  Deep learning has been successfully applied to several problems related to
autonomous driving. Often, these solutions rely on large networks that require
databases of real image samples of the problem (i.e., real world) for proper
training. The acquisition of such real-world data sets is not always possible
in the autonomous driving context, and sometimes their annotation is not
feasible (e.g., takes too long or is too expensive). Moreover, in many tasks,
there is an intrinsic data imbalance that most learning-based methods struggle
to cope with. It turns out that traffic sign detection is a problem in which
these three issues are seen altogether. In this work, we propose a novel
database generation method that requires only (i) arbitrary natural images,
i.e., requires no real image from the domain of interest, and (ii) templates of
the traffic signs, i.e., templates synthetically created to illustrate the
appearance of the category of a traffic sign. The effortlessly generated
training database is shown to be effective for the training of a deep detector
(such as Faster R-CNN) on German traffic signs, achieving 95.66% of mAP on
average. In addition, the proposed method is able to detect traffic signs with
an average precision, recall and F1-score of about 94%, 91% and 93%,
respectively. The experiments surprisingly show that detectors can be trained
with simple data generation methods and without problem domain data for the
background, which is in the opposite direction of the common sense for deep
learning.

Computer Vision and Pattern Recognition