The experiments show that traffic light recognition with an optimized implementation of YOLOv3 is adequate.
YOLOv3 has many hyper\textendash parameters, which can and must be adapted.
The advantage of the algorithm is the flexibility in rescaling.
This can be dynamically adjusted depending on the situation and resource availability.
Especially in autonomous cars, the availability of resources like energy and spatiality is limited.\myref{subsubsec:portability}

YOLOv3 is, according to the results of the experiments, preferable to YOLOv3\textendash Tiny.
Although the Tiny variant allows up to 8 times more frames per second on a system with an SSD, the accuracy is also significantly lower.
The additional layer in the Feature Pyramid makes YOLOv3 more suitable for small objects.
For traffic light detection, an average of 20 \ac{FPS}, which YOLOv3 offers, is sufficient.

\section{Perspective}\label{sec:perspective}

The currently used implementation uses the anchors suggested by the paper of YOLOv3.
These have been developed by analyzing the COCO dataset with the 80 standard classes.
A kmeans algorithm has been run over all bounding boxes of all objects to find the nine best\textendash distributed anchors.
The class \q{traffic lights} is exceptional and has a substantially different distribution than the 80 classes with very heterogeneous objects.
The anchors should be recalculated with the help of a kmeans so that the traffic lights are distributed more evenly over the anchors again in order to specialize better.

To not be dependent on a fixed size even after the training, the network should use the possibility of flexible scaling and change the image size every ten batches during the training, as suggested in the paper.\cite{DBLP:journals/corr/abs-1804-02767}
Tensorflow does not offer the possibility to vary the input size of a model during training.
The model has to get saved and reloaded with changed input size.

With the existing data, it is possible to distinguish traffic lights in color.
For this purpose the size of the traffic lights and the identifiability of the traffic light on the image is crucial.
Either decide everything at a glance, which is the principle of YOLO, or prepare the found traffic lights again to determine the class.
The advantage of the second method is that searching through the entire image is faster because only a low resolution can be used.

YOLOv4 is relatively new when the master thesis has been started.
This project, which was published under the supervision of a third party, has addressed this problem in the further development of YOLOv3.
YOLO has problems with small objects.
Predominantly these are targeted with the new version, and the recognition improves.\cite{DBLP:journals/corr/abs-2004-10934}
The change between YOLOv3 and YOLOv4 is substantial.

The use of recurrent layers tends to show good results.
On the one hand, annotated videos allow introducing a hard metric for the precision of bounding boxes in videos.\cite{Mao_2019_ICCV}
More high\textendash grade teacher learning can be used without emulating the frame with a current label before.
Viewing the last n frames makes the network more stable against short dropouts.\cite{ning2016spatially}
