soroosh Tayebi Arasteh

This project was part of the Labopraktikum Machine Learning in Signal Processing (SS 2019, Prof. Dr. Veniamin Morgenshtern) offered by the Chair of Multimedia Communications and Signal Processing (LMS) of the Electrical Engineering Department at University of Erlangen-Nuremberg (FAU).

Introduction

In autonomous driving, among other things, the car needs to steer itself to keep driving in its own lane. To accomplish this, the central problem is to detect the road-lane markings. These are the white solid or dashed lines that are drawn on each side of the lane. The standard modern approach to solve this type of problems is to take a large dataset of labeled examples and train a deep neural network model to accomplish the task. This is how car and pedestrian detection algorithms are developed. The difficulty with the road-lane markings is that there is no labeled dataset of them and creating such dataset would cost millions of dollars.

Goal of this project

To solve the afformentioned problem, using a dataset of simulated images intermixed with a dataset of real images that contain no road.

Installation of requirements

The software is developed in Python 2.7 using Jupyter Notebook development kit. For the deep learning, the PyTorch 0.4 framework is used.

All Python modules required for the software can be installed from requirements in two stages:

1. Create a conda environment and install all modules mentioned in the spec_file.txt:

$ conda create --name soroosh --file spec_file.txt

2. Install the remaining dependencies from requirements.txt using pip:

$ pip install -r requirements.txt

Code structure

1. Everything can be ran from ./Train.ipynb for training, and from ./Predict.ipynb for testing.

• Set the hyper-parameters and model parameters here.
• The data preprocessing parameters and directories can be modified from ./configs/config.json.
• Also, you should first choose an experiment name (if you are starting a new experiment) for training, in which all the evaluation and loss value statistics, tensorboard events, and model & checkpoints will be stored. Furthermore, a config.json file will be created for each experiment storing all the information needed.
• For testing, just load the experiment which its model you need.

2. The rest of the files:

• ./models/ directory contains the model architecture.
• ./simulator/ directory contains the files which simulate cartoon-like training images.
• ./Training.py contains the training and validation processes.
• ./Prediction.py contains the testing process.
• ./data/Img_dataset.py and ./data/Video_dataset.py contain data handlers.

Proposed architecture

The architecture used in this software is a variant of the U-Net architecture.

Training

Training data

Intermixed dataset of:

1. 258 real images which contain no lane, from folder True_Negative_Dataset_Train.
2. 1000 simulated cartoon-like images of road containing lanes. You can generate as much simulate images as you want from simulator. Done in Python, you can find the codes of simulation in simulator. An example image is shown below.

Training details

The network is trained with the following details:

Name	Value
Learning rate	0.01 (fixed)
Number of epochs	13
Loss function	CrossEntropyLoss
Optimizer	SGD with a batch size of 1
Lane to no-lane weight ratio	3

No data augmentation is used on the True_Negative_Dataset_Train dataset. ReLU is used as the activatoin function and batch normalization is used.

Results

You can see the input video here and the output video here.

As you can see in the video, predictions are pretty acceptable considering that we have given no single real TP image as the input to the network. Also, the simulated images are very simple. Simulating images more close to reality would lead to better performance though.