  Automatic detection and recognition of traffic signs plays a crucial role in
management of the traffic-sign inventory. It provides accurate and timely way
to manage traffic-sign inventory with a minimal human effort. In the computer
vision community the recognition and detection of traffic signs is a
well-researched problem. A vast majority of existing approaches perform well on
traffic signs needed for advanced drivers-assistance and autonomous systems.
However, this represents a relatively small number of all traffic signs (around
50 categories out of several hundred) and performance on the remaining set of
traffic signs, which are required to eliminate the manual labor in traffic-sign
inventory management, remains an open question. In this paper, we address the
issue of detecting and recognizing a large number of traffic-sign categories
suitable for automating traffic-sign inventory management. We adopt a
convolutional neural network (CNN) approach, the Mask R-CNN, to address the
full pipeline of detection and recognition with automatic end-to-end learning.
We propose several improvements that are evaluated on the detection of traffic
signs and result in an improved overall performance. This approach is applied
to detection of 200 traffic-sign categories represented in our novel dataset.
Results are reported on highly challenging traffic-sign categories that have
not yet been considered in previous works. We provide comprehensive analysis of
the deep learning method for the detection of traffic signs with large
intra-category appearance variation and show below 3% error rates with the
proposed approach, which is sufficient for deployment in practical applications
of traffic-sign inventory management.
