  The paper presents a Traffic Sign Recognition (TSR) system, which can fast
and accurately recognize traffic signs of different sizes in images. The system
consists of two well-designed Convolutional Neural Networks (CNNs), one for
region proposals of traffic signs and one for classification of each region. In
the proposal CNN, a Fully Convolutional Network (FCN) with a dual multi-scale
architecture is proposed to achieve scale invariant detection. In training the
proposal network, a modified "Online Hard Example Mining" (OHEM) scheme is
adopted to suppress false positives. The classification network fuses
multi-scale features as representation and adopts an "Inception" module for
efficiency. We evaluate the proposed TSR system and its components with
extensive experiments. Our method obtains $99.88\%$ precision and $96.61\%$
recall on the Swedish Traffic Signs Dataset (STSD), higher than
state-of-the-art methods. Besides, our system is faster and more lightweight
than state-of-the-art deep learning networks for traffic sign recognition.
