  Object detection and classification of traffic signs in street-view imagery
is an essential element for asset management, map making and autonomous
driving. However, some traffic signs occur rarely and consequently, they are
difficult to recognize automatically. To improve the detection and
classification rates, we propose to generate images of traffic signs, which are
then used to train a detector/classifier. In this research, we present an
end-to-end framework that generates a realistic image of a traffic sign from a
given image of a traffic sign and a pictogram of the target class. We propose a
residual attention mechanism with dense concatenation called Dense Residual
Attention, that preserves the background information while transferring the
object information. We also propose to utilize multi-scale discriminators, so
that the smaller scales of the output guide the higher resolution output. We
have performed detection and classification tests across a large number of
traffic sign classes, by training the detector using the combination of real
and generated data. The newly trained model reduces the number of false
positives by 1.2 - 1.5% at 99% recall in the detection tests and an absolute
improvement of 4.65% (top-1 accuracy) in the classification tests.
