  In recent years, event cameras (DVS - Dynamic Vision Sensors) have been used
in vision systems as an alternative or supplement to traditional cameras. They
are characterised by high dynamic range, high temporal resolution, low latency,
and reliable performance in limited lighting conditions -- parameters that are
particularly important in the context of advanced driver assistance systems
(ADAS) and self-driving cars. In this work, we test whether these rather novel
sensors can be applied to the popular task of traffic sign detection. To this
end, we analyse different representations of the event data: event frame, event
frequency, and the exponentially decaying time surface, and apply video frame
reconstruction using a deep neural network called FireNet. We use the deep
convolutional neural network YOLOv4 as a detector. For particular
representations, we obtain a detection accuracy in the range of 86.9-88.9%
<EMAIL_ADDRESS>The use of a fusion of the considered representations allows us to
obtain a detector with higher accuracy of 89.9%<EMAIL_ADDRESS>In comparison, the
detector for the frames reconstructed with FireNet is characterised by an
accuracy of 72.67%<EMAIL_ADDRESS>The results obtained illustrate the potential of
event cameras in automotive applications, either as standalone sensors or in
close cooperation with typical frame-based cameras.
