  Deep neural networks come as an effective solution to many problems
associated with autonomous driving. By providing real image samples with
traffic context to the network, the model learns to detect and classify
elements of interest, such as pedestrians, traffic signs, and traffic lights.
However, acquiring and annotating real data can be extremely costly in terms of
time and effort. In this context, we propose a method to generate artificial
traffic-related training data for deep traffic light detectors. This data is
generated using basic non-realistic computer graphics to blend fake traffic
scenes on top of arbitrary image backgrounds that are not related to the
traffic domain. Thus, a large amount of training data can be generated without
annotation efforts. Furthermore, it also tackles the intrinsic data imbalance
problem in traffic light datasets, caused mainly by the low amount of samples
of the yellow state. Experiments show that it is possible to achieve results
comparable to those obtained with real training data from the problem domain,
yielding an average mAP and an average F1-score which are each nearly 4 p.p.
higher than the respective metrics obtained with a real-world reference model.
