\section{Used Datasets}\label{sec:used-datasets}
A central point in the development of neural networks is the use of large and precise datasets.
The creation of datasets for images is very time\textendash  and resource\textendash consuming because these datasets have to be labeled individually.
Since there is intensive research in autonomous driving, many large and information\textendash rich datasets are available.

\subsection{Cityscapes Dataset}\label{subsec:cityscapes-dataset}
A collaboration between Daimler AG, the Max Planck Institute, and Darmstadt Technical University created the Cityscapes dataset in 2016.\cite{DBLP:journals/corr/CordtsORREBFRS16}
Many previously created records contain only a few records, a single city, a few classes, or only bounding box annotations.
The Cityscapes dataset covers over 50 cities in Germany and neighboring countries in three seasons.
The available images have a size of 1024 x 2048 pixels.
All images with its 30 classes of the dataset are labeled by hand.\cite{cityscapesYoutube}

The used version of the dataset contains 41 cities with 22973 images, which are divided accordingly into training and test images.
The dataset is designed for the segmentation of various objects and not specifically for the detection of traffic lights.
Therefore the dataset contains only 25077 traffic lights in 6734 images.

\subsection{DriveU Traffic Light Dataset (DTLD)}\label{subsec:driveu-traffic-light-dataset-(dtld)}
Traffic lights can appear in many different appearances.
The shape and size often vary.
A traffic light becomes enlarged when approaching it, and the viewing angle can change due to curves or bending.
Traffic signals are often partially occluded by trees.
An essential part is also the currently displayed color of the signal.
If individual trips are the basis of the datasets, the latter can have a bias.
Predominantly green, but also red traffic lights can be recognized preferentially.
A traffic light in the yellow phase may not be recognized because it appears comparingly less.
Balanced datasets are especially important at this point.

If a traffic light is still more than 100 meters away, it is only 1.7px wide, under the condition of a width of 20 centimeters (as specified in the European standard EN12368)\cite{anforderungenLichtsignalanlagen}, a camera viewing angle of 100 degrees on an image with a resolution of two megapixels, and thus hardly recognizable by a neural network.
To make matters worse, the image is often scaled down for faster training and detection.
The actual traffic light is now no longer visible on the image \textemdash  the network detects the light only by its context.
All the more important is a large and variable dataset.

\begin{figure}[!htbp]
    \vspace{0cm}
    \minipage{0.0\textwidth}
    \endminipage\hfill
    \minipage{0.65\textwidth}
    \includegraphics[width=\linewidth]{../data/traffic_light_on_plane.png}
    \endminipage\hfill
    \minipage{0.0\textwidth}
    \endminipage
    \caption[Traffic Light Size Within Image]{\small If the distance d between the car and the traffic light equals 100m and the camera viewing angle $\Theta$ is 100 degrees, the plane p in that distance has a width of 238 meters.
    The traffic light is 20cm wide, as states in EN12368\cite{anforderungenLichtsignalanlagen}.
    That results in a share of $0.08 \%$.
    If the captured image is 2048px wide, the width of the traffic light corresponds to $1.7$.}
    \label{fig:traffic_light_size}       %fig:ID
\end{figure}



Cooperation between the University of Ulm and Daimler AG developed the DriveU Traffic Light Dataset.
On a total of 43875 pictures, it contains 232039 traffic lights taken in eleven different German cities.
This dataset is specialized for traffic lights, and the labels also include differentiation of the current status of the indicator.
That includes the number of existing light bulbs, the direction indicator, and the current light signal.
This differentiation is not considered at first.
Traffic lights with more or less than three display elements - the typical green, yellow, red - are excluded.

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