\section*{Abstract}
\addcontentsline{toc}{chapter}{Abstract}

\ac{YOLO} is a widespread neural network architecture that has been used in many applications, especially since the release of the third version.
Meanwhile, implementations for different frameworks exist.
\ac{YOLO} is characterized by the high accuracy and speed with which it can recognize objects in an image.
Particularly in the field of autonomous driving, \ac{YOLO} has gained tremendous popularity.
The recognition of traffic lights in the successful \acs{FU} project \q{autonomos} still requires human assistance in some cases.

In this master thesis, a different approach is pursued.
The traffic light recognition shall be automated with the help of the network YOLOv3.
It will be investigated which special requirements the traffic light recognition has and how a neural network can fulfill these specifications.
Afterward, YOLOv3 will be applied to the problem concerning the requirements.
In individual experiments, it is investigated which hyperparameters are the most suitable for the network.

Several successes were achieved in the thesis.
The traffic light recognition with this network is better than using the already trained weightings on the class traffic lights.
Further improvements have been made or suggested, which have become evident by using YOLO. Nevertheless, there are still many possibilities for improvement based on the findings of this work.
At the end of the thesis, some of them are explained in more detail.
