Road Detection and Recognition from Monocular Images Using Neural Networks
Name
Leonid Dashko
Abstract
Road recognition is one of the important aspects in Autonomous Navigation Systems. These systems help to navigate the autonomous vehicle and robot on the ground. Further, road detection is useful in related sub-tasks such as finding valid road path where the robot/vehicle can go, for supportive driverless vehicles, preventing the collision with the obstacle, object detection on the road, and others.
The goal of this thesis is to examine existing road detection and recognition techniques and propose an alternative solution for road classification and detection task.
Our contribution consists of several parts. Firstly, we released the road images dataset with approximately 5,300 unlabeled road images. Secondly, we summarized the information about the existing road images datasets. Thirdly, we proposed the convolutional LeNet-5-based neural network for the road image classification for various environments. Finally, our FCN-8-based model for pixel-wise image recognition has been presented.
The goal of this thesis is to examine existing road detection and recognition techniques and propose an alternative solution for road classification and detection task.
Our contribution consists of several parts. Firstly, we released the road images dataset with approximately 5,300 unlabeled road images. Secondly, we summarized the information about the existing road images datasets. Thirdly, we proposed the convolutional LeNet-5-based neural network for the road image classification for various environments. Finally, our FCN-8-based model for pixel-wise image recognition has been presented.
Graduation Thesis language
English
Graduation Thesis type
Master - Computer Science
Supervisor(s)
Amnir Hadachi
Defence year
2018